bluuebunny/crossref_metadata_2025 huggingface dataset:
Change in Convection Mixing Properties with Salinity and Temperature: CO2 Storage Application
Authors: Jiang Lanlan , Wang Sijia , Liu Donglei , Zhang Weixin , Lu Guohuan , Liu Yu , Zhao Jiafei
Published: September 2020

In this study, we visualised CO2-brine, density-driven convection in a Hele-Shaw cell. Several experiments were conducted to analyse the effects of the salinity and temperature. The salinity and temperature of fluids were selected according to the storage site. By using charge coupled device (CCD) technology, convection finger formation and development were obtained through direct imaging and processing. The process can be divided into three stages: diffusion-dominated, convection-dominated and shutdown stages. Fingers were formed along the boundary at the onset time, reflecting the startup of convection mixing. Fingers formed, moved and aggregated with adjacent fingers during the convection-dominated stage. The relative migration of brine-saturated CO2 and brine enhanced the mass transfer. The effects of salinity and temperature on finger formation, number, and migration were analysed. Increasing the salinity accelerated finger formation but suppressed finger movement, and the onset time was inversely related to the salinity. However, the effect of temperature on convection is complex. The dissolved CO2 mass was investigated by calculating the CO2 mass fraction in brine during convection mixing. The results show that convection mixing greatly enhanced mass transfer. The study has implications for predicting the CO2 dissolution trapping time and accumulation for the geological storage of CO2.
Copernicia Prunifera Leaf Fiber: A Promising New Reinforcement for Epoxy Composites
Authors: Junio Raí , Nascimento Lucio , Neuba Lucas , Souza Andressa , Moura João , Garcia Filho Fábio , Monteiro Sergio
Published: September 2020

A basic characterization of novel epoxy matrix composites incorporated with up to 40 vol% of processed leaf fibers from the Copernicia prunifera palm tree, known as carnauba fibers, was performed. The tensile properties for the composite reinforced with 40 vol% of carnauba fibers showed an increase (40%) in the tensile strength and (69%) for the elastic modulus. All composites presented superior elongation values in comparison to neat epoxy. Izod impact tests complemented by fibers/matrix interfacial strength evaluation by pullout test and Fourier transformed infrared (FTIR) analysis revealed for the first time a significant reinforcement effect (> 9 times) caused by the carnauba fiber to polymer matrix. Additional thermogravimetric analysis (TG/DTG) showed the onset of thermal degradation for the composites (326 ~ 306 °C), which represents a better thermal stability than the plain carnauba fiber (267 °C) but slightly lower than that of the neat epoxy (342 °C). Differential scanning calorimetry (DSC) disclosed an endothermic peak at 63 °C for the neat epoxy associated with the glass transition temperature (Tg). DSC endothermic peaks for the composites, between 73 to 103 °C, and for the plain carnauba fibers, 107 °C, are attributed to moisture release. Dynamic mechanical analysis confirms Tg of 64 °C for the neat epoxy and slightly higher composite values (82–84 °C) due to the carnauba fiber interference with the epoxy macromolecular chain mobility. Both by its higher impact resistance and thermal behavior, the novel carnauba fibers epoxy composites might be considered a viable substitute for commonly used glass fiber composites.
The Effects of Varying Heat Treatments on Lipid Composition during Pelagic Fishmeal Production
Authors: Hilmarsdottir Gudrun Svana , Ogmundarson Ólafur , Arason Sigurjón , Gudjónsdóttir María
Published: September 2020

The study aimed to provide insight into the lipid quality of pelagic fishmeal and fish oil processing of mackerel and herring cut-offs, and the effect of temperature changes in the cooker (85–95 °C) during production. Samples were collected after each processing step at a traditional processing line where water and lipid content, free fatty acids (FFA), phospholipids (PL) and fatty acid composition (FAC) were measured. Results showed that the standard procedures at 90 °C included ineffective draining and concentration steps. Moreover, the solid streams entering the driers variated in chemical composition, suggesting that processing each stream separately could be beneficial for maintaining the lipid quality. The cooking temperature affected the lipid removal from the fishmeal processing, where lowering the temperature to 85 °C resulted in a lower lipid content of the final fishmeal, along with lower FFA and PL values. Hence, the fishmeal and fish oil factories could save energy by lowering the cooking temperature, as well as obtaining more stable and higher value products. Further recommendations include more focus on the initial steps for a better homogenization and breakdown of the raw material, as well as investigation of different drying techniques applied on each processing stream entering the drying steps.
CO2 Utilization via Integration of an Industrial Post-Combustion Capture Process with a Urea Plant: Process Modelling and Sensitivity Analysis
Authors: Shirmohammadi Reza , Aslani Alireza , Ghasempour Roghayeh , Romeo Luis M.
Published: September 2020

