bluuebunny/crossref_metadata_2025 huggingface dataset:
RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
Authors: Zhou Xiaohua , Wang Xuezhi , Zhou Yuanchun , Lin Qinghui , Zhao Jianghua , Meng Xianghai
Published: May 2021

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.
Spatio-Temporal Assessment of Global Gridded Evapotranspiration Datasets across Iran
Authors: Moshir Panahi Davood , Sadeghi Tabas Sadegh , Kalantari Zahra , Ferreira Carla Sofia Santos , Zahabiyoun Bagher
Published: May 2021

Estimating evapotranspiration (ET), the main water output flux within basins, is an important step in assessing hydrological changes and water availability. However, direct measurements of ET are challenging, especially for large regions. Global products now provide gridded estimates of ET at different temporal resolution, each with its own method of estimating ET based on various data sources. This study investigates the differences between ERA5, GLEAM, and GLDAS datasets of estimated ET at gridded points across Iran, and their accuracy in comparison with reference ET. The spatial and temporal discrepancies between datasets are identified, as well as their co-variation with forcing variables. The ET reference values used to check the accuracy of the datasets were based on the water balance (ETwb) from Iran’s main basins, and co-variation of estimated errors for each product with forcing drivers of ET. The results indicate that ETERA5 provides higher base average values and lower maximum annual average values than ETGLEAM. Temporal changes at the annual scale are similar for GLEAM, ERA5, and GLDAS datasets, but differences at seasonal and monthly time scales are identified. Some discrepancies are also recorded in ET spatial distribution, but generally, all datasets provide similarities, e.g., for humid regions basins. ETERA5 has a higher correlation with available energy than available water, while ETGLEAM has higher correlation with available water, and ETGLDAS does not correlate with none of these drivers. Based on the comparison of ETERA5 and ETGLEAM with ETwb, both have similar errors in spatial distribution, while ETGLDAS provided over and under estimations in northern and southern basins, respectively, compared to them (ETERA5 and ETGLEAM). All three datasets provide better ET estimates (values closer to ETWB) in hyper-arid and arid regions from central to eastern Iran than in the humid areas. Thus, the GLEAM, ERA5, and GLDAS datasets are more suitable for estimating ET for arid rather than humid basins in Iran.
AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China
Authors: Qi Tianjun , Zhao Yan , Meng Xingmin , Chen Guan , Dijkstra Tom
Published: May 2021

Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.
A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets
Authors: Zhuo Zhenyu , Zhou Yu , Du Lan , Ren Ke , Li Yi
Published: May 2021

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods.
Comparison of Satellite Driven Surface Energy Balance Models in Estimating Crop Evapotranspiration in Semi-Arid to Arid Inter-Mountain Region
Authors: Acharya Bibek , Sharma Vivek
Published: May 2021

The regional-scale estimation of crop evapotranspiration (ETc) over a heterogeneous surface is an important tool for the decision-makers in managing and allocating water resources. This is especially critical in the arid to semi-arid regions that require supplemental water due to insufficient precipitation, soil moisture, or groundwater. Over the years, various remote sensing-based surface energy balance (SEB) models have been developed to accurately estimate ETc over a regional scale. However, it is important to carry out the SEB model assessment for a particular geographical setting to ensure the suitability of a model. Thus, in this study, four commonly used and contrasting remote sensing models viz. METRIC (mapping evapotranspiration at high resolution with internalized calibration), SEBAL (surface energy balance algorithm for land), S-SEBI (simplified surface energy balance index), and SEBS (surface energy balance system) were compared and used to quantify and map the spatio-temporal variation of ETc in the semi-arid to arid inter-mountain region of Big Horn Basin, Wyoming (Landsat Path/Row: 37/29). Model estimates from 19 cloud-free Landsat 7 and 8 images were compared with the Bowen ratio energy balance system (BREBS) flux stationed in a center pivot irrigated field during 2017 (sugar beet), 2018 (dry bean), and 2019 (barley) growing seasons. The results indicated that all SEB models are effective in capturing the variation of ETc with R2 ranging in between 0.06 to 0.95 and RMSD between 0.07 to 0.15 mm h−1. Pooled data over three vegetative surfaces for three years under irrigated conditions revealed that METRIC (NSE = 0.9) performed better across all land cover types, followed by SEBS (NSE = 0.76), S-SEBI (NSE = 0.73), and SEBAL (NSE = 0.65). In general, all SEB models substantially overestimated ETc and underestimated sensible heat (H) fluxes under dry conditions when only crop residue was available at the surface. A mid-season density plot and absolute difference maps at image scale between the models showed that models involving METRIC, SEBAL, and S-SEBI are close in their estimates of daily crop evapotranspiration (ET24) with pixel-wise RMSD ranged from 0.54 to 0.76 mm d−1 and an average absolute difference across the study area ranged from 0.47 to 0.56 mm d−1. Likewise, all the SEB models underestimated the seasonal ETc, except SEBS.
PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching
Authors: Wu Lang , Zhong Kai , Li Zhongwei , Zhou Ming , Hu Hongbin , Wang Congjun , Shi Yusheng
Published: May 2021

