bluuebunny/crossref_metadata_2025 huggingface dataset:
Arabidopsis EGY1 Is Critical for Chloroplast Development in Leaf Epidermal Guard Cells
Authors: Sanjaya Alvin , Muramatsu Ryohsuke , Sato Shiho , Suzuki Mao , Sasaki Shun , Ishikawa Hiroki , Fujii Yuki , Asano Makoto , Itoh Ryuuichi D. , Kanamaru Kengo , Ohbu Sumie , Abe Tomoko , Kazama Yusuke , Fujiwara Makoto T.
Published: June 2021

In Arabidopsis thaliana, the Ethylene-dependent Gravitropism-deficient and Yellow-green 1 (EGY1) gene encodes a thylakoid membrane-localized protease involved in chloroplast development in leaf mesophyll cells. Recently, EGY1 was also found to be crucial for the maintenance of grana in mesophyll chloroplasts. To further explore the function of EGY1 in leaf tissues, we examined the phenotype of chloroplasts in the leaf epidermal guard cells and pavement cells of two 40Ar17+ irradiation-derived mutants, Ar50-33-pg1 and egy1-4. Fluorescence microscopy revealed that fully expanded leaves of both egy1 mutants showed severe chlorophyll deficiency in both epidermal cell types. Guard cells in the egy1 mutant exhibited permanent defects in chloroplast formation during leaf expansion. Labeling of plastids with CaMV35S or Protodermal Factor1 (PDF1) promoter-driven stroma-targeted fluorescent proteins revealed that egy1 guard cells contained the normal number of plastids, but with moderately reduced size, compared with wild-type guard cells. Transmission electron microscopy further revealed that the development of thylakoids was impaired in the plastids of egy1 mutant guard mother cells, guard cells, and pavement cells. Collectively, these observations demonstrate that EGY1 is involved in chloroplast formation in the leaf epidermis and is particularly critical for chloroplast differentiation in guard cells.
Differential Detection of the Tobamoviruses Tomato Mosaic Virus (ToMV) and Tomato Brown Rugose Fruit Virus (ToBRFV) Using CRISPR-Cas12a
Authors: Alon Dan Mark , Hak Hagit , Bornstein Menachem , Pines Gur , Spiegelman Ziv
Published: June 2021

CRISPR/Cas12a-based detection is a novel approach for the efficient, sequence-specific identification of viruses. Here we adopt the use of CRISPR/Cas12a to identify the tomato brown rugose fruit virus (ToBRFV), a new and emerging tobamovirus which is causing substantial damage to the global tomato industry. Specific CRISPR RNAs (crRNAs) were designed to detect either ToBRFV or the closely related tomato mosaic virus (ToMV). This technology enabled the differential detection of ToBRFV and ToMV. Sensitivity assays revealed that viruses can be detected from 15–30 ng of RT-PCR product, and that specific detection could be achieved from a mix of ToMV and ToBRFV. In addition, we show that this method can enable the identification of ToBRFV in samples collected from commercial greenhouses. These results demonstrate a new method for species-specific detection of tobamoviruses. A future combination of this approach with isothermal amplification could provide a platform for efficient and user-friendly ways to distinguish between closely related strains and resistance-breaking pathogens.
Anti-Inflammatory Effects of Abeliophyllum distichum Nakai (Cultivar Okhwang 1) Callus through Inhibition of PI3K/Akt, NF-κB, and MAPK Signaling Pathways in Lipopolysaccharide-Induced Macrophages
Authors: Jang Tae-Won , Park Jae-Ho
Published: June 2021