Carbon capture and utilization (CCU) may offer a response to climate change mitigation from major industrial emitters. CCU can turn waste CO2 emissions into valuable products such as chemicals and fuels. Consequently, attention has been paid to petrochemical industries as one of the best options for CCU. The largest industrial CO2 removal monoethanol amine-based plant in Iran has been simulated with the aid of a chemical process simulator, i.e., Aspen HYSYS® v.10. The thermodynamic properties are calculated with the acid gas property package models, which are available in Aspen HYSYS®. The results of simulation are validated by the actual data provided by Kermanshah Petrochemical Industries Co. Results show that there is a good agreement between simulated results and real performance of the plant under different operational conditions. The main parameters such as capture efficiency in percent, the heat consumption in MJ/kg CO2 removed, and the working capacity of the plant are calculated as a function of inlet pressure and temperature of absorber column. The best case occurred at the approximate temperature of 40 to 42 °C and atmospheric pressure with CO2 removal of 80.8 to 81.2%; working capacity of 0.232 to 0.233; and heat consumption of 4.78 MJ/kg CO2.
Effects of Processing Parameters for Vacuum-Bagging-Only Method on Shape Conformation of Laminated Composites
Authors: Mujahid Yasir , Sallih Nabihah , Mustapha Mazli , Abdullah Mohamad Zaki , Mustapha Faizal
Published: September 2020

Complex composite structures manufactured using a low-pressure vacuum bag-only (VBO) method are more susceptible to defects than flat laminates because of the presence of complex compaction conditions at corners. This study investigates the contribution of multivariate processing parameters such as bagging techniques, curing profiles, and laminate structures on laminates’ shape conformation. Nine sets of laminates were produced with a concave corner and another nine sets with a convex corner, both with a 45° inclined structure. Three-way analysis of variance (ANOVA) was performed to quantify thickness variation and spring effect of laminated composites. The analysis for concave and convex corners showed that the bagging techniques is the main factor in controlling the laminate thickness for complex shape applications. The modified (single) vacuum-bag-only (MSVB) technique appeared to be superior when compared to other bagging techniques, exhibiting the least coefficients of variation of 0.015 and 0.016 in composites with concave and convex corners, respectively. Curing profiles and their interaction with bagging techniques showed no statistical significance in the contribution toward laminate thickness variation. The spring effect of laminated composites was investigated by calculating the coefficient of determination (R2) relative to that of the mold. The specimens exhibited a good agreement with R2 values ranging from 0.9824 to 0.9946, with no major data offset. This study provides guidelines to reduce thickness variations and spring effect in laminated composites with complex shapes by the optimum selection of processing parameters for prepreg processing.
Effect of Regenerated Cellulose Fibers Derived from Black Oat on Functional Properties of PVA-Based Biocomposite Film
Authors: Shahi Naresh , Joshi Gautam , Min Byungjin
Published: September 2020

In this study, agricultural residue from black oat, a cover crop usually grown to improve soil nutrients between the periods of regular crop production, was used as a source of cellulose fibers. Concentrations of 1, 3, and 5 wt. % of regenerated cellulose (RC) fibers blended in poly(vinyl alcohol) (PVA) solution were used to prepare the reinforced composite films (CFs) by the solvent cast method. Compared to neat PVA film (control), the effects of RC addition on functional properties of CFs, such as water absorption, transparency, thermal stability, and mechanical property were investigated. All CFs with different RC concentrations exhibited improved mechanical property and thermal stability while the swelling property was decreased, and no significant changes were observed in the film transparency as compared with the control film. Among the CFs, films with 3% RC significantly decreased water vapor transmission rate, swelling, and soluble fraction (p < 0.05). In addition, Young’s modulus and tensile strength were increased by 40 MPa and 3 MPa, respectively, while elongation at break was decreased by 4%, compared to the control film. The results indicate that RC from black oat might be feasible as potential bio fillers to improve film properties in a bio-based composite matrix.
Serum Glycoproteomic Alterations in Patients with Diabetic Retinopathy
Authors: Sharma Ashok , Cox James , Glass Joshua , Lee Tae Jin , Kodeboyina Sai Karthik , Zhi Wenbo , Ulrich Lane , Lukowski Zachary , Sharma Shruti
Published: September 2020