Three-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface descriptor using point-pair transformation feature histograms (PPTFHs) to address these challenges. The generation process of the PPTFH descriptor consists of three steps. First, a simple but efficient strategy is introduced to partition the point-pair sets on the local surface into four subsets. Then, three feature histograms corresponding to each point-pair subset are generated by the point-pair transformation features, which are computed using the proposed Darboux frame. Finally, all the feature histograms of the four subsets are concatenated into a vector to generate the overall PPTFH descriptor. The performance of the PPTFH descriptor is evaluated on several popular benchmark datasets, and the results demonstrate that the PPTFH descriptor achieves superior performance in terms of descriptiveness and robustness compared with state-of-the-art algorithms. The benefits of the PPTFH descriptor for 3D surface matching are demonstrated by the results obtained from five benchmark datasets.
Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network
Authors: Lee Dongwook , Kim Ji-Chul , Kim Mingeuk , Lee Hanmin
Published: May 2021

Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.
Zero-Shot Image Classification Based on a Learnable Deep Metric
Authors: Liu Jingyi , Shi Caijuan , Tu Dongjing , Shi Ze , Liu Yazhi
Published: May 2021

The supervised model based on deep learning has made great achievements in the field of image classification after training with a large number of labeled samples. However, there are many categories without or only with a few labeled training samples in practice, and some categories even have no training samples at all. The proposed zero-shot learning greatly reduces the dependence on labeled training samples for image classification models. Nevertheless, there are limitations in learning the similarity of visual features and semantic features with a predefined fixed metric (e.g., as Euclidean distance), as well as the problem of semantic gap in the mapping process. To address these problems, a new zero-shot image classification method based on an end-to-end learnable deep metric is proposed in this paper. First, the common space embedding is adopted to map the visual features and semantic features into a common space. Second, an end-to-end learnable deep metric, that is, the relation network is utilized to learn the similarity of visual features and semantic features. Finally, the invisible images are classified, according to the similarity score. Extensive experiments are carried out on four datasets and the results indicate the effectiveness of the proposed method.
Automatic Detection and Segmentation for Group-Housed Pigs Based on PigMS R-CNN
Authors: Tu Shuqin , Yuan Weijun , Liang Yun , Wang Fan , Wan Hua
Published: May 2021

Instance segmentation is an accurate and reliable method to segment adhesive pigs’ images, and is critical for providing health and welfare information on individual pigs, such as body condition score, live weight, and activity behaviors in group-housed pig environments. In this paper, a PigMS R-CNN framework based on mask scoring R-CNN (MS R-CNN) is explored to segment adhesive pig areas in group-pig images, to separate the identification and location of group-housed pigs. The PigMS R-CNN consists of three processes. First, a residual network of 101-layers, combined with the feature pyramid network (FPN), is used as a feature extraction network to obtain feature maps for input images. Then, according to these feature maps, the region candidate network generates the regions of interest (RoIs). Finally, for each RoI, we can obtain the location, classification, and segmentation results of detected pigs through the regression and category, and mask three branches from the PigMS R-CNN head network. To avoid target pigs being missed and error detections in overlapping or stuck areas of group-housed pigs, the PigMS R-CNN framework uses soft non-maximum suppression (soft-NMS) by replacing the traditional NMS to conduct post-processing selected operation of pigs. The MS R-CNN framework with traditional NMS obtains results with an F1 of 0.9228. By setting the soft-NMS threshold to 0.7 on PigMS R-CNN, detection of the target pigs achieves an F1 of 0.9374. The work explores a new instance segmentation method for adhesive group-housed pig images, which provides valuable exploration for vision-based, real-time automatic pig monitoring and welfare evaluation.
Liquid Chromatographic Fingerprints for the Characterization of Flavanol-Rich Nutraceuticals Based on 4-Dimethylaminocinnamaldehyde Precolumn Derivatization
Authors: Vidal-Casanella Oscar , Nuñez Oscar , Saurina Javier
Published: May 2021

