bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanical Strength, Thermal Conductivity and Electrical Breakdown of Kenaf Core Fiber/Lignin/Polypropylene Biocomposite
Authors: Ahmad Saffian Harmaen , Talib Mohd Aizam , Lee Seng Hua , Md Tahir Paridah , Lee Ching Hao , Ariffin Hidayah , Asa’ari Ainun Zuriyati Mohamed
Published: August 2020

Mechanical strength, thermal conductivity and electrical breakdown of polypropylene/lignin/kenaf core fiber (PP/L/KCF) composite were studied. PP/L, PP/KCF and PP/L/KCF composites with different fiber and lignin loading was prepared using a compounding process. Pure PP was served as control. The results revealed that tensile and flexural properties of the PP/L/KCF was retained after addition of lignin and kenaf core fibers. Thermal stability of the PP composites improved compared to pure PP polymer. As for thermal conductivity, no significant difference was observed between PP composites and pure PP. However, PP/L/KCF composite has higher thermal diffusivity. All the PP composites produced are good insulating materials that are suitable for building. All PP composites passed withstand voltage test in air and oil state as stipulated in IEC 60641-3 except PP/L in oil state. SEM micrograph showed that better interaction and adhesion between polymer matrix, lignin and kenaf core fibers was observed and reflected on the better tensile strength recorded in PP/L/KCF composite. This study has successfully filled the gap of knowledge on using lignin and kenaf fibers as PP insulator composite materials. Therefore, it can be concluded that PP/Lignin/KCF has high potential as an insulating material.
The Application of Polysaccharides and Their Derivatives in Pigment, Barrier, and Functional Paper Coatings
Authors: Li Qianlong , Wang Shanyong , Jin Xuchen , Huang Caoxing , Xiang Zhouyang
Published: August 2020

As one of the most abundant natural polymers in nature, polysaccharides have the potential to replace petroleum-based polymers that are difficult to degrade in paper coatings. Polysaccharide molecules have a large number of hydroxyl groups that can bind strongly with paper fibers through hydrogen bonds. Chemical modification can also effectively improve the mechanical, barrier, and hydrophobic properties of polysaccharide-based coating layers and thus can further improve the related properties of coated paper. Polysaccharides can also give paper additional functional properties by dispersing and adhering functional fillers, e.g., conductive particles, catalytic particles or antimicrobial chemicals, onto paper surface. Based on these, this paper reviews the application of natural polysaccharides, such as cellulose, hemicellulose, starch, chitosan, and sodium alginate, and their derivatives in paper coatings. This paper analyzes the improvements and influences of chemical structures and properties of polysaccharides on the mechanical, barrier, and hydrophobic properties of coated paper. This paper also summarizes the researches where polysaccharides are used as the adhesives to adhere inorganic or functional fillers onto paper surface to endow paper with great surface properties or special functions such as conductivity, catalytic, antibiotic, and fluorescence.
Qualitative and Quantitative Analysis of Heavy Crude Oil Samples and Their SARA Fractions with 13C Nuclear Magnetic Resonance
Authors: Rakhmatullin Ilfat , Efimov Sergey , Tyurin Vladimir , Gafurov Marat , Al-Muntaser Ameen , Varfolomeev Mikhail , Klochkov Vladimir
Published: August 2020

Nuclear magnetic resonance (NMR) approaches have unique advantages in the analysis of crude oil because they are non-destructive and provide information on chemical functional groups. Nevertheless, the correctness and effectiveness of NMR techniques for determining saturates, aromatics, resins, and asphaltenes (SARA analysis) without oil fractioning are still not clear. In this work we compared the measurements and analysis of high-resolution 13C NMR spectra in B0 ≈ 16.5 T (NMR frequency of 175 MHz) with the results of SARA fractioning for four various heavy oil samples with viscosities ranging from 100 to 50,000 mPa·s. The presence of all major hydrocarbon components both in crude oil and in each of its fractions was established quantitatively using NMR spectroscopy. Contribution of SARA fractions in the aliphatic (10–60 ppm) and aromatic (110–160 ppm) areas of the 13C NMR spectra were identified. Quantitative fractions of aromatic molecules and oil functional groups were determined. Aromaticity factor and the mean length of the hydrocarbon chain were estimated. The obtained results show the feasibility of 13C NMR spectroscopy for the express analysis of oil from physical properties to the composition of functional groups to follow oil treatment processes.
Evaluation of Volatile Compounds during Ageing with Oak Chips and Oak Barrel of Muscat Ottonel Wine
Authors: Călugăr Anamaria , Coldea Teodora Emilia , Pop Carmen Rodica , Pop Tiberia Ioana , Babeș Anca Cristina , Bunea Claudiu Ioan , Manolache Mihail , Gal Emese
Published: August 2020