One of the Korean endemic plants, Abeliophyllum distichum Nakai (Oleaceae), contains acteoside, which is a glycoside exhibiting neuroprotective, anti-inflammation effects and antibacterial capacities. We conducted an investigation on the effects of the callus of A. distichum (cultivar Okhwang 1, CAO) on pro-inflammatory mediators released following nuclear factor-кB (NF-кB), phosphatidylinositol 3-kinase/Akt (PI3K-Akt) and mitogen-activated protein kinase (MAPK) signal activation in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Immunoblotting was employed to find out the expression of cyclooxygenase-2 (COX-2), inducible nitric oxide (iNOS), and activation of MAPK molecules, NF-κB and Akt. Cytokines, COX-2, and iNOS gene expression were assessed using polymerase chain reaction techniques. Cytokines, COX-2, and iNOS gene expression were assessed using polymerase chain reaction techniques. High-performance liquid chromatography revealed that CAO was rich in acteoside and isoacteoside. As a result, CAO inhibited the generation of NO, cytokines, COX-2, and iNOS expression. Further, translocation to the nuclear of NF-κB p65 and degradation of the inhibitor of NF-кB (IкB) were alleviated by suppressing phosphorylation. Additionally, CAO significantly impacted MAPK pathway activation by potentially reducing phosphorylation of MAPKs. These results indicate that the anti-inflammatory effect of CAO is mediated via the inhibition of MAPK, PI3K/Akt, and NF-κB signaling pathways, probably via glycosides, phenolics, and flavonoids bioactivity derived from plants. CAO can serve as a potential anti-inflammatory agent, which alleviates inflammation factors and act through specific cell signaling pathways.
An Integrated Kano Model, Fuzzy Analytical Hierarchy Process, and Decision Matrix for Sustainable Supplier Selection in Palm Oil Industries Indonesia, a Case Study
Authors: Kuo Tsai-Chi , Muniroh Muniroh , Fau Kristin Halisa
Published: June 2021

Industries have to integrate environmental, social, and economic aspects into their supply chain management to achieve sustainability. Hence, the industry needs to take appropriate actions in choosing the right suppliers. The aim of this study is to develop a framework for selecting sustainable suppliers by integrating quality management tools using the Kano model, Fuzzy Analytical Hierarchy Process, and Decision Matrix Method. To identify the critical sustainability criteria, the Kano model by the clustering the criteria for sustainable selection supplier was used. We then used the Fuzzy Analytical Hierarchy Process to determine the weight of each criterion and applied the Decision Matrix Method to select the most sustainable supplier. Afterward, the appropriate proposed framework was implemented in one of the palm oil industries in Indonesia to validate that the framework is applicable and useful. The study shows that the environmental dimension is the most sustainable supplier criteria followed by economy and social dimension. Quality, pollution control, and information disclosures were found to be important sub-dimensions for sustainable supplier selection.
Understanding the Evolution and Applications of Intelligent Systems via a Tri-X Intelligence (TI) Model
Authors: Zhao Min , Ning Zhenbo , Wang Baicun , Peng Chen , Li Xingyu , Huang Sihan
Published: June 2021

The evolution and application of intelligence have been discussed from perspectives of life, control theory and artificial intelligence. However, there has been no consensus on understanding the evolution of intelligence. In this study, we propose a Tri-X Intelligence (TI) model, aimed at providing a comprehensive perspective to understand complex intelligence and the implementation of intelligent systems. In this work, the essence and evolution of intelligent systems (or system intelligentization) are analyzed and discussed from multiple perspectives and at different stages (Type I, Type II and Type III), based on a Tri-X Intelligence model. Elemental intelligence based on scientific effects (e.g., conscious humans, cyber entities and physical objects) is at the primitive level of intelligence (Type I). Integrated intelligence formed by two-element integration (e.g., human-cyber systems and cyber-physical systems) is at the normal level of intelligence (Type II). Complex intelligence formed by ternary-interaction (e.g., a human-cyber-physical system) is at the dynamic level of intelligence (Type III). Representative cases are analyzed to deepen the understanding of intelligent systems and their future implementation, such as in intelligent manufacturing. This work provides a systematic scheme, and technical supports, to understand and develop intelligent systems.
H2B Type 1-K Accumulates in Senescent Fibroblasts with Persistent DNA Damage along with Methylated and Phosphorylated Forms of HMGA1
Authors: Contrepois Kévin , Mann Carl , Fenaille François
Published: June 2021

