bluuebunny/crossref_metadata_2025 huggingface dataset:
Green Banana (Musa acuminata AAA) Wastes to Develop an Edible Film for Food Applications
Authors: Salazar Diego , Arancibia Mirari , Casado Santiago , Viteri Andrés , López-Caballero María Elvira , Montero María Pilar
Published: September 2021

In this study, edible packaging based on discarded green banana (Musa acuminata AAA) flour (whole banana and banana peel flours) was developed for food applications. Films were characterized in terms of film-forming ability, mechanical, barrier, thermal, microbiological, and sensory properties. The film forming solutions were studied for rheological properties. Two formulations were selected based on their film-forming ability: whole banana flour (2.5%), peel flour (1.5%) and glycerol (1.0 %, F-1.0 G or 1.5%, F-1.5 G). Adding 1.5% plasticizer, due to the hygroscopic effect, favored the water retention of the films, increasing the density, which also resulted in a decrease in lightness and transparency. Water activity shows no difference between the two formulations, which were water resistant for at least 25 h. DSC results showed a similar melting temperature (Tm) for both films, around 122 °C. Both films solutions showed a viscoelastic behavior in the frequency spectrum, being the elastic modulus greater in F-1.0 G film than F-1.5 G film at low frequency. F-1.0 G film was less firm, deformable and elastic, with a less compact structure and a rougher surface as confirmed by AFM, favoring a higher water vapor permeability with respect to F.1.5 G film. Microorganisms such as Enterobacteria and Staphylococcus aureus were not found in the films after a period of storage (1 year under ambient conditions). The F-1.0 G film with added spices (cumin, oregano, garlic, onion, pepper, and nutmeg) was tested for some food applications: as a snack (with or without heat treatment) and as a wrap for grilled chicken. The performance of the seasoned film during chilled storage of chicken breast was also studied. Sensory evaluation showed good overall acceptability of all applications. In addition, the chicken breast wrapped with the seasoned film registered lower counts (1-log cycle) than the control (covered with a polystyrene bag) and the film without spices. Green banana flour is a promising material to develop edible films for food applications.
The Role of Structure and Interactions in Thermoplastic Starch–Nanocellulose Composites
Authors: Csiszár Emília , Kun Dávid , Fekete Erika
Published: September 2021

Composite films were fabricated by using cellulose nanocrystals (CNCs) as reinforcement up to 50 wt% in thermoplastic starch (TPS). Structure and interactions were modified by using different types (glycerol and sorbitol) and different amounts (30 and 40%) of plasticizers. The structure of the composites was characterized by visible spectroscopy, Haze index measurements, and scanning electron microscopy. Tensile properties were determined by tensile testing, and the effect of CNC content on vapor permeability was investigated. Although all composite films are transparent and can hardly be distinguished by human eyes, the addition of CNCs somewhat decreases the transmittance of the films. This can be related to the increased light scattering of the films, which is caused by the aggregation of nanocrystals, leading to the formation of micron-sized particles. Nevertheless, strength is enhanced by CNCs, mostly in the composite series prepared with 30% sorbitol. Additionally, the relatively high water vapor permeability of TPS is considerably decreased by the incorporation of at least 20 wt% CNCs. Reinforcement is determined mostly by the competitive interactions among starch, nanocellulose, and plasticizer molecules. The aging of the films is caused by the additional water uptake from the atmosphere and the retrogradation of starch.
A Finite Element Study to Investigate the Mechanical Behaviour of Unidirectional Recycled Carbon Fibre/Glass Fibre–Reinforced Epoxy Composites
Authors: Karuppannan Gopalraj Sankar , Kärki Timo
Published: September 2021