The aim of this work was to compare the variations of alcohols compounds in white wine Muscat Ottonel variety aged in the presence of untoasted oak chips, toasted oak chips and untoasted barrel, considering three ageing periods—30, 60, and 90 days. The liquid-liquid extraction and gas chromatography coupled to mass spectrometry were used to compare the concentrations of the volatile constituents of Muscat Ottonel wines. A total of 51 volatile compounds were quantified. Alcohols, terpenic and carboxylic acids decreased with ageing time, whereas esters, lactones, and phenolic compounds increased due esterification processes. The chips toast level, method, and duration of ageing, significantly influenced the content of aromatic compounds. Partial least squares regression (PLS-R) clearly discriminated the initial wine and also the wines aged with toasted and untoasted medium. The compounds (alcohols and terpenes) that impart distinctive aroma of Muscat Ottonel were enhanced by untoasted medium. Light toasted oak chips enhanced wood volatile components (acetovanillone and p-vinyl guaiacol). This study provides important scientific results on the ageing of Muscat Ottonel wines with practical economic benefits to winemakers. Alternative less expensive ageing methods and improved control on the wood components extraction process, may contribute to obtaining high-quality wines.
Mining and Indigenous Peoples of the North: Assessment and Development Prospects
Authors: Burtseva Evdokia , Sleptsov Anatolii , Bysyina Anna , Fedorova Alla , Dyachkovskii Gavril
Published: August 2020

The main industry in the Republic of Sakha (Yakutia) is the mining industry, which will continue to expand in the future. Already today there are quite a lot of investment projects for the development of minerals in the Arctic, North-West and South Yakutia, which will be implemented in the territories of indigenous minorities of the North. Indigenous Evens, Evenks, Yukaghirs make up 4.2% of the total population of the republic and are characterized by low genetic diversity, which can lead to negative consequences in relation to their health status when exposed to technogenic pollution. Purpose of the study: assessment of the state of life of indigenous minorities of the North in the Republic of Sakha (Yakutia) under the conditions of a new stage of industrial development of territories of traditional nature management. The planned increasing industrial development of territories of traditional nature management can cause large-scale disturbances of the earth’s surface, depletion of biological resources, environmental pollution, which will ultimately lead to deterioration in the quality of life of the population. In order to take measures to prevent and reduce the negative impacts of industrial development of the territories of residence and traditional activities of indigenous minorities of the North, when implementing new projects, the expert commission recommends concluding a trilateral agreement on cooperation and financing of specific programs between industrial companies, government bodies of the Republic of Sakha (Yakutia) and authorized representatives of indigenous minorities of the North. Research area—the position of indigenous minorities of the North in the conditions of industrial development of the North, Siberia and the Far East. This study looks at the impact of industrial development on the natural environment and the traditional way of life of indigenous population. Compensation for damage to the nomadic tribal communities of reindeer herders has taken place. Only about 250 thousand representatives of 40 indigenous peoples live in these regions, who are included in the official list of indigenous minorities of the North, Siberia and the Far East.
Trampling Intensity and Vegetation Response and Recovery according to Altitude: An Experimental Study from the Himalayan Miyar Valley
Authors: Apollo Michal , Andreychouk Viacheslav
Published: August 2020

The trampling of vegetation caused by recreation and tourism can lead to the loss of vegetation and the degradation of plant communities, which adversely affects natural habitats. This paper investigates the impact of trampling on plant species in the high-mountain environment, where plant resources are limited and any recovery is slow. It is commonly accepted that the sensitivity of the vegetation in mountains increases as altitude increases. Therefore, this study supposed that the same plant species would have different responses to trampling at different altitudes. By using a standardized method of experimental trampling, an empirical study was conducted on eight plant species at two altitudes: 4072 m and 4480 m. Each species was trampled 0–500 times. Response to trampling was assessed by determining plant cover two weeks after trampling and one year after trampling. For most species, the relationship between plant cover after trampling and trampling intensity was very clear (linear). This research found the following: (1) vegetation has extremely high ecological sensitivity to trampling in the examined environment; (2) above 4000 m, an increase in altitude does not increase the sensitivity of vegetation. Vegetation above a certain altitude exhibits similar, very high sensitivity to trampling.
Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery
Authors: König Marcel , Birnbaum Gerit , Oppelt Natascha
Published: August 2020