The precise molecular mechanisms of diabetic retinopathy (DR) pathogenesis are unclear, and treatment options are limited. There is an urgent need to discover and develop novel therapeutic targets for the treatment of this disease. Glycosylation is a post-translational modification that plays a critical role in determining protein structure, function, and stability. Recent studies have found that serum glycoproteomic changes are associated with the presence or progression of several inflammatory diseases. However, very little is known about the glycoproteomic changes associated with DR. In this study, glycoproteomic profiling of the serum of diabetic patients with and without DR was performed. A total of 15 glycopeptides from 11 glycoproteins were found to be significantly altered (5 upregulated and 10 downregulated) within the serum glycoproteome of DR patients. These glycoproteins are known to be involved in the maintenance of the extracellular matrix and complement system through peptidolytic activity or regulation.
High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation
Authors: Hernández-Cabronero Miguel , Portell Jordi , Blanes Ian , Serra-Sagristà Joan
Published: September 2020

The capacity of the downlink channel is a major bottleneck for applications based on remote sensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amount of HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraints of spaceborne devices impose limitations on the complexity of practical compression algorithms. To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in this study. This work aims at finding the most advantageous compression–complexity trade-off within the state of the art in HSI compression. To do so, a novel comparison of the most competitive spectral decorrelation approaches combined with the best performing low-complexity compressors of the state is presented. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming only a limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields the best trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster and its compressed data rates are on average within 16% of the CCSDS standard. In scenarios where energy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of all evaluated methods.
Breeding Colony Dynamics of Southern Elephant Seals at Patelnia Point, King George Island, Antarctica
Authors: Fudala Katarzyna , Bialik Robert Józef
Published: September 2020

During the 2019 breeding season (October-December), a battery-powered DIJ Inspire 2 drone was used to investigate a breeding southern elephant seal colony located at Patelnia Point (ASPA 128, King George Island, South Shetland Islands, maritime Antarctica). Twelve unmanned aerial vehicle (UAV) missions conducted 50–70 m above ground level (AGL) were completed to monitor the breeding ground with a maximum of 0.348 km2. The missions were planned in Pix4D Capture software. A drone, with the support of ground cameras and observations, was used to derive population counts, map harems, and track the phenology of the southern elephant seals. Based on data obtained from the UAV missions, orthophotomaps were created in PIX4D Mapper and then analyzed in QGIS. Calculated values of body size parameters such as body length and orthogonal body surface area were used to determine the age and sex of individuals. Analysis of the ranges of the harems on particular days, supported by an analysis of land conditions that generate physical barriers to the movement of animals, allowed zones in which the transformations of groups of harems took place to be determined. The hypothesized hermeticity of the designated zones was supported by statistical tests. The use of drones allows for comprehensive population analyses of the breeding colonies of elephant seals such as censuses of pups and adult individuals, determination of the sex ratio, and spatial analysis of the distribution of breeding formations. In addition, it allows for a more accurate result than ground counting.
Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower
Authors: Pique Gaétan , Fieuzal Rémy , Debaeke Philippe , Al Bitar Ahmad , Tallec Tiphaine , Ceschia Eric
Published: September 2020

The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO2, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO2 fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO2 flux (root mean square error (RMSE) = 0.97 gC·m−2·d−1 and determination coefficient (R2) = 0.83) and water flux (RMSE = 0.68 mm·d−1 and R2 = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m−2, respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets.
Shear-Wave Tomography Using Ocean Ambient Noise with Interference
Authors: Wu Guoli , Dong Hefeng , Ke Ganpan , Song Junqiang
Published: September 2020