Cellular senescence is a state of terminal proliferative arrest that plays key roles in aging by preventing stem cell renewal and by inducing the expression of a series of inflammatory factors including many secreted proteins with paracrine effects. The in vivo identification of senescent cells is difficult due to the absence of universal biomarkers. Chromatin modifications are key aspects of the senescence transition and may provide novel biomarkers. We used a combined protein profiling and bottom-up mass spectrometry approach to characterize the isoforms and post-translational modifications of chromatin proteins over time in post-mitotic human fibroblasts in vitro. We show that the H2B type 1-K variant is specifically enriched in deep senescent cells with persistent DNA damage. This accumulation was not observed in quiescent cells or in cells induced into senescence without DNA damage by expression of the RAF kinase. Similarly, HMGA1a di-methylated and HMGA1b tri-phosphorylated forms accumulated exclusively in the chromatin of cells in deep senescent conditions with persistent DNA damage. H2B type 1-K and modified HMGA1 may thus represent novel biomarkers of senescent cells containing persistent DNA damage.
Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield
Authors: Manivasagam V.S. , Sadeh Yuval , Kaplan Gregoriy , Bonfil David J. , Rozenstein Offer
Published: June 2021

Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester’s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g/m2) accuracy than that of Sentinel-2 LAI (RMSE = 88 g/m2). Even though the spatial yield estimation was only moderately correlated to the ground truth (R2 = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability.
Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults
Authors: Mokhtari Sohrab , Abbaspour Alireza , Yen Kang K. , Sargolzaei Arman
Published: June 2021

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter’s dynamic model to detect faults in the actuators and navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter actuators, and the helicopter tracks the desired trajectory without any interruption.
Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data
Authors: Perich Gregor , Aasen Helge , Verrelst Jochem , Argento Francesco , Walter Achim , Liebisch Frank
Published: June 2021

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.
Spatiotemporal Continuous Impervious Surface Mapping by Fusion of Landsat Time Series Data and Google Earth Imagery
Authors: Chen Rui , Li Xiaodong , Zhang Yihang , Zhou Pu , Wang Yalan , Shi Lingfei , Jiang Lai , Ling Feng , Du Yun
Published: June 2021

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.
Detection of Changes in Arable Chernozemic Soil Health Based on Landsat TM Archive Data
Authors: Savin Igor , Prudnikova Elena , Chendev Yury , Bek Anastasia , Kucher Dmitry , Dokukin Petr
Published: June 2021

When soils are used for a long period of time as arable land, their properties change. This can lead to soil degradation and loss of fertility, as well as other important soil biosphere functions. Obtaining data on the trends in arable soil conditions over large areas using traditional field survey methods is expensive and time-consuming. Currently, there are large archives of satellite data that can be used to monitor the status of arable soils. The analysis of changes in the color of the surface of arable chernozem soils of the Belgorod region, for the period from 1985 to the present, has been carried out based on the analysis of Landsat TM5 satellite data and information about the spectral reflectance of the soils of the region. It is found that, on most parts of arable lands of the region, the color of the soil surface has not changed significantly since 1985. Color changes were revealed on 11% of the analyzed area. The greatest changes are connected with the humus content and moisture content of soils. The three most probable reasons for the change of humus content in an arable horizon of soils are as follows: the dehumidification of soils during plowing; the reduction of the humus content due to water erosion; and the increase in humus content due to changes in the land-use system of the region in recent years. The change in soil moisture regime has mainly been found in arable lands in river valleys, most likely conditioned by the natural evolution of soils. Trends of increasing soil moisture are prevalent. The revealed regularities testify to the high stability of arable soils in the region during the last few decades.
Measurements of Rainfall Rate, Drop Size Distribution, and Variability at Middle and Higher Latitudes: Application to the Combined DPR-GMI Algorithm
Authors: Bringi Viswanathan , Grecu Mircea , Protat Alain , Thurai Merhala , Klepp Christian
Published: June 2021

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter NW, which normalizes the concentrations, and a scaling diameter Dm, which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between NW and Dm, on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the NW–Dm relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (Dm < 1 mm) and large drops (Dm > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates.
Preliminary Significant Wave Height Retrieval from Interferometric Imaging Radar Altimeter Aboard the Chinese Tiangong-2 Space Laboratory
Authors: Ren Lin , Yang Jingsong , Dong Xiao , Jia Yongjun , Zhang Yunhua
Published: June 2021