Recycled carbon fibre–reinforced epoxy (rCF/EP) composites and recycled glass fibre–reinforced epoxy (rGF/EP) composites were numerically investigated to examine their mechanical properties, such as uniaxial tensile and impact resistance, using finite element (FE) methods. The recycled composites possess unidirectional, long and continuous fibre arrangements. A commercially available Abaqus/CAE software was used to perform an explicit non-linear analysis with a macroscale modelling approach, assuming the recycled composites as both homogenous and isotropic hardening. Five composite types were subjected to a numerical study based on the recycled fibre’s volume fraction (40 and 60%) of rCF/EP and rGF/EP, along with (100%) fibreless cured epoxy samples. The materials were defined as elastoplastic with a continuum ductile damage (DUCTCRT) model. The experimental tensile test results were processed and calibrated as primary input data for the developed FE models. The numerical tensile results, maximum principal stress and logarithmic strain were validated with their respective experimental results. The stress–strain curves of both results possess a high accuracy, supporting the developed FE model. The numerical impact tests examined the von Mises stress distribution and found an exponential decrease in the stiffness of the composite types as their strength decreased, with the 60% rCF/EP sample being the stiffest. The model was sensitive to the mesh size, hammer velocity and simulation time step. Additionally, the total internal energy and plastic dissipation energy were measured, but were higher than the experimentally measured energies, as the FE models eliminated the defects from the recycled process, such as a poor fibre wettability to resin, fibre bundle formation in rCFs and char formation in rGFs. Overall, the developed FE models predicted the results for a defect-free rCF/EP and rGF/EP composite. Hence, the adopted modelling techniques can validate the experimental results of recycled composites with complex mechanical properties and damage behaviours in tensile and impact loading conditions.
A Comparative Study of Improvement of Phycoremediation Using a Consortium of Microalgae in Municipal Wastewater Treatment Pond Systems as an Alternative Solution to Africa’s Sanitation Challenges
Authors: Oberholster Paul J. , Steyn Maronel , Botha Anna-Maria
Published: September 2021

The reuse of wastewater has been observed as a viable option to cope with increasing water stress in Africa. The present case studies evaluated the optimization of the process of phycoremediation as an alternative low-cost green treatment technology in two municipality wastewater treatment pond systems that make up the largest number of domestic sewage treatment systems on the African continent. A consortium of specific microalgae (Chlorella vulgaris and Chlorella protothecoides) was used to improve the treatment capacity of domestic wastewater at two operational municipality wastewater pond systems under different environmental conditions in South Africa. Pre- and post-phycoremediation optimization through mass inoculation of a consortium of microalgae, over a period of one year under different environmental conditions, were compared. It was evident that the higher reduction of total phosphates (74.4%) in the effluent, after treatment with a consortium of microalgae at the Motetema pond system, was possibly related to (1) the dominance of the algal taxa C. protothecoides (52%), and to a lesser extent C. vulgaris (36%), (2) more cloudless days, (3) higher air temperature, and (4) a higher domestic wastewater strength. In the case of the Brandwag pond treatment system, the higher reduction of total nitrogen can possibly be related to the dominance of C. vulgaris, different weather conditions, and lower domestic wastewater strength. The nutrient reduction data from the current study clearly presented compelling evidence in terms of the feasibility for use of this technology in developing countries to reduce nutrient loads from domestic wastewater effluent.
IAEA-Assisted Treatment of Liquid Radioactive Waste at the Saakadze Site in Georgia
Authors: Nabakhtiani Giorgi , Giorgadze Irma , Ojovan Michael I.
Published: September 2021

50 m3 of legacy liquid radioactive waste at the Saakadze site in Georgia was treated using a modular type facility with apparatuses encased in three metallic 200 L drums using as purification method the sorption/ion exchange technology. The main contaminant of water in the underground tank was the long-lived radionuclide 226Ra. The casing of processing equipment enabled an effective conditioning of all secondary waste at the end of treatment campaign which resulted in the fully purified water stored on site for further reuse or discharge, and three 200 L metallic drums with cemented radioactive waste which are currently safely stored.
Health Benefits and Pharmacological Properties of Hinokitiol
Authors: El Hachlafi Naoufal , Lakhdar Fatima , Khouchlaa Aya , Bakrim Saad , El Omari Nasreddine , Balahbib Abdelaali , Shariati Mohammad Ali , Zengin Gokhan , Fikri-Benbrahim Kawtar , Orlando Giustino , Ferrante Claudio , Meninghi Luigi , Bouyahya Abdelhakim
Published: September 2021

Hinokitiol is a natural bioactive compound found in several aromatic and medicinal plants. It is a terpenoid synthetized and secreted by different species as secondary metabolites. This volatile compound was tested and explored for its different biological properties. In this review, we report the pharmacological properties of hinokitiol by focusing mainly on its anticancer mechanisms. Indeed, it can block cell transformation at different levels by its action on the cell cycle, apoptosis, autophagy via inhibiting gene expression and dysregulating cellular signaling pathways. Moreover, hinokitiol also exhibits other pharmacological properties, including antidiabetic, anti-inflammatory, and antimicrobial effects. It showed multiple and several effects through its inhibition, interaction and/or activation of the main cellular targets inducing these pathologies.
Optimization of Cascade Cooling System Based on Lithium Bromide Refrigeration in the Polysilicon Industry
Authors: Yang Shutong , Wang Youlei , Wang Yufei
Published: September 2021