Flavanols consist of a great family of bioactive molecules displaying a wide range of health-promoting attributes for humans, including antioxidant, antimicrobial or anti-inflammatory effects. As a result, botanical species rich in this type of compound are often used to develop nutraceutical products or dietary supplements with recognized healthy attributes. This paper aims at characterizing nutraceutical products using liquid chromatographic fingerprints related to flavanol composition. Catechins and their oligomers were exploited to characterize and authenticate various commercial products prepared with extracts of red berries and medicinal plants. These compounds resulted in interesting descriptors of some fruits and vegetables, thus providing an additional perspective for the study of nutraceuticals. For such a purpose, a new method based on liquid chromatography with UV/Vis detection (HPLC–UV/Vis) with precolumn derivatization with 4-dimethylaminocinnamaldehyde was developed. Results indicated that the separation of flavanols was very complex due to the degradation of procyanidin derivatives. The resulting data sets were analyzed using chemometric methods such as principal component analysis and partial least square–discriminant analysis. Despite the complexity of chromatographic fingerprints, nutraceutical samples could be discriminated according to their main ingredients. In general, catechin and epicatechin were the most abundant compounds in the different samples, and procyanidin A2 was highly specific to cranberry.
Recent Advances in the Use of Transition-Metal Porphyrin and Phthalocyanine Complexes as Electro-Catalyst Materials on Modified Electrodes for Electroanalytical Sensing Applications
Authors: Cruz-Navarro J. Antonio , Hernández-García Fabiola , Mendoza-Huizar Luis Humberto , Salazar-Pereda Verónica , Cobos-Murcia J. Ángel , Colorado-Peralta Raúl , Álvarez-Romero Giaan Arturo
Published: May 2021

Metalloporphyrins (MP) and metallophtalocyanines (MPc) are innovative materials with catalytic properties that have attracted attention for their application for diverse electrochemical purposes. The presence of metallic centers in their structure offers a redox-active behavior that is being applied in the design of solid electrodes for the quantification of biomolecules, water contaminants, and pharmaceuticals, among others. Herein, we collect the recent information about porphyrin and phthalocyanine complexes as modifiers of electrodes, and the important aspects of the design, characterization, and application of these electrodes.
Developing Hospital Emergency and Disaster Management Index Using TOPSIS Method
Authors: Mojtahedi Mohammad , Sunindijo Riza Yosia , Lestari Fatma , Suparni  , Wijaya Oktomi
Published: May 2021

Indonesia is a country prone to experiencing natural hazards and disasters, which have frequently damaged public infrastructure, including hospitals. The role of hospitals is crucial to alleviate the impact of disasters. However, there is still a lack of study that analyzes the factors that influence the readiness of hospitals in emergency situations. Filling in this gap, the aim of this paper is to analyze and rank hospitals across West Java and Yogyakarta, Indonesia by the resilience of their emergency management approaches. This research seeks to measure hospital resiliency during emergencies and disasters. Results indicate that the emergency and disaster management coordination, response and disaster recovery planning, communication and information management, logistics and evacuation, human resources, finance, patient care and support services, decontamination and security are key attributes for the decision-making matrix. Based on the Hospital Safety Index tool, this research proposes the Hospital Emergency and Disaster Management (HEDM) index by combining the key attributes and sub-attributes using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as a multi-attribute decision-making technique. The paper concludes that the anticipated benefits of analyzing the resilience of hospitals by using HEDM is the identification of the most susceptible hospitals based on their levels of readiness and resiliency in areas which are prone to experiencing disasters. This prioritization is important for resource allocation and budget planning.
What’s the Name of the Game? The Impact of eHealth on Productive Interactions in Chronic Care Management
Authors: Wannheden Carolina , von Thiele Schwarz Ulrica , Östenson Claes-Göran , Pukk Härenstam Karin , Stenfors Terese
Published: May 2021

Chronic care management is dependent on productive interactions between patients and healthcare professionals. Digital health technologies (eHealth) open up new possibilities for improving the quality of care, but there is a limited understanding of what productive interactions entail. This study explores characteristics of productive interactions to support self-care and healthcare in the context of eHealth use in diabetes care. We collected qualitative data based on interviews with nurses and responses to open-ended survey questions from patients, prior to and post using an eHealth service for self-monitoring and digital communication. We found that eHealth’s influence on productive interactions was characterized by unconstrained access, health parameter surveillance, and data-driven feedback, with implications for self-care and healthcare. Our findings indicate that eHealth perforates the boundaries that define interactions under traditional, non-digital care. This was manifested in expressions of uncertainty and in blurred boundaries between self-care and healthcare. We conclude that the attainment of a sustainable eHealth ecosystem will require healthcare to acknowledge eHealth as a disruptive change that may require re-organization to optimally support the productive use of eHealth services for both patients and staff, which includes agreement on new routines, as well as social interaction rules.
Analysis of River Water and Air Pollution—Pljevlja as a “Hot Spot” of Montenegro
Authors: Doderović Miroslav , Burić Dragan , Mijanović Ivan , Premović Marijan
Published: May 2021