The interferometric imaging radar altimeter (InIRA) aboard the Chinese Tiangong-2 space laboratory is the first spaceborne imaging radar working at low incidence angles. This study focuses on the retrieval of significant wave heights (SWHs) from InIRA data. The retrieved SWHs can be used for correcting the sea state bias of InIRA-derived sea surface heights and can supplement SWH products from other spaceborne sensors. First, we analyzed tilt, range bunching and velocity bunching wave modulations at low incidence angles, and we found clear dependencies between the SWH and two defined factors, range and azimuth integration, for ocean waves in the range and azimuth directions, respectively. These dependencies were further confirmed using InIRA measurements and collocated WaveWatch III (WW3) data. Then, an empirical orthogonal SWH model using the range and azimuth integration factors as model inputs was proposed. The model was segmented by the incidence angle, and the model coefficients were estimated by fitting the collocation at each incidence angle bin. Finally, the SWHs were retrieved from InIRA data using the proposed model. The retrievals were validated using both WW3 and altimeter (JASON2, JASON3, SARAL, and HY2A) SWHs. The validation with WW3 data shows a root mean square error (RMSE) of 0.43 m, while the average RMSE with all traditional altimeter data is 0.48 m. This indicates that the InIRA can be used to measure SWHs.
Impacts of Reservoir Water Level Fluctuation on Measuring Seasonal Seismic Travel Time Changes in the Binchuan Basin, Yunnan, China
Authors: Liu Chunyu , Yang Hongfeng , Wang Baoshan , Yang Jun
Published: June 2021

An airgun source in a water reservoir has been developed in the past decade as a green active source that had been proven effective to derive short-term subsurface structural changes. However, seasonal water level fluctuation in the reservoir affects the airgun signal, and thus whether the airgun signals can be used to derive robust seasonal variation in subsurface structure remains unclear. We use the airgun data observed in the Binchuan basin to estimate the seasonal variation of seismic travel time and compare the results with those derived from ambient noise data in the same frequency band. Our main observation is that seasonal change δt/t from airgun is negatively correlated to the variation of dominant frequency and water table fluctuation in the reservoir. One possible explanation is that water table fluctuation in the reservoir affects the dominant frequency of the airgun signal and causes significant phase shift. We also compute the travel time changes in P-wave from the empirical Green’s function after deconvolving the waveforms from a reference station that is 50 m from the airgun source. The dominant frequency after deconvolution still shows seasonal variation and correlates inversely to the travel time changes, suggesting that deconvolution cannot completely eliminate the source effect on travel time changes. We also use ambient noise cross-correlation to retrieve coda waves and then derive travel time changes in monthly stacked cross-correlations relative to a yearly average cross-correlation. We observe that seismic travel time increases to its local maximum in the end of August. The travel time changes lag behind the precipitation for about one month. We apply a poroelastic physical model to explain seismic travel time changes and find that a combined effect from precipitation and evaporation might induce the seasonal changes as shown in the ambient noise data. However, the pattern of travel time changes from the airgun differs from that from ambient noise, reflecting the strong effects of airgun source property changes. Therefore, we should be cautious to derive long-term subsurface structural variation from the airgun source and put more attention on stabilizing the dominant frequency of each excitation in the future experiments.
A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing
Authors: Hu Heng , Liu Min , Zhong Jiqin , Deng Xin , Cao Yunchang , Fang Peng
Published: June 2021

A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3.
A Pansharpening Generative Adversarial Network with Multilevel Structure Enhancement and a Multistream Fusion Architecture
Authors: Zhang Liping , Li Weisheng , Huang Hefeng , Lei Dajiang
Published: June 2021

Deep learning has been widely used in various computer vision tasks. As a result, researchers have begun to explore the application of deep learning for pansharpening and have achieved remarkable results. However, most current pansharpening methods focus only on the mapping relationship between images and the lack overall structure enhancement, and do not fully and completely research optimization goals and fusion rules. Therefore, for these problems, we propose a pansharpening generative adversarial network with multilevel structure enhancement and a multistream fusion architecture. This method first uses multilevel gradient operators to obtain the structural information of the high-resolution panchromatic image. Then, it combines the spectral features with multilevel gradient information and inputs them into two subnetworks of the generator for fusion training. We design a comprehensive optimization goal for the generator, which not only minimizes the gap between the fused image and the real image but also considers the adversarial loss between the generator and the discriminator and the multilevel structure loss between the fused image and the panchromatic image. It is worth mentioning that we comprehensively consider the spectral information and the multilevel structure as the input of the discriminator, which makes it easier for the discriminator to distinguish real and fake images. Experiments show that our proposed method is superior to state-of-the-art methods in both the subjective visual and objective assessments of fused images, especially in road and building areas.
Numerical Analysis of Enhanced Conductive Deep Borehole Heat Exchangers
Authors: Renaud Theo , Pan Lehua , Doran Hannah , Falcone Gioia , Verdin Patrick G.
Published: June 2021