Cascade cooling systems containing different cooling methods (e.g., air cooling, water cooling, refrigerating) are used to satisfy the cooling process of hot streams with large temperature spans. An effective cooling system can significantly save energy and costs. In a cascade cooling system, the heat load distribution between different cooling methods has great impacts on the capital cost and operation cost of the system, but the relative optimization method is not well established. In this work, a cascade cooling system containing waste heat recovery, air cooling, water cooling, absorption refrigeration, and compression refrigeration is proposed. The objective is to find the optimal heat load distribution between different cooling methods with the minimum total annual cost. Aspen Plus and MATLAB were combined to solve the established mathematical optimization model, and the genetic algorithm (GA) in MATLAB was adopted to solve the model. A case study in a polysilicon enterprise was used to illustrate the feasibility and economy of the cascade cooling system. Compared to the base case, which only includes air cooling, water cooling, and compression refrigeration, the cascade cooling system can reduce the total annual cost by USD 931,025·y−1 and save 7,800,820 kWh of electricity per year. It also can recover 3139 kW of low-grade waste heat, and generate and replace a cooling capacity of 2404 kW.
Numerical Study on Flow and Release Characteristics of Gas Extinguishing Agent under Different Filling Pressure and Amount Conditions
Authors: Li Quanwei , Ge Hui , Pan Renming , Zhang Zhaojun , Chen Ruiyu
Published: September 2021

The fire-extinguishing system is an indispensable fire-protection facility on the aircraft. In order to guide weight reduction of the aircraft’s fixed gas fire-extinguishing system by improving its release efficiency, so as to improve fuel economy and reduce carbon emissions, the influence of filling pressures and filling amounts on the release efficiency of gas extinguishing agent along pipelines were studied based on numerical simulations. The release process of the fire-extinguishing system was analyzed. The effects of the filling pressure and filling amount of Halon 1301 agent on the release characteristics, such as release time, mass flow rate, and gasification ratio, were studied. Results show that the release process can be divided into three major phases, which are firstly the initial rapid filling of the pipeline, secondly the concentrated release of the liquid extinguishing agent, and thirdly the gas ejection along the pipeline. The second phase can also be subdivided into two stages: the outflow of the liquid extinguishing agent from the bottle, and the release of the residual liquid extinguishing agent along the pipeline. The release characteristics of the fire-extinguishing agent were obviously affected by the filling pressures and filling amounts. When the filling pressure was relatively low (2.832 MPa), increasing the filling pressure can significantly increase the mass flow rate, shorten the release time, and reduce the gasification ratio of the extinguishing agent during the release processes. Under the same filling pressure, with the increase of the filling amount of the extinguishing agent, the release times and the gasification ratio showed a linear increase trend, while the average mass flow rates showed a linear decrease trend.
Review on the Battery Model and SOC Estimation Method
Authors: Zhou Wenlu , Zheng Yanping , Pan Zhengjun , Lu Qiang
Published: September 2021

The accuracy of the power battery model and SOC estimation directly affects the vehicle energy management control strategy and the performance of the electric vehicle, which is of great significance to the efficient management of the battery and the improvement of the reliability of the vehicle. Based on the research of domestic and foreign battery models and the previous results of SOC estimation, this paper classifies power battery models into electrochemical mechanism models, equivalent circuit models and data-driven models. This paper analyzes the advantages and disadvantages of various battery models and current research progress. According to the choice of battery model, the previous research results of the power battery SOC estimation method are divided into three categories: the direct measurement method not based on battery model, the estimation method using black box battery model, and the battery model SOC estimation method based on state space. This paper will summarize and analyze the principles, applicable scenarios and research progress of the three categories of estimation algorithms aiming to provide references for future in-depth research. Finally, in view of the shortcomings of the battery model and estimation algorithm of the existing method, the future improvement direction is proposed.
Study of CMOS Sensing System for Radon and Alpha Radiation
Authors: Shor Roy , Nemirovsky Yael
Published: September 2021