The aim of the study was to gather information necessary for the examination of the river Ćehotina water quality as well as the air pollution in the urban area of Pljevlja (far north of Montenegro), from 2011 until 2018. The water quality of the Ćehotina River was observed by the Water Quality Index (WQI) method, based on ten physicochemical and microbiological parameters from five hydrological stations. In order to examine the air quality, we used data on the concentration of the PM10 particles from the station located in the center of Pljevlja. The obtained results of river water quality indicate that the situation was disturbing (bad quality dominates). The results of the air quality analysis indicate that the situation has been alarming and Pljevlja itself as a “hot spot” of Montenegro. Annual, seasonal and daily mean concentrations of PM10 particles were above the prescribed limit values, except during summer. Sources of pollution were mostly known, and in order to protect public health, it is necessary to take appropriate measures as soon as possible, primarily the introduction of modern exhaust gas treatment technology TPP ‘‘Pljevlja’’ and construction of a heating plant that would replace numerous individual (home) fireplaces in Pljevlja.
Coeducation and Citizenship: A Study on Initial Teacher Training in Sexual Equality and Diversity
Authors: Heras-Sevilla Davinia , Ortega-Sánchez Delfín , Rubia-Avi Mariano
Published: May 2021

The present study makes an exhaustive review of the conditions and challenges faced by society to transform the school into a truly inclusive, coeducational, and democratic space. It proposes a theoretical model, of a bottom-up nature, to achieve gender equality in the school environment, giving special importance to teacher training. This study evaluates the training in gender equality and coeducation that students with degrees related to teaching are receiving. An analysis is conducted of the presence of attitudes that support the gender/sex system and the identification of relevant female references in a sample of 452 students in the Degree in Primary Education or the University Master’s Degree in Teacher Training for Compulsory Secondary Education, Upper Secondary Education, Vocational Training, and Language Teaching (MUPES). For the collection of information, an ad hoc questionnaire was used that contemplates formative and cultural aspects, together with the Inventory of Ambivalent Sexism (ASI), the Attitudes of Heterosexuals toward Homosexuals (HATH), and the Women in History (WH) scales. Among the main results, the important lack of training in aspects related to gender equality and coeducation, as well as a general lack of knowledge of historical female references, stands out. It can be concluded that, at present, teacher training is still in the early stages of the proposed model.
The Impact of Employees’ Perceptions of CSR on Career Satisfaction: Evidence from Saudi Arabia
Authors: Al-Ghazali Basheer M. , Sohail M. Sadiq
Published: May 2021

The purpose of this study is to examine the association between employees’ CSR perceptions and their career satisfaction. Moreover, the mediating roles of organizational pride, organizational embeddedness, and psychological capital in the relationship between CSR perceptions and career satisfaction are also examined. Finally, the moderating roles of internalized moral identity and symbolic moral identity in the relationship between CSR perceptions and career satisfaction are investigated. A cross-industry sample of employees from the Kingdom of Saudi Arabia was collected. The results show that CSR perceptions positively affect career satisfaction. Organizational pride, organizational embeddedness, and psychological capital mediate the link between CSR perceptions and career satisfaction. Both dimensions of moral identity (internalized moral identity and symbolic moral identity) positively moderate the effect of CSR perceptions on career satisfaction.
Ecological Compensation Standard of a Water-Receiving Area in an Inter-Basin Water Diversion Based on Ecosystem Service Value and Public Willingness: A Case Study of Beijing
Authors: Peng Zhuoyue , Wu Hao , Ding Maohua , Li Min , Huang Xi , Zheng Rui , Xu Lin
Published: May 2021