Ambient noise carries abundant subsurface structure information and attracts ever-increasing attention in the past decades. However, there are lots of interference factors in the ambient noise in the real world, making the noise difficult to be utilized in seismic interferometry. The paper performs shear-wave tomography on a very short recording of ocean ambient noise with interference. An adapted eigenvalue-based filter is adopted as a pre-processing method to deal with the strong, directional interference problem. Beamforming and the noise crosscorrelation analyses show that the filter works well on the noise recorded by the array. Directional energy is significantly suppressed and the background diffuse component of the noise is relatively enhanced. The shear-wave tomography shows a 4-layer subsurface structure of the area covered by the array, with relatively homogeneous distribution of the shear-wave velocity values in the top three layers and a complicated structure in the bottom layer. Moreover, 3 high-velocity zones can be recognized in the bottom layer. The result is compared with several other tomography results using different methods and data. It demonstrates that, although the ambient noise used in this paper is very short and severely contaminated, a reasonable tomography result can be obtained by applying the adapted eigenvalue-based filter. Since it is the first application of the adapted eigenvalue-based filter in seismic tomography using ambient noise, the paper proves the effectiveness of this technique and shows the potential of the technique in ambient noise processing and passive seismic interferometry.
Understanding Ancient Landscapes in the Venetian Plain through an Integrated Geoarchaeological and Geophysical Approach
Authors: Vacilotto Alice , Deiana Rita , Mozzi Paolo
Published: September 2020

This paper reports the results of the multidisciplinary study carried out in the SE area of Ceggia, in the eastern part of the Venetian Plain. The area has been characterized, since ancient times, by numerous morphological transformation, due to the presence of lagoon and marshes, and interested by repeated reclamation. Aerial and satellite images have identified many natural and anthropogenic traces. From a geophysical point of view, electrical resistivity tomography (ERT) combined with frequency-domain electromagnetic measurements (FDEM) can help to discriminate the spatial distribution of different buried structures in conductive systems. The electrical conductivity is, in fact, directly related to the soil moisture content. The multidisciplinary approach adopted in this context, with the results obtained thanks to the contribution of aerial and satellite images, historical cartography, archaeological survey, geophysical measurements, geomorphological characterization, and 14C dating, allow us to suggest a possible interpretation of the different traces highlighted in the studied area. This approach suggests a potentially useful and replicable methodology to study similar evidence, such as along the North Adriatic coast and in broad sectors of the Po Valley. The key issue, in this kind of system, lies, in fact, in the possibility to date and compare traces visible on the surface by remote sensing, establishing their interest from an archaeological and geomorphological point of view using an integration of field measurements. At the end of this research, the classification of the different anomalies found in this hydraulic variable context, thanks to the multidisciplinary approach here adopted, suggest new hypotheses for reading the complex history of this understudied area.
Trends in Long-Term Drought Changes in the Mekong River Delta of Vietnam
Authors: Phan Vu Hien , Dinh Vi Tung , Su Zhongbo
Published: September 2020

In recent years, short droughts in the dry season have occurred more frequently and caused serious damages to agriculture and human living in the Mekong River Delta of Vietnam (MRD). The paper attempts to quantify the trends of drought changes in the dry seasons from 2001 to 2015 in the region, using daily MODIS MOD09GQ and MOD11A1 data products. Here, we exploit the Temperature Vegetation Dryness Index (TVDI) to assess levels of droughts. For each image-acquisition time, the TVDI image is computed, based on the Normalized Difference Vegetation Index (NDVI), derived from red and near infrared reflectance data, and the Land Surface Temperature (LST), derived from thermal infrared data. Subsequently, a spatiotemporal pattern of drought changes is estimated, based on mean TVDI values of the dry seasons during the observed period, by a linear regression. As a result, the state of drought in the dry seasons in the MRD has mostly been at light and moderate levels, occupying approximately 62% and 34% of the total area. Several sub-areas in the center have an increased trend of drought change, occupying approximately 12.5% of the total area, because impervious surface areas increase, e.g., the obvious land use change, from forest land and land for cultivation for perennial trees being strongly converted to built-up land for residence and public transportation. Meanwhile, several sub-areas in the coastal regions have a negative trend of drought change because water and absorbent surface areas increase, e.g., most of land for cultivation for perennial trees has been converted to aquaculture land. These cases usually occur in and surrounding forest and wet land, also occupying approximately 12.5% of the total area.
Imaging of the Upper Mantle Beneath Southeast Asia: Constrained by Teleseismic P-Wave Tomography
Authors: Shi Huiyan , Li Tonglin , Zhang Rongzhe , Zhang Gongcheng , Yang Hetian
Published: September 2020