This study focuses on a CMOS sensing system for Radon and alpha radiation, which is based on a semiconductor device that is integrated monolithically on a single chip with the Readout Circuitry, thus allowing fabrication of a low-power and low-cost sensing system. The new sensor is based on a new mosaic design of an array of Floating Gate non-volatile memory-like transistors, which are implemented in a standard CMOS technology, with a single polysilicon layer. The transistors are electrically combined in parallel and are operated at subthreshold, thus achieving very high sensitivity and reduced noise. The sensing system’s architecture and design is presented, along with key operation concepts, characterization, and analysis results. Alpha and radon exposure results are compared to commercial radon detectors. The new sensor, dubbed TODOS-Radon sensor, measures continuously, is battery operated and insensitive to humidity.
The Catalytic Role of D-block Elements and Their Compounds for Improving Sorption Kinetics of Hydride Materials: A Review
Authors: Pal Pratibha , Ting Jyh-Ming , Agarwal Shivani , Ichikawa Takayuki , Jain Ankur
Published: September 2021

The goal of finding efficient and safe hydrogen storage material motivated researchers to develop several materials to fulfil the demand of the U.S. Department of Energy (DOE). In the past few years, several metal hydrides, complex hydrides such as borohydrides and alanates, have been researched and found efficient due to their high gravimetric and volumetric density. However, the development of these materials is still limited by their high thermodynamic stability and sluggish kinetics. One of the methods to improve the kinetics is to use catalysts. Among the known catalysts for this purpose, transition metals and their compounds are known as the leading contender. The present article reviews the d-block transition metals including Ni, Co, V, Ti, Fe and Nb as catalysts to boost up the kinetics of several hydride systems. Various binary and ternary metal oxides, halides and their combinations, porous structured hybrid designs and metal-based Mxenes have been discussed as catalysts to enhance the de/rehydrogenation kinetics and cycling performance of hydrogen storage systems.
Hattendorff Differential Equation for Multi-State Markov Insurance Models
Authors: Rajaram Rajeev , Ritchey Nathan
Published: September 2021

We derive a Hattendorff differential equation and a recursion governing the evolution of continuous and discrete time evolution respectively of the variance of the loss at time t random variable given that the state at time t is j, for a multistate Markov insurance model (denoted by 2σt(j)). We also show using matrix notation that both models can be easily adapted for use in MATLAB for numerical computations.
ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation
Authors: Zhang Lilin , Yao Yunjun , Bei Xiangyi , Li Yufu , Shang Ke , Yang Junming , Guo Xiaozheng , Yu Ruiyang , Xie Zijing
Published: September 2021

Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management.
Improve the Estimation of Monocular Vision 6-DOF Pose Based on the Fusion of Camera and Laser Rangefinder
Authors: Zhu Zifa , Ma Yuebo , Zhao Rujin , Liu Enhai , Zeng Sikang , Yi Jinhui , Ding Jian
Published: September 2021

Monocular vision is one of the most commonly used noncontact six-degrees-of-freedom (6-DOF) pose estimation methods. However, the large translational DOF measurement error along the optical axis of the camera is one of its main weaknesses, which greatly limits the measurement accuracy of monocular vision measurement. In this paper, we propose a novel monocular camera and 1D laser rangefinder (LRF) fusion strategy to overcome this weakness and design a remote and ultra-high precision cooperative targets 6-DOF pose estimation sensor. Our approach consists of two modules: (1) a feature fusion module that precisely fuses the initial pose estimated from the camera and the depth information obtained by the LRF. (2) An optimization module that optimizes pose and system parameters. The performance of our proposed 6-DOF pose estimation method is validated using simulations and real-world experiments. The experimental results show that our fusion strategy can accurately integrate the information of the camera and the LRF. Further optimization carried out on this basis effectively reduces the measurement error of monocular vision 6-DOF pose measurement. The experimental results obtained from a prototype show that its translational and rotational DOF measurement accuracy can reach up to 0.02 mm and 15″, respectively, at a distance of 10 m.
Editorial to Special Issue “Remote Sensing Data Compression”
Authors: Vozel Benoit , Lukin Vladimir , Serra-Sagristà Joan
Published: September 2021

A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.
Long-Term Changes in the Land–Ocean Ecological Environment in Small Island Countries in the South Pacific: A Fiji Vision
Authors: Zhu Bozhong , Bai Yan , He Xianqiang , Chen Xiaoyan , Li Teng , Gong Fang
Published: September 2021