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.
A Comparison of Low-Cost Sensor Systems in Automatic Cloud-Based Indoor 3D Modeling
Authors: Ingman Matias , Virtanen Juho-Pekka , Vaaja Matti T. , Hyyppä Hannu
Published: August 2020

The automated 3D modeling of indoor spaces is a rapidly advancing field, in which recent developments have made the modeling process more accessible to consumers by lowering the cost of instruments and offering a highly automated service for 3D model creation. We compared the performance of three low-cost sensor systems; one RGB-D camera, one low-end terrestrial laser scanner (TLS), and one panoramic camera, using a cloud-based processing service to automatically create mesh models and point clouds, evaluating the accuracy of the results against a reference point cloud from a higher-end TLS. While adequately accurate results could be obtained with all three sensor systems, the TLS performed the best both in terms of reconstructing the overall room geometry and smaller details, with the panoramic camera clearly trailing the other systems and the RGB-D offering a middle ground in terms of both cost and quality. The results demonstrate the attractiveness of fully automatic cloud-based indoor 3D modeling for low-cost sensor systems, with the latter providing better model accuracy and completeness, and with all systems offering a rapid rate of data acquisition through an easy-to-use interface.
Neural Network Training for the Detection and Classification of Oceanic Mesoscale Eddies
Authors: Santana Oliverio , Hernández-Sosa Daniel , Martz Jeffrey , Smith Ryan
Published: August 2020

Recent advances in deep learning have made it possible to use neural networks for the detection and classification of oceanic mesoscale eddies from satellite altimetry data. Various neural network models have been proposed in recent years to address this challenge, but they have been trained using different types of input data and evaluated using different performance metrics, making a comparison between them impossible. In this article, we examine the most common dataset and metric choices, by analyzing the reasons for the divergences between them and pointing out the most appropriate choice to obtain a fair evaluation in this scenario. Based on this comparative study, we have developed several neural network models to detect and classify oceanic eddies from satellite images, showing that our most advanced models perform better than the models previously proposed in the literature.
Impairing Land Registry: Social, Demographic, and Economic Determinants of Forest Classification Errors
Authors: Adamiak Maciej , Biczkowski Mirosław , Leśniewska-Napierała Katarzyna , Nalej Marta , Napierała Tomasz
Published: August 2020

This paper investigates the social, demographic, and economic factors determining differences between forest identification based on remote sensing techniques and land registry. The Database of Topographic Objects and Sentinel-2 satellite imagery data from 2018 were used to train a forest detection supervised machine learning model. Results aggregated to communes (NUTS-5 units) were compared to data from land registry delivered in Local Data Bank by Statistics Poland. The differences identified between above mentioned sources were defined as errors of land registry. Then, geographically weighted regression was applied to explain spatially varying impact of investigated errors’ determinants: Urbanization processes, civic society development, education, land ownership, and culture and quality of spatial planning. The research area covers the entirety of Poland. It was confirmed that in less developed areas, local development policy stimulating urbanization processes does not respect land use planning principles, including the accuracy of land registry. A high education level of the society leads to protective measures before the further increase of the investigated forest cover’s overestimation of the land registry in substantially urbanized areas. Finally, higher coverage by valid local spatial development plans stimulate protection against forest classification errors in the land registry.
Accuracy Assessment of Digital Elevation Models (DEMs): A Critical Review of Practices of the Past Three Decades
Authors: Mesa-Mingorance José L. , Ariza-López Francisco J.
Published: August 2020

An analysis of almost 200 references has been carried out in order to obtain knowledge about the DEM (Digital Elevation Model) accuracy assessment methods applied in the last three decades. With regard to grid DEMs, 14 aspects related to the accuracy assessment processes have been analysed (DEM data source, data model, reference source for the evaluation, extension of the evaluation, applied models, etc.). In the references analysed, except in rare cases where an accuracy assessment standard has been followed, accuracy criteria and methods are usually established according to the premises established by the authors. Visual analyses and 3D analyses are few in number. The great majority of cases assess accuracy by means of point-type control elements, with the use of linear and surface elements very rare. Most cases still consider the normal model for errors (discrepancies), but analysis based on the data itself is making headway. Sample size and clear criteria for segmentation are still open issues. Almost 21% of cases analyse the accuracy in some derived parameter(s) or output, but no standardization exists for this purpose. Thus, there has been an improvement in accuracy assessment methods, but there are still many aspects that require the attention of researchers and professional associations or standardization bodies such as a common vocabulary, standardized assessment methods, methods for meta-quality assessment, and indices with an applied quality perspective, among others.
Area and Mass Changes of Glaciers in the West Kunlun Mountains Based on the Analysis of Multi-Temporal Remote Sensing Images and DEMs from 1970 to 2018
Authors: Cao Bo , Guan Weijin , Li Kaiji , Wen Zhenling , Han Hui , Pan Baotian
Published: August 2020