Geothermal energy is a reliable and mature energy source, but it represents less than 1% of the total renewable energy mix. While the enhanced geothermal system (EGS) concept faces technical validation challenges and suffers from public acceptance issues, the development of unconventional deep-well designs can help to improve their efficiency and reliability. Modelling single-EGS-well designs is key to assessing their long-term thermal performances, particularly in unconventional geological settings. Numerical results obtained with the T2WELL/EOS1 code have been validated with available experimental data from a deep borehole heat exchanger (DBHE), where a temperature of 358 ∘C has been measured at a depth of 1962 m. Based on a calibrated model, the thermal performances of two enhanced thermal conductive DBHEs with graphite were compared for high geothermal gradients. The analysis highlights the potential recovery of a variable fraction of vapour. Graphite used along the well appears to be the most suitable solution to enhance the thermal output by 5 to 8% when compared to conventional wells. The theoretical implementation of such well in the Newberry volcano field was investigated with a single and doublet DBHE. The findings provide a robust methodology to assess alternative engineering solutions to current geothermal practices.
Agent-Based Modeling of the Hajj Rituals with the Possible Spread of COVID-19
Authors: Al-Shaery Ali M. , Hejase Bilal , Tridane Abdessamad , Farooqi Norah S. , Jassmi Hamad Al
Published: June 2021

With the coronavirus (COVID-19) pandemic continuing to spread around the globe, there is an unprecedented need to develop different approaches to containing the pandemic from spreading further. One particular case of importance is mass-gathering events. Mass-gathering events have been shown to exhibit the possibility to be superspreader events; as such, the adoption of effective control strategies by policymakers is essential to curb the spread of the pandemic. This paper deals with modeling the possible spread of COVID-19 in the Hajj, the world’s largest religious gathering. We present an agent-based model (ABM) for two rituals of the Hajj: Tawaf and Ramy al-Jamarat. The model aims to investigate the effect of two control measures: buffers and face masks. We couple these control measures with a third control measure that can be adopted by policymakers, which is limiting the capacity of each ritual. Our findings show the impact of each control measure on the curbing of the spread of COVID-19 under the different crowd dynamics induced by the constraints of each ritual.
Wind Blade Twist Correction for Enhanced Annual Energy Production of Wind Turbines
Authors: Debbache Mohammed , Hazmoune Messaoud , Derfouf Semcheddine , Ciupageanu Dana-Alexandra , Lazaroiu Gheorghe
Published: June 2021

Blade geometry is an important design parameter that influences global wind turbine energy harvesting performances. The geometric characteristics of the blade profile are obtained by determining the distribution of the chord and twist angle for each blade section. In order to maximize the wind energy production, implying a maximum lift-to-drag ratio for each wind speed, this distribution should be optimized. This paper presents a methodology to numerically determine the change in the twist angle by introducing a range of pitch angles for the maximum power coefficient case. The obtained pitch values were distributed from the root to the tip of blade. The results prove that the power coefficient increases for wind speeds greater than the rated point, which improves the yearly production of energy by 5% compared to the reference case.
Innovative Contract Solutions for the Provision of Agri-Environmental Climatic Public Goods: A Literature Review
Authors: Olivieri Matteo , Andreoli Maria , Vergamini Daniele , Bartolini Fabio
Published: June 2021