Small island countries in the South Pacific are ecologically fragile areas, vulnerable to climate change, and the long-term ecological changes in the sea and land have an important impact on their sustainable development. This study takes Fiji, a typical small island country in the South Pacific, as an example, to analyze the change and connection of marine and terrestrial ecosystem environments based on 30 years of multi-source, satellite, remote-sensing data. From 1991 to 2019, according to the change in forest area in Fiji, three stages were delineated: first was a period of stability, then a decrease, and then a recovery in recent years. From 1991 to 2002, Fiji’s vegetation accounted for 73% of the total area; sea environment surrounding the islands, such as sea level height and sea surface temperature, were relatively low, with high water transparency. From 2002 to 2014, with the development of forestry and tourism, vegetation decreased by 6.89% and bare land increased, which changes the runoff erosion in the drainage basin; correspondingly, the chlorophyll a concentration in three major estuaries was found to be slightly increased with low water transparency. Meanwhile, coupled with the rising sea temperature, the area of Fiji’s coral reefs shrank significantly, with 51.13% of the total loss of coral reefs occurring in the Vanua Levu, where bare land and runoff were more distributed in its drainage basin. From 2014 to 2019, Fiji’s vegetation and coral reef areas recovered from the former stage; affected by short-term climate oscillations such as El Niño-Southern Oscillation (ENSO), the sea surface temperature showed a significant abnormal drop and the water transparency decreased. In the past 30 years (1993–2018), the sea level rise rate around Fiji reached 4 mm/year, and the temperature increased by 0.3 °C, which threatens the coastal ecosystem environment, including coral reefs and mangrove; inappropriate land-use change would worsen the situation in these ecologically fragile areas.
Identifying Dynamic Changes in Water Surface Using Sentinel-1 Data Based on Genetic Algorithm and Machine Learning Techniques
Authors: Huang Zelin , Wu Wei , Liu Hongbin , Zhang Weichun , Hu Jin
Published: September 2021

The knowledge of water surface changes provides invaluable information for water resources management and flood monitoring. However, the accurate identification of water bodies is a long-term challenge due to human activities and climate change. Sentinel-1 synthetic aperture radar (SAR) data have been drawn, increasing attention to water extraction due to the availability of weather conditions, water sensitivity and high spatial and temporal resolutions. This study investigated the abilities of random forest (RF), Extreme Gradient Boosting (XGB) and support vector machine (SVM) methods to identify water bodies using Sentinel-1 imageries in the upper stream of the Yangtze River, China. Three sets of hyper-parameters including default values, optimized by grid searches and genetic algorithms, were examined for each model. Model performances were evaluated using a Sentinel-1 image of the developed site and the transfer site. The results showed that SVM outperformed RF and XGB under the three scenarios on both the validated and transfer sites. Among them, SVM optimized by genetic algorithm obtained the best accuracy with precisions of 0.9917 and 0.985, kappa statistics of 0.9833 and 0.97, F1-scores of 0.9919 and 0.9848 on validated and transfer sites, respectively. The best model was then used to identify the dynamic changes in water surfaces during the 2020 flood season in the study area. Overall, the study further demonstrated that SVM optimized using a genetic algorithm was a suitable method for monitoring water surface changes with a Sentinel-1 dataset.
Mid-Latitude Mesospheric Zonal Wave 1 and Wave 2 in Recent Boreal Winters
Authors: Shi Yu , Evtushevsky Oleksandr , Shulga Valerii , Milinevsky Gennadi , Klekociuk Andrew , Andrienko Yulia , Han Wei
Published: September 2021

Planetary waves in the mesosphere are studied using observational data and models to establish their origin, as there are indications of their generation independently of waves in the stratosphere. The quantitative relationships between zonal wave 1 and wave 2 were studied with a focus on the mid-latitude mesosphere at 50°N latitude. Aura Microwave Limb Sounder measurements were used to estimate wave amplitudes in geopotential height during sudden stratospheric warmings in recent boreal winters. The moving correlation between the wave amplitudes shows that, in comparison with the anticorrelation in the stratosphere, wave 2 positively correlates with wave 1 and propagates ahead of it in the mesosphere. A positive correlation r = 0.5–0.6, statistically significant at the 95% confidence level, is observed at 1–5-day time lag and in the 75–91 km altitude range, which is the upper mesosphere–mesopause region. Wavelet analysis shows a clear 8-day period in waves 1 and 2 in the mesosphere at 0.01 hPa (80 km), while in the stratosphere–lower mesosphere, the period is twice as long at 16 days; this is statistically significant only in wave 2. Possible sources of mesospheric planetary waves associated with zonal flow instabilities and breaking or dissipation of gravity waves are discussed.
Recent Changes of Glacial Lakes in the High Mountain Asia and Its Potential Controlling Factors Analysis
Authors: Zhang Meimei , Chen Fang , Zhao Hang , Wang Jinxiao , Wang Ning
Published: September 2021