Most of the world’s glaciers have retreated significantly against the background of recent climate warming, while reports have indicated that the glaciers in the West Kunlun Mountains (WKL) may be in a relatively stable state, although there are some gaps in previous research. Based on Landsat series data, topographic maps, SRTM and TanDEM-x data, this paper extracts detailed glacial area information and glacial mass balance during different time periods from 1970 to 2018. We found that, the total area of glaciers in the WKL decreased by 8.0 km2 from 1972 to 2018. The area decreased by 12.0 km2 from 1972 to 1991 and increased by 4.7 km2 from 2010 to 2018. Glacier surface elevation change results in the WKL showed that the overall glacier thickness slightly decreased from 1970 to 2016, with an average of 1.9 ± 1.0 m. The glaciers thinned by approximately 2.5 ± 1.0 m from 1970 to 2000, while from 2000 to 2016, the glaciers thickened approximately by 0.6 ± 1.0 m. Overall, the glaciers in the WKL showed very slight retreat. In addition, the mass changes of glaciers were affected by glacial surging.
Determination of Cloud Motion Applying the Lucas-Kanade Method to Sky Cam Imagery
Authors: Mondragón Román , Alonso-Montesinos Joaquín , Riveros-Rosas David , Bonifaz Roberto
Published: August 2020

The atmospheric conditions existing where concentrated solar power plants (CSP) are installed need to be carefully studied. A very important reason for this is because the presence of clouds causes drops in electricity generated from solar energy. Therefore, forecasting the cloud displacement trajectory in real time is one of the functions and tools that CSP operators must develop for plant optimization, and to anticipate drops in solar irradiance. For short forecast of cloud movement (10 min) is enough with describe the cloud advection while for longer forecast (over 15 min), it is necessary to predict both advection and cloud changes. In this paper, we present a model that predict only the cloud advection displacement trajectory for different sky conditions and cloud types at the pixel level, using images obtained from a sky camera, as well as mathematical methods and the Lucas-Kanade method to measure optical flow. In the short term, up to 10 min the future position of the cloud front is predicted with 92% certainty while for 25–30 min, the best predicted precision was 82%.
CNN and LSTM-Based Emotion Charting Using Physiological Signals
Authors: Dar Muhammad Najam , Akram Muhammad Usman , Khawaja Sajid Gul , Pujari Amit N.
Published: August 2020

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.
Improved Antibiotic Detection in Raw Milk Using Machine Learning Tools over the Absorption Spectra of a Problem-Specific Nanobiosensor
Authors: Gutiérrez Pablo , Godoy Sebastián E. , Torres Sergio , Oyarzún Patricio , Sanhueza Ignacio , Díaz-García Victor , Contreras-Trigo Braulio , Coelho Pablo
Published: August 2020

In this article we present the development of a biosensor system that integrates nanotechnology, optomechanics and a spectral detection algorithm for sensitive quantification of antibiotic residues in raw milk of cow. Firstly, nanobiosensors were designed and synthesized by chemically bonding gold nanoparticles (AuNPs) with aptamer bioreceptors highly selective for four widely used antibiotics in the field of veterinary medicine, namely, Kanamycin, Ampicillin, Oxytetracycline and Sulfadimethoxine. When molecules of the antibiotics are present in the milk sample, the interaction with the aptamers induces random AuNP aggregation. This phenomenon modifies the initial absorption spectrum of the milk sample without antibiotics, producing spectral features that indicate both the presence of antibiotics and, to some extent, its concentration. Secondly, we designed and constructed an electro-opto-mechanic device that performs automatic high-resolution spectral data acquisition in a wavelength range of 400 to 800 nm. Thirdly, the acquired spectra were processed by a machine-learning algorithm that is embedded into the acquisition hardware to determine the presence and concentration ranges of the antibiotics. Our approach outperformed state-of-the-art standardized techniques (based on the 520/620 nm ratio) for antibiotic detection, both in speed and in sensitivity.
An Instrumented Assessment of a Rhythmic Finger Task among Children with Motor Coordination Difficulties
Authors: Keinan Artal , Bar-Shalita Tami , Portnoy Sigal
Published: August 2020