Lack of public funding and environmental deterioration are promoting the search for innovative mechanisms enabling to boost farmers’ provision of agri-environmental climate public goods. This work aims to contribute to the current debate by highlighting the role of innovative contractual solutions through a systematic review of more than 60 articles. The review analyses the potential of result-based and collective contracts as innovative solutions compared to action-based instruments, which are those currently most used. The design of innovative contracts and other mechanisms, e.g., auction and screening contracts, can reduce the policy failures due to asymmetric information. The paper emphasises the trade-off between an accurate design of agri-environmental schemes and the related administrative burden, highlighting the need for a better understanding of the role of mechanisms design into the policy cycle. Some new instruments were not analysed in the review, due to the scarcity of literature, and there is the need of more case studies providing information on the effectiveness of instruments when implemented in different contexts. We fill the gap in empirical evidence through a SWOT analysis that evaluates the effectiveness and acceptability of innovative instruments for policy purposes.
Analysis of Policies of Railway Operators Using SWOT Criteria and the SIMUS Method: A Case for the Bulgarian Railway Network
Authors: Stoilova Svetla , Munier Nolberto
Published: June 2021

This study is useful for railway operators as it enables them to verify their decisions against the results of the application of the techniques of strategic planning and multi-criteria analysis. It gives railway stakeholders concise, objective and unbiased information so that they can then make decisions and also allows them to determine the strengths and sensitivity, of the best solution found. This paper presents a methodology for the assessment of the policies of railway operators using Strengths–Weakness–Opportunities–Threats (SWOT) criteria and the Sequential Interactive Modelling for Urban Systems (SIMUS) method. The methodology of the research consists of two stages. In the first stage, the alternatives of the policies for the railway operator are formulated; the criteria in the SWOT group are defined; and the values of the criteria are determined for each of the alternatives. In the second stage, the SIMUS method is applied to rank the alternatives and assess the criteria in the SWOT groups. The criteria are interpreted as objectives and linear optimizations are performed. A comparison between the desired values for each objective of the SWOT criteria and the optimum values of the objective functions obtained by SIMUS was made. The methodology was applied to the Bulgarian railway network. Three policies for railway operation were studied. The total number of 17 railway policies criteria in the SWOT group were defined and assessed—three strengths criteria, seven weaknesses criteria, three opportunities criteria and four threats criteria. The results indicated that the best strategy is A3 (some reconstruction of the railway infrastructure and new rolling stock on some lines), with the highest score of 3.76, followed by A2 (new rolling stock on some lines), with a score of 2.71. The status-quo strategy (A1) has a very low score of 0.43, that the current situation or status-quo cannot be supported. The weights of both strengths and opportunities are both of the same importance with a weight of 0.180. It was found out that the clusters Weakness and Threats are dominant with weights of 0.4 and 0.24 respectively. The results show that the weights are all practically the same, about 0.06, and therefore, no discrimination by importance is possible. The methodology makes it possible to consider the alternatives simultaneously, and in this way, the results will reflect the effect of one criterion on all others, and permit us to quantify the differences between expected and real results.
Evaluating Performance of Public Transport Networks by Using Public Transport Criteria Matrix Analytic Hierarchy Process Models—Case Study of Stonnington, Bayswater, and Cockburn Public Transport Network
Authors: Lin Gang , Wang Shaoli , Lin Conghua , Bu Linshan , Xu Honglei
Published: June 2021

To mitigate car traffic problems, the United Nations Human Settlements Programme (UN-Habitat) issued a document that provides guidelines for sustainable development and the promotion of public transport. The efficiency of the policies and strategies needs to be evaluated to improve the performance of public transportation networks. To assess the performance of a public transport network, it is first necessary to select evaluation criteria. Based on existing indicators, this research proposes a public transport criteria matrix that includes the basic public transport infrastructure level, public transport service level, economic benefit level, and sustainable development level. A public transport criteria matrix AHP model is established to assess the performance of public transport networks. The established model selects appropriate evaluation criteria based on existing performance standards. It is applied to study the Stonnington, Bayswater, and Cockburn public transport network, representing a series of land use and transport policy backgrounds. The local public transport authorities can apply the established transport criteria matrix AHP model to monitor the performance of a public transport network and provide guidance for its improvement.
Budo in Physical Recreation as a Form of Rapprochement to Nature
Authors: Bielec Grzegorz , Dziadek Bartosz , Borysiuk Zbigniew , Cynarski Wojciech J.
Published: June 2021