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their mixed reflectance with backgrounds. In addition, most studies have only focused on the changes in the area of a glacial lake as a whole, but do not involve the actual changes of per pixel on its boundary and the potential controlling factors. In this research, we produced more accurate and complete maps of glacial lake extent in the HMA in 2008, 2012, and 2016 with consistent time intervals using Landsat satellite images and the Google Earth Engine (GEE) cloud computing platform, and further studied the formation, distribution, and dynamics of the glacial lakes. In total, 17,016 and 21,249 glacial lakes were detected in 2008 and 2016, respectively, covering an area of 1420.15 ± 232.76 km2 and 1577.38 ± 288.82 km2; the lakes were mainly located at altitudes between 4400 m and 5600 m. The annual areal expansion rate was approximately 1.38% from 2008 to 2016. To explore the cause of the rapid expansion of individual glacial lakes, we investigated their long-term expansion rates by measuring changes in shoreline positions. The results show that glacial lakes are expanding rapidly in areas close to glaciers and had a high expansion rate of larger than 20 m/yr from 2008 to 2016. Glacial lakes in the Himalayas showed the highest expansion rate of more than 2 m/yr, followed by the Karakoram Mountains (1.61 m/yr) and the Tianshan Mountains (1.52 m/yr). The accelerating rate of glacier ice and snow melting caused by global warming is the primary contributor to glacial lake growth. These results may provide information that will help in the understanding of detailed lake dynamics and the mechanism, and also facilitate the scientific recognition of the potential hazards associated with glacial lakes in this region.
Optimized Estimation of Leaf Mass per Area with a 3D Matrix of Vegetation Indices
Authors: Chen Yuwen , Sun Jia , Wang Lunche , Shi Shuo , Gong Wei , Wang Shaoqiang , Tagesson Torbern
Published: September 2021

Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was constructed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, respectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.
Improving YOLOv5 with Attention Mechanism for Detecting Boulders from Planetary Images
Authors: Zhu Linlin , Geng Xun , Li Zheng , Liu Chun
Published: September 2021

It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.
A Physics-Driven CNN Model for Real-Time Sea Waves 3D Reconstruction
Authors: Pistellato Mara , Bergamasco Filippo , Torsello Andrea , Barbariol Francesco , Yoo Jeseon , Jeong Jin-Yong , Benetazzo Alvise
Published: September 2021

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpredictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigating the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC.
Optimizing the Sowing Date to Improve Water Management and Wheat Yield in a Large Irrigation Scheme, through a Remote Sensing and an Evolution Strategy-Based Approach
Authors: Belaqziz Salwa , Khabba Saïd , Kharrou Mohamed Hakim , Bouras El Houssaine , Er-Raki Salah , Chehbouni Abdelghani
Published: September 2021

This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach based on the covariance matrix adaptation–evolution strategy (CMA-ES) was proposed to optimize both the spatiotemporal distribution of sowing dates and the irrigation schedules, and then evaluate wheat crop using the 2011–2012 growing season dataset. Six sowing scenarios were simulated and compared to identify the most optimal spatiotemporal sowing calendar. The obtained results showed that with reference to the existing sowing patterns, early sowing of wheat leads to higher yields compared to late sowing (from 7.40 to 5.32 t/ha). Compared with actual conditions in the study area, the spatial heterogeneity is highly reduced, which increased equity between farmers. The results also showed that the proportion of plots irrigated in time can be increased (from 40% to 82%) compared to both the actual irrigation schedules and to previous results of irrigation optimization, which did not take into consideration sowing dates optimization. Furthermore, considerable reduction of more than 40% of applied irrigation water can be achieved by optimizing sowing dates. Thus, the proposed approach in this study is relevant for irrigation managers and farmers since it provides an insight on the consequences of their agricultural practices regarding the wheat sowing calendar and irrigation scheduling and can be implemented to recommend the best practices to adopt.
Maximizing Contents of Phytochemicals Obtained from Dried Sour Cherries by Ultrasound-Assisted Extraction
Authors: Milić Anita , Daničić Tatjana , Tepić Horecki Aleksandra , Šumić Zdravko , Bursać Kovačević Danijela , Putnik Predrag , Pavlić Branimir
Published: September 2021