Background: Coordination is crucial for motor function, yet objective clinical evaluations are limited. We therefore developed and tested the reliability and validity of a low-cost sensorized evaluation of a rhythmic finger task. Methods: Children with coordination difficulties (n = 24) and typically developing children (n = 24) aged from 5 to 7 years performed the Sensorized Finger Sequencing Test (SFST), a finger sequencing test that records the correct sequence, total time, and the standard deviation (SD) of touch time. Additionally, motor performance tests and parents’ reports were applied in order to test the reliability and validity of the SFST. Results: The study group had significantly greater thumb-finger test scores—total time in the dominant hand (p = 0.035) and the SD of the touch time in both dominant (p = 0.036) and non-dominant (p = 0.032) hands. Motor performance tests were not correlated with the SFST. Test–retest reliability in 10 healthy children was found for the SD of touch time in the dominant hand (r = 0.87, p = 0.003). Conclusions: The SFST was successful in assessing the movement pattern variability reported in children with motor difficulties. This exploratory study indicates that the low-cost SFST could be utilized as an objective measure for the assessment of proprioception components, which currently are overlooked by standardized motor performance assessments.
Non-Invasive PPG-Based System for Continuous Heart Rate Monitoring of Incubated Avian Embryo
Authors: Youssef Ali , Berckmans Daniel , Norton Tomas
Published: August 2020

The chicken embryo is a widely used experimental animal model in many studies, including in the field of developmental biology, of the physiological responses and adaptation to altered environments, and for cancer and neurobiology research. The embryonic heart rate is an important physiological variable used as an index reflecting the embryo’s natural activity and is considered one of the most difficult parameters to measure. An acceptable measurement technique of embryonic heart rate should provide a reliable cardiac signal quality while maintaining adequate gas exchange through the eggshell during the incubation and embryonic developmental period. In this paper, we present a detailed design and methodology for a non-invasive photoplethysmography (PPG)-based prototype (Egg-PPG) for real-time and continuous monitoring of embryonic heart rate during incubation. An automatic embryonic cardiac wave detection algorithm, based on normalised spectral entropy, is described. The developed algorithm successfully estimated the embryonic heart rate with 98.7% accuracy. We believe that the system presented in this paper is a promising solution for non-invasive, real-time monitoring of the embryonic cardiac signal. The proposed system can be used in both experimental studies (e.g., developmental embryology and cardiovascular research) and in industrial incubation applications.
Personalized Office Lighting for Circadian Health and Improved Sleep
Authors: Papatsimpa Charikleia , Linnartz Jean-Paul
Published: August 2020

In modern society, the average person spends more than 90% of their time indoors. However, despite the growing scientific understanding of the impact of light on biological mechanisms, the existing light in the built environment is designed predominantly to meet visual performance requirements only. Lighting can also be exploited as a means to improve occupant health and well-being through the circadian functions that regulate sleep, mood, and alertness. The benefits of well-lit spaces map across other regularly occupied building types, such as residences and schools, as well as patient rooms in healthcare and assisted-living facilities. Presently, Human Centric Lighting is being offered based on generic insights on population average experiences. In this paper, we suggest a personalized bio-adaptive office lighting system, controlled to emit a lighting recipe tailored to the individual employee. We introduce a new mathematical optimization for lighting schedules that align the 24-h circadian cycle. Our algorithm estimates and optimizes parameters in experimentally validated models of the human circadian pacemaker. Moreover, it constrains deviations from the light levels desired and needed to perform daily activities. We further translate these into general principles for circadian lighting. We use experimentally validated models of the human circadian pacemaker to introduce a new algorithm to mathematically optimize lighting schedules to achieve circadian alignment to the 24-h cycle, with constrained deviations from the light levels desired for daily activities. Our suggested optimization algorithm was able to translate our findings into general principles for circadian lighting. In particular, our simulation results reveal: (1) how energy constrains drive the shape of optimal lighting profiles by dimming the light levels in the time window that light is less biologically effective; (2) how inter-individual variations in the characteristic internal duration of the day shift the timing of optimal lighting exposure; (3) how user habits and, in particular, late-evening light exposure result in differentiation in late afternoon office lighting.
Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension
Authors: Lee Jihyun , Woo Jiyoung , Kang Ah Reum , Jeong Young-Seob , Jung Woohyun , Lee Misoon , Kim Sang Hyun
Published: August 2020

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.
Optimization Design and Flexible Detection Method of Wall-Climbing Robot System with Multiple Sensors Integration for Magnetic Particle Testing
Authors: Zhang Xiaojun , Zhang Xuan , Zhang Minglu , Sun Lingyu , Li Manhong
Published: August 2020