Ecological compensation has recently gained significant attention as an economic incentive promoting natural resource management. However, there remain several challenges to its application. A key issue is the lack of a method clearly define the standard of ecological compensation. This study established an accounting methodology for the ecological compensation standard for the water-receiving area in an inter-basin water diversion in China. Beijing, a major water-receiving area of the South-to-North Water Diversion Project, was used as an example of the application of this approach. First, the evaluation index of ecosystem service value of the water-receiving area was selected, then, emergy theory was used to calculate the increment of ecological service value based on the characteristics of each indicator. The ecological service value due to the project was calculated to be 3.898 billion RMB, while the willingness-to-pay by the public was estimated at 915 million RMB. Therefore, the increment of ecological service value was the highest standard of compensation, and the public’s willingness-to-pay was the lower limit of ecological compensation. The final compensation standard can be determined through negotiation between suppliers and beneficiaries under the leadership of relevant governments and water-transfer authorities. Thus, this study aimed to provide a scientific basis for the construction of diversified ecological compensation mechanisms and promote sustainable development of the region.
Strategic Pathways to Scale up Forest and Landscape Restoration: Insights from Nepal’s Tarai
Authors: Bhattarai Sushma , Pant Basant , Laudari Hari Krishna , Rai Rajesh Kumar , Mukul Sharif A.
Published: May 2021

Deforestation and forest degradation mostly caused by human interventions affect the capacity of the forest ecosystem to provide ecosystem services and livelihood benefits. Forest and landscape restoration (FLR) is an emerging concept that focuses on the improvement of the ecosystem as well as the livelihood of the people at the landscape level. Nepal has successfully recovered degraded forest land mainly from the hilly region through forest restoration initiatives, especially community-based forestry. However, the Tarai region is still experiencing deforestation and forest degradation. This study navigated the gaps related to forest restoration in the existing policies and practices and revealed that the persistence of deforestation and forest degradation in Tarai is a result of a complex socioeconomic structure, the limitations of the government in implementing appropriate management modality, unplanned infrastructure, and urban development. We suggest that forest restoration should focus on ecological and social wellbeing pathways at the landscape level to reverse the trend of deforestation and forest degradation in the Tarai regions of Nepal. The study provides critical insight to the policymakers and practitioners of Nepal and other countries (with similar context) who are engaged in forest/ecosystem restoration enterprise.
Sustainable Development Goals Analysis with Ordered Weighted Average Operators
Authors: Ruiz-Morales Betzabe , Espitia-Moreno Irma Cristina , Alfaro-Garcia Victor G. , Leon-Castro Ernesto
Published: May 2021

The present research proposes a new method to analyze the sustainable development goals (SDGs) index using ordered weighted average (OWA) operators. To develop this method, five experts evaluated and designated the relative importance of each of the 17 SDGs defined by the United Nations (UN), and with the use of the OWA and prioritized OWA (POWA) operators, rankings were generated. With the results, it is possible to visualize that the ranking of countries can change depending on the weights related to each SDG because the OWA and POWA operator methods can capture the uncertainty of the phenomenon.
Longitudinal Effects of Dietary Advice on Wheelchair Basketball Athletes: Nutritional and Environmental Aspects
Authors: Toti Elisabetta , Raguzzini Anna , Fedullo Anna Lucia , Cavedon Valentina , Milanese Chiara , Bernardi Marco , Mariani Bianca Maria , Massaro Luca , Mellara Francesco , Sciarra Tommaso , Peluso Ilaria
Published: May 2021

Background: Diet impacts both human and environmental health and must be designed to optimize the training and performance of athletes. The aim of this study was to quantify the effect of dietary advice (DA) on the food intake of wheelchair basketball athletes (WBA) and the environmental impact longitudinally. Methods: DA were provided to WBA of the Italian national team (DAM-T1). Nutritional assessment and evaluation of the environmental impact of diet were performed two months (DAM-T2) and one year later (DAM-T3), when a post-COVID-19 survey was performed also in WBA who did not receive counselling (men: NDAM-T3, women: NDAW-T3). Results: After DA, WBA showed a reduced percentage of energy derived from sugars and fats. Athletes adjusted their protein intake according to the personalized recommendations. The intake of some micronutrients improved after DA and was higher compared to NDAM-T3 and NDAW-T3. Despite the lower impact of diet in NDAW-T3, they presented more nutritional deficiency than men. The mean intake of vegetables was in the range of the EAT-Lancet Commission suggestion only two months after DA. After DA, fiber intake increased and adapted to the tolerated level. Conclusions: WBA improved their diet after DA but did not comply with the EAT-Lancet suggestion for red meat and legumes.
Characterization and Thermal Behavior Study of Biomass from Invasive Acacia mangium Species in Brunei Preceding Thermochemical Conversion
Authors: Ahmed Ashfaq , Bakar Muhammad S. Abu , Razzaq Abdul , Hidayat Syarif , Jamil Farrukh , Amin Muhammad Nadeem , Sukri Rahayu S. , Shah Noor S. , Park Young-Kwon
Published: May 2021