Sour cherries were first dried by vacuum drying and then used as material for obtaining extracts rich in bioactive compounds by ultrasound-assisted extraction (UAE). The first step was to apply a factorial design for the preliminary experiments to determine the most influential UAE factors, and thus the three studied parameters were chosen as the most suitable for the design of the main experiment (temperature, liquid–solid ratio and ethanol concentration). In this part, the contents of total phenols and the total content of monomeric anthocyanins were taken for responses. For the further optimization of UAE, experimental design (face-centered) was applied, and the yield, total phenolics, flavonoid content and content of monomeric anthocyanins and antioxidant activity (DPPH, ABTS and FRAP assays) were analyzed. Temperature (40–80 °C), ethanol concentration (40–80%, w/w) and liquid–solid ratio (10–30 mL/g) were investigated as independent variables. The obtained experimental results were fitted to a second-order polynomial model and analysis of variance was used to determine the fit of the model and the optimal conditions for investigated responses. High quality extracts with high concentrations of polyphenols and anthocyanins were also obtained, which could be used as food additives.
Analysis of Volatiles in Food Products
Authors: Starowicz Małgorzata
Published: September 2021

The evaluation of volatiles in food is an important aspect of food production. It gives knowledge about the quality of foods and their relationship to consumers’ choices. Alcohols, aldehydes, acids, esters, terpenes, pyrazines, and furans are the main chemical groups that are involved in aroma formation. They are products of food processing: thermal treatment, fermentation, storage, etc. Food aroma is a mixture of varied molecules. Because of this, the analysis of aroma composition can be challenging. The four main steps can be distinguished in the evaluation of the volatiles in the food matrix as follows: (1) isolation and concentration; (2) separation; (3) identification; and (4) sensory characterization. The most commonly used techniques to separate a fraction of volatiles from non-volatiles are solid-phase micro-(SPME) and stir bar sorptive extractions (SBSE). However, to study the active components of food aroma by gas chromatography with olfactometry detector (GC-O), solvent-assisted flavor evaporation (SAFE) is used. The volatiles are mostly separated on GC systems (GC or comprehensive two-dimensional GCxGC) with the support of mass spectrometry (MS, MS/MS, ToF–MS) for chemical compound identification. Besides omics techniques, the promising part could be a study of aroma using electronic nose. Therefore, the main assumptions of volatolomics are here described.
Currently Applied Extraction Processes for Secondary Metabolites from Lippia turbinata and Turnera diffusa and Future Perspectives
Authors: Martínez-Ávila Guillermo C. G. , Aguilar-Zarate Pedro , Rojas Romeo
Published: September 2021

The poleo (Lippia turbinata Griseb.) and damiana (Turnera diffusa Wild) are two of the most valued species in the Mexican semidesert due to their medicinal uses. The conventional essential oil extraction process is hydrodistillation, and for the extraction of antioxidants, the use of organic solvents. However, these techniques are time-consuming and degrade thermolabile molecules, and the efficiency of the process is dependent on the affinity of the solvent for bioactive compounds. Likewise, they generate solvent residues such as methanol, hexane, petroleum ether, toluene, chloroform, etc. Therefore, in recent years, ecofriendly alternatives such as ohmic heating, microwaves, ultrasound, and supercritical fluids have been studied. These methodologies allow reducing the environmental impact and processing times, in addition to increasing yields at a lower cost. Currently, there is no up-to-date information that provides a description of the ecofriendly trends for the recovery process of essential oils and antioxidants from Lippia turbinata and Turnera diffusa. This review includes relevant information on the most recent advancements in these processes, including conditions and methodological foundation.
The Use of an Unmanned Aerial Vehicle for Tree Phenotyping Studies
Authors: Ahmed Shara , Nicholson Catherine E. , Muto Paul , Perry Justin J. , Dean John R.
Published: September 2021