Weld detection is vital to the quality of ship construction and navigation safety, and numerous detection robots have been developed and widely applied. Focusing on the current bottleneck of robot safety, efficiency, and intelligent detection, this paper developed a wall-climbing robot that integrates multiple sensors and uses fluorescent magnetic powder for nondestructive testing. We designed a moving mechanism that can safely move on a curved surface and a serial-parallel hybrid flexible detection mechanism that incorporates a force sensor to solve the robot’s safe adsorption and a flexible detection of the curved surface to complete the flaw detection operation. We optimized the system structure and improved the overall performance of the robot by establishing a unified mechanical model for different operating conditions. Based on the collected sensor information, a multi-degree of freedom component collaborative flexible detection method with a standard detecting process was developed to complete efficient, high-quality detection. Results showed that the developed wall-climbing robot can move safely and steadily on the complex facade and can complete the flaw detection of wall welds.
Micro-Crack Mechanism in the Fracture Evolution of Saturated Granite and Enlightenment to the Precursors of Instability
Authors: Dong Longjun , Zhang Yihan , Ma Ju
Published: August 2020

To explore the potential precursors of rock instability, it is necessary to clarify the mechanism of micro-crack from fracturing to failure, which involves the evolution of fracture size, orientation, source model, and their relationships to the loading. The waveforms of acoustic emission (AE) recorded by the sensor network attached rock sample during laboratory tests provide a data basis for solving these problems, since these observations are directly related to the characteristics of the fracturing sources. Firstly, we investigated the source mechanism, looking at the rise angle and the average frequency (RA-AF) trends during five loading stages in a uniaxial compression test. Results show that the proportion of shear events significantly increases when approaching instability. Secondly, we calculated the moment tensor for each event, considering the uncertainties of P-wave polarity, azimuth, and the takeoff angles of the rays. Moment tensor solutions suggest that there are obviously more crack events than shear events in all loading stages. Moment tensor evolutions confirmed that the decreasing of isotropic component and the increment of double-couple can be used as precursors of rock fracturing development. Considering the limitations of these two methods, it is suggested that we should be concerned more about the proportions of individual failure components and their evolutions over time, instead of absolutely classifying the events into a certain source type.
A Virtual Instrument for Road Vehicle Classification Based on Piezoelectric Transducers
Authors: González Bernardino , Jiménez Francisco J. , De Frutos José
Published: August 2020

This paper describes a virtual instrument capable of the automatic and quasi-instantaneous classification of a vehicle according to category when it is driving along the road. The vehicle’s classification is based on accurate measurements of both the vehicle’s speed and its wheelbase. Our research is focused on achieving accurate speed and wheelbase measurements and then determining the category of the vehicle through the developed software. The vehicle categorization is based on the wheelbase measurements and the number of axles and metal masses of the vehicle. The system has a complementary magnetic sensor, which helps in classifying the vehicle when the wheelbase measurement could be representative of different categories, and a camera to confirm the results of the experiment. The proposed measurement system presents a novel method for classifying vehicles according to type using piezoelectric transducers (piezo sensors). In addition, no system references have been found that encompass the functionalities of the presented system based on the information of only two piezoelectric transducers. The system has important advantages over current alternatives (systems based on inductive loops, cameras, fiber optic sensors or lasers), the installation is simple and non-invasive and with a success rate of the classification greater than 90%. The system consists of a signal acquisition point on the pavement, signal conditioning hardware and a data acquisition (DAQ) module, which links the hardware and the virtual instrument developed in LabVIEW®. Finally, the system has been tested on the road with real traffic, and the experimental results are presented and discussed in this paper.
Effects of the Design of Overview Maps on Three-Dimensional Virtual Environment Interfaces
Authors: Chen Chien-Hsiung , Chen Meng-Xi
Published: August 2020

This study examined how users acquire spatial knowledge in an onscreen three-dimensional virtual environment when using overview maps. This experiment adopted a three (the size of overview maps) x two (the transparency of overview maps) between-subjects design. Three levels of the size of overview maps were evaluated, i.e., 1/2, 1/8, and 1/16 screen size. Comparisons between 20% transparent and 80% transparent were made. We asked 108 participants to complete spatial perception tasks and fill out questionnaires regarding their feelings. The results indicate the following: (1) The effects of the transparency of overview maps on users’ spatial perception vary with the size of overview maps. The 80% transparent overview map is significantly more efficient than the 20% transparent overview map in the condition of 1/2 screen size. However, the result is opposite in the condition of 1/8 screen size. (2) Users like the 80% transparent overview map significantly better than the 20% transparent overview map in the condition of 1/2 screen size. (3) Concerning subjective evaluations of satisfaction, preference, and system usability, overview maps in the condition of 1/8 screen size are significantly better than those in the condition of 1/2 screen size.
Sustainable Agriculture Development in the Western Desert of Egypt: A Case Study on Crop Production, Profit, and Uncertainty in the Siwa Region
Authors: Moghazy Noha H. , Kaluarachchi Jagath J.
Published: August 2020