It is of great significance to construct a three-dimensional underground velocity model for the study of geodynamics and tectonic evolution. Southeast Asia has attracted much attention due to its complex structural features. In this paper, we collected relative travel time residuals data for 394 stations distributed in Southeast Asia from 2006 to 2019, and 14,011 seismic events were obtained. Then, teleseismic tomography was applied by using relative travel time residuals data to invert the velocity where the fast marching method (FMM) and subspace method were used for every iteration. A novel 3D P-wave velocity model beneath Southeast Asia down to 720 km was obtained using this approach. The tomographic results suggest that the southeastern Tibetan Plateau, the Philippines, Sumatra, and Java, and the deep part of Borneo exhibit high velocity anomalies, while low velocity anomalies were found in the deep part of the South China Sea (SCS) basin and in the shallow part of Borneo and areas near the subduction zone. High velocity anomalies can be correlated to subduction plates and stable land masses, while low velocity anomalies can be correlated to island arcs and upwelling of mantle material caused by subduction plates. We found a southward subducting high velocity body in the Nansha Trough, which was presumed to be a remnant of the subduction of the Dangerous Grounds into Borneo. It is further inferred that the Nansha Trough and the Dangerous Grounds belong to the same tectonic unit. According to the tomographic images, a high velocity body is located in the deep underground of Indochina–Natuna Island–Borneo–Palawan, depth range from 240 km to 660 km. The location of the high velocity body is consistent with the distribution range of the ophiolite belt, so we speculate that the high velocity body is the remnant of thee Proto-South China Sea (PSCS) and Paleo-Tethys. This paper conjectures that the PSCS was the southern branch of Paleo-Tethys and the gateway between Paleo-Tethys and the Paleo-Pacific Ocean. Due to the squeeze of the Australian plate, PSCS closed from west to east in a scissor style, and was eventually extinct under Borneo.
Shape Adaptive Neighborhood Information-Based Semi-Supervised Learning for Hyperspectral Image Classification
Authors: Hu Yina , An Ru , Wang Benlin , Xing Fei , Ju Feng
Published: September 2020

Hyperspectral image (HSI) classification is an important research topic in detailed analysis of the Earth’s surface. However, the performance of the classification is often hampered by the high-dimensionality features and limited training samples of the HSIs which has fostered research about semi-supervised learning (SSL). In this paper, we propose a shape adaptive neighborhood information (SANI) based SSL (SANI-SSL) method that takes full advantage of the adaptive spatial information to select valuable unlabeled samples in order to improve the classification ability. The improvement of the classification mainly relies on two aspects: (1) the improvement of the feature discriminability, which is accomplished by exploiting spectral-spatial information, and (2) the improvement of the training samples’ representativeness which is accomplished by exploiting the SANI for both labeled and unlabeled samples. First, the SANI of labeled samples is extracted, and the breaking ties (BT) method is used in order to select valuable unlabeled samples from the labeled samples’ neighborhood. Second, the SANI of unlabeled samples are also used to find more valuable samples, with the classifier combination method being used as a strategy to ensure confidence and the adaptive interval method used as a strategy to ensure informativeness. The experimental comparison results tested on three benchmark HSI datasets have demonstrated the significantly superior performance of our proposed method.
Calibration and Validation of Antenna and Brightness Temperatures from Metop-C Advanced Microwave Sounding Unit-A (AMSU-A)
Authors: Yan Banghua , Chen Junye , Zou Cheng-Zhi , Ahmad Khalil , Qian Haifeng , Garrett Kevin , Zhu Tong , Han Dejiang , Green Joseph
Published: September 2020

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme (MetOp)-C satellite. The calibration comprises the selection of optimal space view positions for the instrument and the determination of coefficients in calibration equations from the Raw Data Record (RDR) to TDR and SDR. The validation covers the analyses of the instrument noise equivalent differential temperature (NEDT) performance and the TDR and SDR data quality from the launch until 15 November 2019. In particular, the Metop-C data quality is assessed by comparing to radiative transfer model simulations and observations from Metop-A/B AMSU-A, respectively. The results demonstrate that the on-orbit instrument NEDTs have been stable since launch and continue to meet the specifications at most channels except for channel 3, whose NEDT exceeds the specification after April 2019. The quality of the Metop-C AMSU-A data for all channels except channel 3 have been reliable since launch. The quality at channel 3 is degraded due to the noise exceeding the specification. Compared to its TDR data, the Metop-C AMSU-A SDR data exhibit a reduced and more symmetric scan angle-dependent bias against radiative transfer model simulations, demonstrating the great performance of the TDR to SDR conversion coefficients. Additionally, the Metop-C AMSU-A data quality agrees well with Metop-A/B AMSU-A data, with an averaged difference in the order of 0.3 K, which is confirmed based on Simultaneous Nadir Overpass (SNO) inter-sensor comparisons between Metop-A/B/C AMSU-A instruments via either NOAA-18 or NOAA-19 AMSU-A as a transfer.
Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework
Authors: Oh Sungchan , Chang Anjin , Ashapure Akash , Jung Jinha , Dube Nothabo , Maeda Murilo , Gonzalez Daniel , Landivar Juan
Published: September 2020