Acacia mangium is a widely grown tree species across the forests in Brunei Darussalam, posing a threat to the existence of some native species in Brunei Darussalam. These species produce large quantities of lignocellulosic biomass from the tree parts comprising the phyllodes, trunk, bark, twigs, pods, and branches. This study examined the thermochemical characteristics and pyrolytic conversion behavior of these tree parts to assess the possibility of valorization to yield bioenergy. Proximate, ultimate, heating value, and Fourier Transform Infrared Spectroscopy (FTIR) analyses were performed to assess the thermochemical characterization, while thermogravimetric analysis was conducted to examine the pyrolytic degradation behavior. Proximate analysis revealed a moisture content, volatile, fixed carbon, and ash contents of 7.88–11.65 wt.%, 69.82–74.85 wt.%, 14.47–18.31 wt.%, and 1.41–2.69 wt.%, respectively. The heating values of the samples were reported in a range of 19.51–21.58 MJ/kg on a dry moisture basis, with a carbon content in the range of 45.50–50.65 wt.%. The FTIR analysis confirmed the heterogeneous nature of the biomass samples with the presence of multiple functional groups. The pyrolytic thermal degradation of the samples occurred in three major stages from the removal of moisture and light extractives, hemicellulose and cellulose decomposition, and lignin decomposition. The bio-oil yield potential from the biomass samples was reported in the range of 40 to 58 wt.%, highlighting the potential of Acacia mangium biomass for the pyrolysis process.
The Relationship between Patents, Technology Transfer and Desorptive Capacity in Korean Universities
Authors: Koo Youngseong , Cho Keuntae
Published: May 2021

This study sought to examine the relationship between patent rights and technology transfer performances of Korean universities and to analyze the moderating effect of the desorptive capacity of industry-academic cooperation foundations. Through this, we study the impact of universities’ patents on both the number of licenses and the license incomes of the universities. We also examine the meaning of the desorptive capacity between them. Regression analysis and structural equation modeling are performed by using the number of patents registered as an independent variable, the number of licenses and license incomes as dependent variables and the desorptive capacity as a moderating variable. The analysis shows that the patent had a positive relationship with both the number of licenses and license incomes. Desorptive capacity had no significant effect between patents and the number of technology transfers. However, it is seen as having a moderating effect between patents and licensing fees from the technology transfers. This paper meaningfully illustrates the desorptive capacity of the industry-academic cooperation foundation from the perspective of outbound open innovation and analyzes the moderating effect of desorptive capacity in relation to patent rights and technology transfer performances.
Longevity of Raw and Lyophilized Crude Urease Extracts
Authors: Javadi Neda , Khodadadi Tirkolaei Hamed , Hamdan Nasser , Kavazanjian Edward
Published: May 2021

The stability (longevity of activity) of three crude urease extracts was evaluated in a laboratory study as part of an effort to reduce the cost of urease for applications that do not require high purity enzyme. A low-cost, stable source of urease will greatly facilitate engineering applications of urease such as biocementation of soil. Inexpensive crude extracts of urease have been shown to be effective at hydrolyzing urea for carbonate precipitation. However, some studies have suggested that the activity of a crude extract may decrease with time, limiting the potential for its mass production for commercial applications. The stability of crude urease extracts shown to be effective for biocementation was studied. The crude extracts were obtained from jack beans via a simple extraction process, stored at room temperature and at 4 ℃, and periodically tested to evaluate their stability. To facilitate storage and transportation of the extracted enzyme, the longevity of the enzyme following freeze drying (lyophilization) to reduce the crude extract to a powder and subsequent re-hydration into an aqueous solution was evaluated. In an attempt to improve the shelf life of the lyophilized extract, dextran and sucrose were added during lyophilization. The stability of purified commercial urease following rehydration was also investigated. Results of the laboratory tests showed that the lyophilized crude extract maintained its activity during storage more effectively than either the crude extract solution or the rehydrated commercial urease. While incorporating 2% dextran (w/v) prior to lyophilization of the crude extract increased the overall enzymatic activity, it did not enhance the stability of the urease during storage.
On 2-Rainbow Domination Number of Generalized Petersen Graphs P(5k,k)
Authors: Erveš Rija , Žerovnik Janez
Published: May 2021