The Egyptian government initiated a development project in 2015 to reclaim 1.5 million acres with the primary goal of increasing agricultural production. Siwa is one of these areas in the Western Desert of Egypt, with 30,000 acres using groundwater from the Nubian Sandstone Aquifer System (NSAS). This study investigates if government goals are achievable in the next 20 years to secure the food and water needs of the Siwa region. Results show that total required crop areas are 7154 and 6629 acres in winter and summer, respectively. These areas are less than 17,010 acres of available area for cultivation (Av). The estimated total water use is 40.6 million cubic meters (MCM), which is less than the 88 MCM that is considered available groundwater in the Nubian Aquifer System (NAS). Due to available capacity in Siwa, an optimization model is used to maximize crop production considering government policies. The Autoregressive Integrated Moving Average (ARIMA) model was applied to predict production costs and sell prices of cultivated crops. Analysis included different scenarios beyond government-recommended approaches to identify ways to further expand agriculture production under sustainable conditions. Results provide valuable insights to the ability to achieve government goals from the project and changes that may be required to enhance production.
Green Technology and Sustainable Development: Assessment and Green Growth Frameworks
Authors: Guo Minjian , Nowakowska-Grunt Joanna , Gorbanyov Vladimir , Egorova Maria
Published: August 2020

The aim of this study is to investigate the characteristics of a sustainable development assessment methodology being designed in the context of green technology. The methodology in question is based on indicators from the Sustainable Development Goals Index (SGDI), specifically in its ecological component. These indicators underlie an Averaging Sustainable Development Index (ASDI) and a Normalized Sustainable Development Index (NSDI). The resultant methodology was applied to 20 countries from the SDGI ranking. According to the research results, the intensive activity of the brown industries in the United Arab Emirates, Kazakhstan, the United States, Korea, and Russia resulted in significant carbon dioxide emissions. Switzerland, Kazakhstan, and Russia had high scores on sustainable management of water and sanitation. Russia was the only developed country to have an ASDI higher than its SDGI and its gap between NSDI and ASDI indexes was not significant, indicating a positive trend in greentech development. The reason why NSDI was increasingly different from SDGI was that countries leading the socio-economic rankings had higher consumption of energy and resources, and a much greater environmental footprint than those countries that consumed less. The originality of this study is that it identifies gaps between NSDI and ASDI values, which indicate that conditions for greentech adoption in most developing countries are unfavorable.
Life-Cycle Break-Even Analysis of Electric Carsharing: A Comparative Study in China
Authors: Jia Jingjing , Ma Shujie , Xue Yixi , Kong Deyang
Published: August 2020

Electric carsharing (ECS) is a potential option to address the problem of unsustainability in the transportation sector. The business-to-consumer model of ECS, which is one of several different electric carsharing models, has gained much popularity in recent years. Generating sufficient revenue to cover costs is a critical factor for ECS companies to maintain healthy development. This study makes an economic analysis, on the basis of life-cycle cost and monetary revenue associated with the operation of ECS, of two Chinese ECS companies: EVCARD and LCCS. Based on data gathered by field investigation, this study aims to determine the break-even moment for each company’s main vehicle models by means of the net present value method. The results show that EVCARD achieved an earlier break-even moment than LCCS. The break-even moment of Chery eQ of EVCARD was the shortest of all the vehicle models, at only 181.3 min. Moreover, a sensitivity analysis was conducted to portray how different cost-related and revenue-related factors influence the break-even moment. Our findings indicate that a wide difference exists in terms of the influence of different factors on the break-even moment. Among these, the manufacturer’s suggested retail price is the most influential variable, followed by the unit rental price. The reaction of the break-even moment to the market price of a charging pile and the non-rental revenue per vehicle—especially the latter—was found to be negligible in the sensitivity analysis.
Investigating Factors that Influence Math Homework Expectancy: A Multilevel Approach
Authors: Xu Jianzhong , Wang Chuang , Du Jianxia
Published: August 2020