Assessing plant population of cotton is important to make replanting decisions in low plant density areas, prone to yielding penalties. Since the measurement of plant population in the field is labor intensive and subject to error, in this study, a new approach of image-based plant counting is proposed, using unmanned aircraft systems (UAS; DJI Mavic 2 Pro, Shenzhen, China) data. The previously developed image-based techniques required a priori information of geometry or statistical characteristics of plant canopy features, while also limiting the versatility of the methods in variable field conditions. In this regard, a deep learning-based plant counting algorithm was proposed to reduce the number of input variables, and to remove requirements for acquiring geometric or statistical information. The object detection model named You Only Look Once version 3 (YOLOv3) and photogrammetry were utilized to separate, locate, and count cotton plants in the seedling stage. The proposed algorithm was tested with four different UAS datasets, containing variability in plant size, overall illumination, and background brightness. Root mean square error (RMSE) and R2 values of the optimal plant count results ranged from 0.50 to 0.60 plants per linear meter of row (number of plants within 1 m distance along the planting row direction) and 0.96 to 0.97, respectively. The object detection algorithm, trained with variable plant size, ground wetness, and lighting conditions generally resulted in a lower detection error, unless an observable difference of developmental stages of cotton existed. The proposed plant counting algorithm performed well with 0–14 plants per linear meter of row, when cotton plants are generally separable in the seedling stage. This study is expected to provide an automated methodology for in situ evaluation of plant emergence using UAS data.
Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model
Authors: Lin Yeneng , Xu Dongyun , Wang Nan , Shi Zhou , Chen Qiuxiao
Published: September 2020

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.
Atmospheric Temperature and CO2: Hen-or-Egg Causality?
Authors: Koutsoyiannis Demetris , Kundzewicz Zbigniew
Published: September 2020

It is common knowledge that increasing CO2 concentration plays a major role in enhancement of the greenhouse effect and contributes to global warming. The purpose of this study is to complement the conventional and established theory that increased CO2 concentration due to human emissions causes an increase of temperature, by considering the reverse causality. Since increased temperature causes an increase in CO2 concentration, the relationship of atmospheric CO2 and temperature may qualify as belonging to the category of “hen-or-egg” problems, where it is not always clear which of two interrelated events is the cause and which the effect. We examine the relationship of global temperature and atmospheric carbon dioxide concentration at the monthly time step, covering the time interval 1980–2019, in which reliable instrumental measurements are available. While both causality directions exist, the results of our study support the hypothesis that the dominant direction is T → CO2. Changes in CO2 follow changes in T by about six months on a monthly scale, or about one year on an annual scale. We attempt to interpret this mechanism by involving biochemical reactions, as at higher temperatures soil respiration, and hence CO2 emission, are increasing.
Smallholder Farmers’ Perspectives on Advisory Extension Services: A Case Study of the Gamo Communities of Southern Ethiopia
Authors: Dowsing Miki , Cardey Sarah
Published: September 2020

This is a case study-based research project investigating the status of Advisory Extension Services in southern Ethiopia. The goal was to determine whether available service provisions meet the requirements of smallholder farmers and enabled them to improve their farming practices and livelihoods. A combination of an exploratory inductive approach and mixed methods was used (e.g., questionnaire survey, focus group discussions, key informant interviews). Participants included members of farming households, and agents, experts, and providers working in the agricultural rural sector. The key findings suggested that limited access to resources and unpredictable environmental conditions were stifling smallholder farmer innovation and livelihoods. Service provisions should be better tailored to local conditions, provide greater resource access, and work more closely with farmers. The development and implementation of service provision should involve a wide range of institutions and farmers throughout the process. Local community- and farmer-based organisations are especially important, and can work alongside innovative and talented farmers to enable more effective dissemination of information. Agricultural rural development and service provision should focus greater attention on the views and perspectives of farmers from a range of areas with differing socio-demographic and agro-ecological characteristics for comparative analysis.
The Specificity of Motor Learning Tasks Determines the Kind of Skating Skill Development in Older School-Age Children
Authors: Novak Dominik , Tomasek Adam , Lipinska Patrycja , Stastny Petr
Published: September 2020