We obtain new results on 2-rainbow domination number of generalized Petersen graphs P(5k,k). In some cases (for some infinite families), exact values are established, and in all other cases lower and upper bounds are given. In particular, it is shown that, for k>3, γr2(P(5k,k))=4k for k≡2,8mod10, γr2(P(5k,k))=4k+1 for k≡5,9mod10, 4k+1≤γr2(P(5k,k))≤4k+2 for k≡1,6,7mod10, and 4k+1≤γr2(P(5k,k))≤4k+3 for k≡0,3,4mod10.
Recent Advances in Mathematical Aspects of Engineering
Authors: Ellahi Rahmat , Sait Sadiq M. , Xu Huijin
Published: May 2021

This special issue took this opportunity to invite researchers to contribute their latest original research findings, review articles, and short communications on advances in the state of the art of mathematical methods, theoretical studies, or experimental studies that extend the bounds of existing methodologies to new contributions addressing current challenges and engineering problems on “Recent Advances in Mathematical Aspects of Engineering” to be published in Symmetry.
A Novel Technique for Determination of Residual Direct-Current Voltage of Liquid Crystal Cells with Vertical and In-Plane Electric Fields
Authors: Mizusaki Msanobu , Ishihara Shoichi
Published: May 2021

Generation of residual direct-current (DC) voltage (VrDC) induces serious image sticking of liquid crystal displays (LCDs). In this study, a novel technique to determine the VrDC of LC cells is proposed. We found that the VrDC could be determined from a current-voltage (I-V) curve obtained by the application of triangular voltage. In the case of a vertically aligned twisted nematic (VTN) mode LC cell, where a vertical electric field is applied, the I-V curve shows maximum and minimum current peaks owing to rotation of an LC director, and the VrDC is able to be determined from an average value of the two peaks. On the other hand, in the case of a fringe field switching (FFS) mode LC cell, where an in-plane (lateral) electric field is applied from comb electrodes, the current peaks derived from the rotation of the LC director do not appear. Therefore, we could not adopt the same way with that of the VTN mode LC cell. However, we found that there were two minimum current peaks derived from minimum capacitances of the FFS mode LC cell, and could determine the VrDC by using these two current peaks. The proposed technique would be useful for the evaluation of the VrDC of the LCDs, where the electric field is applied both vertically and laterally.
On the Relationship between Generalization and Robustness to Adversarial Examples
Authors: Pedraza Anibal , Deniz Oscar , Bueno Gloria
Published: May 2021

One of the most intriguing phenomenons related to deep learning is the so-called adversarial examples. These samples are visually equivalent to normal inputs, undetectable for humans, yet they cause the networks to output wrong results. The phenomenon can be framed as a symmetry/asymmetry problem, whereby inputs to a neural network with a similar/symmetric appearance to regular images, produce an opposite/asymmetric output. Some researchers are focused on developing methods for generating adversarial examples, while others propose defense methods. In parallel, there is a growing interest in characterizing the phenomenon, which is also the focus of this paper. From some well known datasets of common images, like CIFAR-10 and STL-10, a neural network architecture is first trained in a normal regime, where training and validation performances increase, reaching generalization. Additionally, the same architectures and datasets are trained in an overfitting regime, where there is a growing disparity in training and validation performances. The behaviour of these two regimes against adversarial examples is then compared. From the results, we observe greater robustness to adversarial examples in the overfitting regime. We explain this simultaneous loss of generalization and gain in robustness to adversarial examples as another manifestation of the well-known fitting-generalization trade-off.
Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly Detection
Authors: Dogo Eustace M. , Nwulu Nnamdi I. , Twala Bhekisipho , Aigbavboa Clinton
Published: May 2021

Automatic anomaly detection monitoring plays a vital role in water utilities’ distribution systems to reduce the risk posed by unclean water to consumers. One of the major problems with anomaly detection is imbalanced datasets. Dynamic selection techniques combined with ensemble models have proven to be effective for imbalanced datasets classification tasks. In this paper, water quality anomaly detection is formulated as a classification problem in the presences of class imbalance. To tackle this problem, considering the asymmetry dataset distribution between the majority and minority classes, the performance of sixteen previously proposed single and static ensemble classification methods embedded with resampling strategies are first optimised and compared. After that, six dynamic selection techniques, namely, Modified Class Rank (Rank), Local Class Accuracy (LCA), Overall-Local Accuracy (OLA), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U) and Meta-Learning for Dynamic Ensemble Selection (META-DES) in combination with homogeneous and heterogeneous ensemble models and three SMOTE-based resampling algorithms (SMOTE, SMOTE+ENN and SMOTE+Tomek Links), and one missing data method (missForest) are proposed and evaluated. A binary real-world drinking-water quality anomaly detection dataset is utilised to evaluate the models. The experimental results obtained reveal all the models benefitting from the combined optimisation of both the classifiers and resampling methods. Considering the three performance measures (balanced accuracy, F-score and G-mean), the result also shows that the dynamic classifier selection (DCS) techniques, in particular, the missForest+SMOTE+RANK and missForest+SMOTE+OLA models based on homogeneous ensemble-bagging with decision tree as the base classifier, exhibited better performances in terms of balanced accuracy and G-mean, while the Bg+mF+SMENN+LCA model based on homogeneous ensemble-bagging with random forest has a better overall F1-measure in comparison to the other models.
The Properties of Eigenvalues and Eigenfunctions for Nonlocal Sturm–Liouville Problems
Authors: Liu Zhiwen , Qi Jiangang
Published: May 2021