The present study investigated several clusters of variables influencing students’ math homework expectancy based on 3018 students in grade 8 in China. Results revealed that math homework expectancy was positively associated with homework quality at both the individual and class level. At the individual level, math homework expectancy was positively associated with prior math knowledge, parent education, homework interest, homework favorability, parent autonomy support, mastery-approach, and homework value. Meanwhile, math homework expectancy was negatively associated with homework time, parent help quantity, and performance-approach. Finally, compared with females, males tended to have higher math homework expectancy.
Higher Education as a Determinant of the Competitiveness and Sustainable Development of an Economy
Authors: Krstić Miloš , Filipe José António , Chavaglia José
Published: August 2020

The aim of this paper is to examine the interdependence between higher education on the one hand and the competitiveness of the economy and sustainable development on the other hand. To examine the mentioned interdependence, we used a sample that includes EU member states and candidate countries. The paper applies correlation and regression analysis of comparative data sections. The research findings indicate a strong correlation between higher education on the one hand and the competitiveness of the economy and sustainable development on the other. The results obtained by research can serve as a “global benchmark” of future public policy in the field of higher education.
The Effect of Chemical Sulfide Oxidation on the Oxygenic Activity of an Alkaliphilic Microalgae Consortium Deployed for Biogas Upgrading
Authors: Ramírez-Rueda Arnold , Velasco Antonio , González-Sánchez Armando
Published: August 2020

The oxygenic photosynthetic activity (OPA) of an alkaliphilic microalgae consortium was evaluated at different concentrations of dissolved sulfide under room temperature and well-defined conditions of irradiance and pH in a tubular closed photobioreactor. The kinetic assays showed that it was optimal at a sulfide concentration of 3.2 mg/L under an external photosynthetically active radiation of 50 and 120 μE/m2 s together with a pH of 8.5 and 9.2. In contrast, the oxygenic photosynthetic activity was insignificant at 15 μE/m2 s with a pH of 7.3, both in the absence and presence of sulfide. Consecutive pulse additions of dissolved sulfide evidenced that the accumulation rate of dissolved oxygen was decreased by the spontaneous chemical oxidation of sulfide with dissolved oxygen in alkaline culture media, mainly at high sulfide levels. At 3.2 mg/L of sulfide, the oxygenic photosynthetic activity was improved by around 60% compared to the treatment without sulfide at external irradiances of 120 μE/m2 s, 30 °C, and pH of 8.5 and 9.2. Additionally, an even higher OPA enhancement (around 85%) was observed in the same previous conditions but using 16 mg/L of sulfide. Thiosulfate was the major end-product of sulfide by oxic chemical reaction, both in biotic and abiotic assays with yields of 0.80 and 0.68, respectively.
A Comprehensive Model of Consumers’ Perceptions, Attitudes and Behavioral Intention toward Organic Tea: Evidence from an Emerging Economy
Authors: Bu Xiangzhi , Nguyen Hoang Viet , Chou Tsung Piao , Chen Chia-Pin
Published: August 2020

This study investigates factors that predict consumers’ perceptions of healthiness of organic tea. Furthermore, this study also examines the relationship between perceived healthiness and purchase intention with the mediating role of consumers’ attitudes toward organic tea. Because China has been the largest producer and the largest market of the global tea market, this study collects sample data from consumers at different tea stores in China. Using structural equation modelling to analyze data, results indicate that product quality, consumer service, in-store experience, store prestige, and store innovativeness are important predictors of consumers’ perceptions of healthiness of organic tea. Furthermore, results show that perceived healthiness has a positive influence on purchase intention toward organic tea. Consumers’ attitudes toward organic tea also has a positive mediating effect into this relationship.
Towards Sustainable Collaborative Logistics Using Specialist Planning Algorithms and a Gain-Sharing Business Model: A UK Case Study
Authors: Vargas Alix , Fuster Carmen , Corne David
Published: August 2020

This paper introduces the FreightShare Lab Platform (FSLP) and its embedded business model, aiming to facilitate and encourage horizontal collaboration in freight logistics. The idea of the FSLP is to create collaborating clusters of freight operators, and corresponding collaborative operational plans, via specialised decision support algorithms and multi-fleet optimisation. Further, a gain-sharing business model embedded within the FSLP algorithms ensures that participants, mainly logistics service providers and freight operators, can retain their own profit margins and fairly share the efficiency gains from collaboration. A case study is presented, centred on a large UK freight operator, to evaluate the key FSLP algorithms in a realistic context. The results evidence the potential for significant financial and environmental benefits for industry and society.