A strip of 20th-century landscape woodland planted alongside a 17th to mid-18th century ancient and semi-natural woodland (ASNW) was investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with a multispectral image camera (MSI). A simple classification approach of normalized difference spectral index (NDSI), derived using principal component analysis (PCA), enabled the identification of the non-native trees within the 20th-century boundary. The tree species within this boundary, classified by NDSI, were further segmented by the machine learning segmentation method of k-means clustering. This combined innovative approach has enabled the identification of multiple tree species in the 20th-century boundary. Phenotyping of trees at canopy level using the UAV with MSI, across 8052 m2, identified black pine (23%), Norway maple (19%), Scots pine (12%), and sycamore (19%) as well as native trees (oak and silver birch, 27%). This derived data was corroborated by field identification at ground-level, over an area of 6785 m2, that confirmed the presence of black pine (26%), Norway maple (30%), Scots pine (10%), and sycamore (14%) as well as other trees (oak and silver birch, 20%). The benefits of using a UAV, with an MSI camera, for monitoring tree boundaries next to a new housing development are demonstrated.
Can Regional Gender Ideologies Account for Variation of Gender Pay Gaps? The Case of Germany
Authors: Hamjediers Maik
Published: September 2021

While research often invokes gender disparities in wage-determining characteristics to explain gender pay gaps, why these gender disparities and gender pay gaps vary across contexts has received less attention. Therefore, I analyze how subnational gender ideologies predict gender pay gaps in two ways: as directly affecting gender pay gaps and as indirectly predicting gender pay gaps through intermediate gender disparities in determinants of wage. The analyses are based on German survey data (SOEP 2014–2018) supplemented with regional-level statistics. First, I leverage regional differences in predictors of gender ideologies to estimate region-specific gender ideologies. Mapping these gender ideologies across Germany reveals substantial regional variation. Second, multi-level models provide region-specific gender disparities in wage determinants and gender pay gaps. Results reveal that traditional gender ideologies are associated with women gaining less labor market experience and working less often in full-time jobs or supervising positions. In addition to this indirect association, gender ideologies directly predict the extent of adjusted gender pay gaps. These associations contribute novel evidence on regional variation of gender ideologies and how they can underlie explanations often invoked for gender pay gaps.
Discourse of Folk Literature on Healthy Ageing: A Case Study in Sindh, Pakistan during the Pandemic Crisis
Authors: David Maya Khemlani , Ali Ameer
Published: September 2021

Due to COVID-19 and the repeated imposition of lockdowns in Pakistan’s Sindh province, the life of senior citizens has become challenging. Given the scarcity of health care policies targeted at Sindh’s aged persons, the use of folk literature as therapy has increased to support against isolation, depression, and distress caused by COVID-19 and lockdowns. Although research on healthy ageing from medical and health care perspectives has been increasingly conducted in different contexts, there is a need to explore how folk literature can contribute to psychological, spiritual, and social wellbeing. Therefore, this research, conducted by collecting data from 15 aged participants through interviews and conversations, seeks to explore how senior Sindhis have used folk literature such as poetry, proverbs, and tales as therapy for their healing. Findings show that the participants’ use of Sindhi folk literature contributes to their psychological (eudemonic, evaluative, and hedonic) wellbeing, spiritual healing, and social satisfaction.
MRE: A Military Relation Extraction Model Based on BiGRU and Multi-Head Attention
Authors: Lu Yiwei , Yang Ruopeng , Jiang Xuping , Zhou Dan , Yin Changsheng , Li Zizhuo
Published: September 2021

A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extraction is one of the main tasks of military information extraction, which aims at identifying the relation between two named entities from unstructured military texts. However, the traditional methods of extracting military relations cannot easily resolve problems such as inadequate manual features and inaccurate Chinese word segmentation in military fields, failing to make full use of symmetrical entity relations in military texts. With our approach, based on the pre-trained language model, we present a Chinese military relation extraction method, which combines the bi-directional gate recurrent unit (BiGRU) and multi-head attention mechanism (MHATT). More specifically, the conceptual foundation of our method lies in constructing an embedding layer and combining word embedding with position embedding, based on the pre-trained language model; the output vectors of BiGRU neural networks are symmetrically spliced to learn the semantic features of context, and they fuse the multi-head attention mechanism to improve the ability of expressing semantic information. On the military text corpus that we have built, we conduct extensive experiments. We demonstrate the superiority of our method over the traditional non-attention model, attention model, and improved attention model, and the comprehensive evaluation value F1-score of the model is improved by about 4%.