The specificity of motor learning tasks for skating development in older school-age children has not been sufficiently explored. The main objective was to compare the effects of training programs using change-of-direction (COD) speed exercises and partial skating task (SeqT) training on speed and agility performance in U12 ice hockey players. Thirteen young ice hockey males (13 ± 0.35 years, 41.92 ± 9.76 kg, 152.23 ± 9.41 cm) underwent three straight speed (4 and 30 m with and without a puck) and agility testing sessions before and after six weeks of COD training and then after a six-week intervention involving partial skating task (SeqT) training. The statistics were performed using magnitude-based decision (MBD) analysis to calculate the probability of the performance change achieved by the interventions. The MBD analysis showed that COD training had a large effect (11.7 ± 2.4% time decrease) on skating start improvement (straight sprint 4 m) and a small effect (−2.2 ± 2.4%) on improvement in agility with a puck. Partial skating task (SeqT) training had a large effect (5.4 ± 2.5%) on the improvement of the 30-m sprint with a puck and moderate effect on agility without a puck (1.9 ± 0.9%) and likely improved the 30-m sprint without a puck (2.6 ± 1.3%). COD training on the ice improves short starts and agility with a puck, while partial skating tasks (SeqT) target longer 30-m sprints and agility without a puck. Therefore, both types of training should be applied in accordance with motor learning tasks specific to current training needs.
Flexible Assignment of Loading Bays for Efficient Vehicle Routing in Urban Last Mile Delivery
Authors: Letnik Tomislav , Mencinger Matej , Peruš Iztok
Published: September 2020

Urban freight deliveries are often subject to many access restrictions which creates the need to establish a system of loading bays and to split the last mile delivery into driving and walking parts. A new model based on hard and soft clustering approach is developed to solve the loading bay assignment problem for efficient vehicle routing and walking in last mile delivery. The flexibility of the model is provided by the soft clustering approach based on different membership degrees of customers to loading bays. Especially for instances with large numbers of loading bays, soft clustering seems to give better results, it leads to higher flexibility of city logistics systems, minimal driving distances, and adequately short walking paths, which contribute to the goal of reaching sustainable urban freight deliveries.
An Improved System Dynamics Model to Evaluate Regional Water Scarcity from a Virtual Water Perspective: A Case Study of Henan Province, China
Authors: Wu Zhaodan , Zhang Yi , Hua Yu , Ye Quanliang , Xu Lixiao , Wang Shiqi
Published: September 2020

An accurate and practically useful evaluation of regional water scarcity is a necessary procedure in scarcity monitoring and threat mitigation. From the perspective of virtual water, this study proposed an improved system dynamics model to evaluate regional water scarcity (WS), including a case study of Henan province, China. We enhanced the existing system dynamics model of WS evaluation from a virtual water perspective by (1) defining WS as the ratio of the consumption-based blue water footprint to water availability, in order to compare the water requirements that need to be met to satisfy the local demand of goods and services with water supply; (2) integrating the economic growth, trade, and water use efficiency in the tertiary industry (e.g., accommodation, food and beverage services) into the model, in order to improve the accuracy of WS assessment and help find more specific measures to reduce WS by factor adjustment; (3) distinguishing the product use structure matrix, as well as the sectoral direct water use coefficient, in local regions from that in other domestic regions and foreign countries, and identifying the regional use structure matrices of products from these three kinds of regions, in order to increase the calculating veracity; and (4) displaying performances of the society, economy, and environment in WS reduction, in order to offer a more comprehensive reference for practical policy decisions. The case study results show that Henan has been suffering from, and in the near future could continue to face, water scarcity, with an average of 2.19 and an annual rise of 1.37% during 2008–2030. In the scenario comparison of current development, production structure adjustment, technology upgrade, and trade structure adjustment in supply-side structural reform of Henan from 2019 to 2030, WS could be reduced by updating production structures into less production of agricultural products or other sectors with a high production-based water footprint (with the smallest average WS of 2.02 and the second smallest total population and GDP, i.e., gross domestic production), technology enhancement in water saving, purification and pollution control (with the second smallest average WS of 2.04 and the largest total population, GDP and total available water resources). Furthermore, for the agricultural products or other sectors with high domestic/international virtual water outflow (inflow), if we reduce (increase) their percentage of outflow (inflow) in the industry involved, WS will increase only more slightly than that when we keep the current development trend, with the smallest total population. Potential measures for alleviating WS should be taken comprehensively, with priorities being identified according to the socioeconomic and environmental performance. Our model can be useful for practical policymaking and valuable for relevant research worldwide.
Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)
Authors: Kim Hyunsoo , Kwon Youngwoo , Choi Yeol
Published: September 2020