The present paper is concerned with the spectral theory of nonlocal Sturm–Liouville eigenvalue problems on a finite interval. The continuity, differentiability and comparison results of eigenvalues with respect to the nonlocal potentials are studied, and the oscillation properties of eigenfunctions are investigated. The comparison result of eigenvalues and the oscillation properties of eigenfunctions indicate that the spectral properties of nonlocal problems are very different from those of classical Sturm–Liouville problems. Some examples are given to explain this essential difference.
Cytoskeletal Filaments Deep Inside a Neuron Are not Silent: They Regulate the Precise Timing of Nerve Spikes Using a Pair of Vortices
Authors: Singh Pushpendra , Sahoo Pathik , Saxena Komal , Manna Jhimli Sarkar , Ray Kanad , Ghosh Subrata , Bandyopadhyay Anirban
Published: May 2021

Hodgkin and Huxley showed that even if the filaments are dissolved, a neuron’s membrane alone can generate and transmit the nerve spike. Regulating the time gap between spikes is the brain’s cognitive key. However, the time modula-tion mechanism is still a mystery. By inserting a coaxial probe deep inside a neuron, we have re-peatedly shown that the filaments transmit electromagnetic signals ~200 μs before an ionic nerve spike sets in. To understand its origin, here, we mapped the electromagnetic vortex produced by a filamentary bundle deep inside a neuron, regulating the nerve spike’s electrical-ionic vortex. We used monochromatic polarized light to measure the transmitted signals beating from the internal components of a cultured neuron. A nerve spike is a 3D ring of the electric field encompassing the perimeter of a neural branch. Several such vortices flow sequentially to keep precise timing for the brain’s cognition. The filaments hold millisecond order time gaps between membrane spikes with microsecond order signaling of electromagnetic vortices. Dielectric resonance images revealed that ordered filaments inside neural branches instruct the ordered grid-like network of actin–beta-spectrin just below the membrane. That layer builds a pair of electric field vortices, which coherently activates all ion-channels in a circular area of the membrane lipid bilayer when a nerve spike propagates. When biomaterials vibrate resonantly with microwave and radio-wave, simultaneous quantum optics capture ultra-fast events in a non-demolition mode, revealing multiple correlated time-domain operations beyond the Hodgkin–Huxley paradigm. Neuron holograms pave the way to understanding the filamentary circuits of a neural network in addition to membrane circuits.
Selecting a Biomass Pelleting Processing Depot Using a Data Driven Decision-Making Approach
Authors: Tazzit Siham , Ibne Hossain Niamat Ullah , Nur Farjana , Elakramine Fatine , Jaradat Raed , El Amrani Safae
Published: May 2021

Bioenergy is one of the potential solutions to satisfy the extensive demand for energy and reduce fossil fuel dependency. For biomass to be an efficient source of bioenergy, it must be converted to a usable form, one of which is pellets. This study compares three commonly used methods to produce pellets in a biomass depot and presents a framework to select the most effective and economic pelleting processes. The comparison is performed using a data driven decision-making method called the Preference Index Selection Method (PSI). We considered three main pelletization technologies and compared four of their most critical attributes. The three popular biomass pellet processing methods used for this study are the conventional pelleting process (CPP), the high moisture pelleting process (HMPP), and the ammonia fiber expansion (AFEX). These processes were evaluated from both economic and environmental perspectives. We used the state of Mississippi as a testing ground for our analyses. The results obtained through the PSI method were validated with the Grey relational analysis (GRA) method. The results revealed that of the three available pelleting processes, the conventional pelleting process and the high moisture pelleting process were the most economic and environmentally friendly.