Martial arts, or budo in Japanese, are practiced recreationally on a global scale. Is there a relation between the regular practice/training of various fighting arts and the attitude of these people towards the natural world? Does budo educate in this direction? Representatives of various fighting arts (n = 145) were examined using a diagnostic survey. It was found that the attitude of the respondents to nature and ecology was positive for the majority of the respondents (almost 74%). This applied to both men and women and was not determined by the level of education. The type of martial art or combat sport practiced did not differentiate this attitude.
Optical-Sensor-Based Nitrogen Management in Oat for Yield Enhancement
Authors: Shah Junaid , Wang Xiukang , Khan Sami Ullah , Khan Sajjad , Gurmani Zulfiqar Ali , Fiaz Sajid , Qayyum Abdul
Published: June 2021

The deficiency of nitrogen (N) in soil restricts agricultural productivity and its overdosage pollutes the atmosphere. Nitrogen is a vital component of protein, chlorophyll and various physiological processes. When it is applied at a recommended dose, it may be lost through fixation, leaching, volatilization and denitrification, etc. Therefore, there is a dire need to harmonize the supply of nitrogen according to crop and soil requirements. Under this situation, precision nitrogen management is one of the best options. GreenSeekerTM is an integrated optical sensor with a variable application rate and mapping system that measures crops’ nitrogen requirements. To ascertain the abovementioned facts, a research study was conducted at the National Agriculture Research Center, Islamabad, Pakistan, to examine the response of fodder oat to nitrogen management (N0 = control, N1 = 80 kg ha−1 basal dose, N2 = 40 + 40 kg ha−1 split doses, N3 = 40 kg ha−1 with one-time management with GreenSeekerTM and N4 = 20 kg ha−1 with two-time management with GreenSeekerTM) and seed rate (S1 = 80, S2 = 100, S3 = 120 and S4 = 140 kg ha−1). Data were recorded on the agronomic and physiological aspects of the crop and economic analysis was performed for GreenSeekerTM-based N application against the conventional recommended dose of nitrogen application. Mean values showed that greater number of tillers plant−1 (6), fresh weight (16572 kg) and photosynthetic rate (11.64 mmol m−2 s−1) were noted in the treatment N4 (20 kg ha−1 and two-time management with GreenSeekerTM). Greater plant height (70.8 cm) and leaf area (64.14 cm2) were recorded in treatment N2 (40 + 40 kg ha−1 split doses) as compared to the control. The effects of nitrogen on fodder oat were forecasted through NDVI. The results suggested that nitrogen treatment N4 (18 kg ha−1) managed by GreenSeeker in the PARC Oat cultivar produced the maximum NDVI value (0.68) at the booting stage among all treatments. The correlation of NDVI at the tillering and booting stages with green fodder yield was positive (R2 = 0.80). Therefore, the tillering and booting stages can be good depictive stages at early and later growth stages of fodder oat under the agro-climatic conditions of Islamabad, Pakistan. Based on the results, it is recommended to apply an initial dose of 20 kg ha−1 nitrogen along with two-time management with GreenSeekerTM for obtaining more green fodder yield in fodder oat. In Crux, with N1, a total of 80 kg ha−1 nitrogen was applied to achieve an estimated net profitability of USD 582.13. With N4, a total 58 kg ha−1 nitrogen was used to achieve a net profitability of USD 836.16; therefore, this treatment was found to be environmentally safe as compared to N1 (80 kg ha−1).
Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer
Authors: Ramadan Abd-ElHady , Kamel Salah , Khurshaid Tahir , Oh Seung-Ryle , Rhee Sang-Bong
Published: June 2021