Providing adequate public rental housing (PRH) of a decent quality at a desirable location is a major challenge in many cities. Often, a prominent opponent of PRH development is its host community, driven by a belief that PRH depreciates nearby property values. While this is a persistent issue in many cities around the world, this study proposed a new approach to assessing the impact of PRH on nearby property value. This study utilized a machine learning technique called long short-term memory (LSTM) to construct a set of housing price prediction models based on 547,740 apartment transaction records from the city of Busan, South Korea. A set of apartment characteristics and proximity measures to PRH were included in the modeling process. Four geographic boundaries were analyzed: The entire region of Busan, all neighborhoods of PRH, the neighborhoods of PRH in the “favorable,” and the “less favorable” local housing market. The study produced accurate and reliable price predictions, which indicated that the proximity to PRH has a meaningful impact on nearby housing prices both at the city and the neighborhood level. The approach taken by the study can facilitate improved decision making for future PRH policies and programs.
Analysis and Prediction Model of Resident Travel Satisfaction
Authors: Xu Zhenzhen , Shao Chunfu , Wang Shengyou , Dong Chunjiao
Published: September 2020

To promote the sustainable development of urban traffic and improve resident travel satisfaction, the significant factors affecting resident travel satisfaction are analyzed in this paper. An evaluation and prediction model for travel satisfaction based on support vector machine (SVM) is constructed. First, a multinomial logit (MNL) model is constructed to reveal the impact of individual attributes, family attributes and safety hazards on resident travel satisfaction and to clarify the significant factors. Then, a travel satisfaction evaluation model based on the SVM is constructed by taking significant factors as independent variables. Finally, travel optimization measures are proposed and the SVM model is used to predict the effect. Futian Street in Futian District of Shenzhen is taken as the object to carry out specific research. The results show that the following factors have a significant effect on resident travel satisfaction: age, job, level of education, number of car, income, residential area and potential safety hazards of people, vehicles, roads, environment, etc. The model fitting accuracy is 87.76%. The implementation of travel optimization measures may increase travel satisfaction rate by 14.07%.
Beyond Sustainability in Food Systems: Perspectives from Agroecology and Social Innovation
Authors: Marchetti Livia , Cattivelli Valentina , Cocozza Claudia , Salbitano Fabio , Marchetti Marco
Published: September 2020

Food security faces many multifaceted challenges, with effects ranging far beyond the sectors of agriculture and food science and involving all the multiscale components of sustainability. This paper puts forward our point of view about more sustainable and responsible approaches to food production research underlying the importance of knowledge and social innovation in agroecological practices. Increased demand for food worldwide and the diversification of food choices would suggest the adoption of highly productive, but low-resilient and unsustainable food production models. However, new perspectives are possible. These include the revitalization and valorization of family-based traditional agriculture and the promotion of diversified farming systems as a social and economic basis to foster social-ecological conversion. Additionally, they encompass the forecasting of the Participatory Guarantee Systems (PGS) and the drafting of a new agenda for food sovereignty. Thanks to a desk analysis, the study describes and discusses these perspectives, their trajectories and action research implications. The results suggest the need to adopt a more inclusive and systemic approach to the described problems, as the solutions require the promotion of responsibility within decision makers, professionals and consumers. This appears essential for reading, analyzing and understanding the complex ecological-functional, social and economic relations that characterize farming systems, as well as mobilizing local communities.
Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM
Authors: Vochozka Marek , Vrbka Jaromir , Suler Petr
Published: September 2020

There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.