The enhancement of photovoltaic (PV) energy systems relies on an accurate PV model. Researchers have made significant efforts to extract PV parameters due to their nonlinear characteristics of the PV system, and the lake information from the manufactures’ PV system datasheet. PV parameters estimation using optimization algorithms is a challenging problem in which a wide range of research has been conducted. The idea behind this challenge is the selection of a proper PV model and algorithm to estimate the accurate parameters of this model. In this paper, a new application of the improved gray wolf optimizer (I-GWO) is proposed to estimate the parameters’ values that achieve an accurate PV three diode model (TDM) in a perfect and robust manner. The PV TDM is developed to represent the effect of grain boundaries and large leakage current in the PV system. I-GWO is developed with the aim of improving population, exploration and exploitation balance and convergence of the original GWO. The performance of I-GWO is compared with other well-known optimization algorithms. I-GWO is evaluated through two different applications. In the first application, the real data from RTC furnace is applied and in the second one, the real data of PTW polycrystalline PV panel is applied. The results are compared with different evaluation factors (root mean square error (RMSE), current absolute error and statistical analysis for multiple independent runs). I-GWO achieved the lowest RMSE values in comparison with other algorithms. The RMSE values for the two applications are 0.00098331 and 0.0024276, respectively. Based on quantitative and qualitative performance evaluation, it can be concluded that the estimated parameters of TDM by I-GWO are more accurate than those obtained by other studied optimization algorithms.
Forest Biomass and Bioenergy Supply Chain Resilience: A Systematic Literature Review on the Barriers and Enablers
Authors: Dashtpeyma Mosayeb , Ghodsi Reza
Published: June 2021

This research aimed to systematically review the development studies pertaining to forest biomass and bioenergy supply chain resilience (SCR). In this regard, a mixed procedure was implemented in order to explore and analyze the relevant publications, and to answer the research questions. First, the databases and journals working on forest biomass and bioenergy supply chains (SCs) were identified based on the indices of the review process and the indices of the barriers and enablers. Next, data refinement was employed to filter the publications into four levels and determine the semifinal cases. Moreover, the references of the semifinal publications were tracked in order to achieve the final cases. Consequently, 88 papers were determined as the final cases through which the barriers and enablers were explored and analyzed. Furthermore, in order to meet the research gap in this area and prove the connections of those barriers and enablers with the resilience capability, their relationships with the main resilience factors were investigated. According to the assessment, the findings of this research on the definition, barriers and enablers of forest biomass and bioenergy SCR can be applied as a basis for the comprehension and optimization of the structure of SCs in the forest biomass and bioenergy industries.
A GIS Software Module for Environmental Impact Assessment of the Open Pit Mining Projects for Small Mining Operators in Kazakhstan
Authors: Zarubin Mikhail , Statsenko Larissa , Spiridonov Pavel , Zarubina Venera , Melkoumian Noune , Salykova Olga
Published: June 2021

This research article presents a software module for the environmental impact assessment (EIA) of open pit mines. The EIA software module has been developed based on the comprehensive examination of both country-specific (namely, Kazakhstan) and current international regulatory frameworks, legislation and EIA methodologies. EIA frameworks and methods have been critically evaluated, and mathematical models have been developed and implemented in the GIS software module ‘3D Quarry’. The proposed methodology and software module allows for optimised EIA calculations of open pit mines, aiming to minimise the negative impacts on the environment. The study presents an original methodology laid out as a basis for a software module for environmental impact assessment on atmosphere, water basins, soil and subsoil, tailored to the context of mining operations in Kazakhstan. The proposed software module offers an alternative to commercial off-the-shelf software packages currently used in the mining industry and is suitable for small mining operators in post-Soviet countries. It is anticipated that applications of the proposed software module will enable the transition to sustainable development in the Kazakh mining industry.
Impacts of the COVID-19 Pandemic on Active Travel Mode Choice in Bangladesh: A Study from the Perspective of Sustainability and New Normal Situation
Authors: Zafri Niaz Mahmud , Khan Asif , Jamal Shaila , Alam Bhuiyan Monwar
Published: June 2021

The COVID-19 pandemic has caused incredible impacts on people’s travel behavior. Recent studies suggest that while the demand for public transport has decreased due to passengers’ inability to maintain physical distance inside this mode, the demand for private automobile and active transport modes (walking and cycling) has increased during the pandemic. Policymakers should take this opportunity given by the pandemic and encourage people to use active transport more in the new normal situation to achieve sustainable transportation outcomes. This study explores the expected change in active transport mode usage in the new normal situation in Bangladesh based on the data from a questionnaire survey. The study finds that 56% and 45% of the respondents were expected to increase travel by walking and cycling, respectively, during the new normal situation. On the other hand, 19% of the respondents were expected to do the opposite. The study further identifies the factors influencing the expected change in travel by active transport modes during the new normal situation by developing multinomial logistic regression models. Finally, this study proposes policies to increase active transport use beyond the pandemic and ensure sustainable mobility for city dwellers and their well-being.