bluuebunny/crossref_metadata_2025 huggingface dataset:
The Intention of Becoming Religiously Moderate in Indonesian Muslims: Do Knowledge and Attitude Interfere?
Authors: Latifa Rena , Fahri Muhamad , Subchi Imam , Mahida Naufal Fadhil
Published: June 2022

As religious moderation is becoming one of the alternative solutions to prevent religious extremism behavior in Indonesian Muslims, research regarding becoming religiously moderate is essential. This research aimed to (1) construct Islamic Religious Moderation Intentions instruments. Intentions are usually shaped by knowledge and attitudes. For this reason, we also (2) constructed the instrument of Islamic Religious Moderation Knowledge and Islamic Religious Moderation Attitudes. Finally, we tried to (3) discover the intention of becoming religiously moderate in Indonesian Muslims by testing the knowledge and attitudes of religiously moderate. The participants consisted of 305 Indonesian Muslims from any religious organizational affiliation. First, we constructed the instruments from the theoretical definition of knowledge, attitudes, and intentions. Confirmatory factor analysis was used to validate the instruments. Moreover, the hypothesis was tested with multiple regression analysis. This research found three valid instruments regarding Islamic religious moderation: (1) Islamic Religious Moderation Knowledge, (2) Islamic Religious Moderation Attitudes, and (3) Islamic Religious Moderation Intentions. The hypothesis test results show a significant influence of knowledge and attitudes in forming religious moderation intentions in Indonesian Muslims. Therefore, to have any intentions of becoming a moderate Muslim, one should know “what Islamic moderation is” and have a positive attitude toward Islamic moderation. Further dissemination regarding religious moderation should be put into action to increase the knowledge and attitudes in Indonesian Muslim society.
Using Open Vector-Based Spatial Data to Create Semantic Datasets for Building Segmentation for Raster Data
Authors: Glinka Szymon , Owerko Tomasz , Tomaszkiewicz Karolina
Published: June 2022

With increasing access to open spatial data, it is possible to improve the quality of analyses carried out in the preliminary stages of the investment process. The extraction of buildings from raster data is an important process, especially for urban, planning and environmental studies. It allows, after processing, to represent buildings registered on a given image, e.g., in a vector format. With an actual image it is possible to obtain current information on the location of buildings in a defined area. At the same time, in recent years, there has been huge progress in the use of machine learning algorithms for object identification purposes. In particular, the semantic segmentation algorithms of deep convolutional neural networks which are based on the extraction of features from an image by means of masking have proven themselves here. The main problem with the application of semantic segmentation is the limited availability of masks, i.e., labelled data for training the network. Creating datasets based on manual labelling of data is a tedious, time consuming and capital-intensive process. Furthermore, any errors may be reflected in later analysis results. Therefore, this paper aims to show how to automate the process of data labelling of cadastral data from open spatial databases using convolutional neural networks, and to identify and extract buildings from high resolution orthophotomaps based on this data. The conducted research has shown that automatic feature extraction using semantic ML segmentation on the basis of data from open spatial databases is possible and can provide adequate quality of results.
Mapping Cork Oak Mortality Using Multitemporal High-Resolution Satellite Imagery
Authors: Catalão João , Navarro Ana , Calvão João
Published: June 2022

In the Mediterranean region, a significant decline in the vitality of vegetation has been observed in the last two decades, with a high forest mortality rate for several species. The increase in mortality has been attributed to water stress resulting from an increase in temperature and long periods of drought. To detect and quantify the impact of these events on tree mortality, an efficient and easy-to-use methodology for rapid damage assessment is required. Our study aims to assess the potential of high spatial resolution multispectral images from the Pleiades constellation to detect and map cork oak mortality in a pasture environment with multiple forest species. An approach based on change detection and the use of an unsupervised classifier is proposed to detect mortality at the cork oak level. The change in the values observed for three vegetation indices, NDVI, RGI, and GNDVI, between two epochs is used in an unsupervised classification algorithm to estimate the dead tree class. The classification results are accurate, with precision and recall values higher than 90%. Detailed cork oak mortality mapping is of significant use in comprehending ecosystem change as a result of tree mortality and for the implementation of mitigation mechanisms for the ongoing desertification process.
Remote Sensing of Complex Permittivity and Penetration Depth of Soils Using P-Band SAR Polarimetry
Authors: Fluhrer Anke , Jagdhuber Thomas , Tabatabaeenejad Alireza , Alemohammad Hamed , Montzka Carsten , Friedl Peter , Forootan Ehsan , Kunstmann Harald
Published: June 2022

A P-band SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm).
Shallow Crustal Structure of S-Wave Velocities in the Coastal Area of South China Constrained by Receiver Function Amplitudes
Authors: Zhang Xin , Qian Yinping , Shen Xuzhang , Huang He , Chai Haibin
Published: June 2022

As a traditional method, passive seismic exploration is used to construct the body-wave velocity structure of the upper crust, but it is cost-ineffective and depth-limited when applied to large areas. In this study, we use another more economical method to determine the S-wave velocity (SWV) of the upper crust based on the principle that the amplitude of the direct P-wave on the teleseismic receiver function is sensitive to the upper crust. Using the amplitudes of the massive receiver functions from permanent broadband seismic stations, the SWV structure of the upper crust is obtained in the coastal area of South China (CASC). A pattern of high to low SWVs is exhibited across the study area, with SWVs varying about 2.5–3.7 km/s from west to east. In the profile parallel to the coastline, lateral variations in the SWV correspond to the fault zone, indicating that the cutting depth of most coastal faults is approximately 10 km. Referring to previous studies, we deduce that the low SWV in most sub-areas can be interpreted as the joint effect of the sedimentary layer of the alluvial plain and the accumulation of underground heat flows, in addition to multistage fracturing tectonism. Moreover, the gradual change in the SWV in each profile from the surface to approximately 10 km is correlated with multiple invasions and the coverage of volcanic rocks, to a certain extent.
Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM2.5 in China—Implications for PM2.5 Remote Sensing
Authors: Qi Ling , Zheng Haotian , Ding Dian , Ye Dechao , Wang Shuxiao
Published: June 2022

PM2.5 retrieval from satellite-observed aerosol optical depth (AOD) is still challenging due to the strong impact of meteorology. We investigate influences of meteorology changes on the inter-annual variations of AOD and surface PM2.5 in China between 2006 and 2017 using a nested 3D chemical transport model, GEOS-Chem, by fixing emissions at the 2006 level. We then identify major meteorological elements controlling the inter-annual variations of AOD and surface PM2.5 using multiple linear regression. We find larger influences of meteorology changes on trends of AOD than that of surface PM2.5. On the seasonal scale, meteorology changes are beneficial to AOD and surface PM2.5 reduction in spring (1–50%) but show an adverse effect on aerosol reduction in summer. In addition, major meteorological elements influencing variations of AOD and PM2.5 are similar between spring and fall. In winter, meteorology changes are favorable to AOD reduction (−0.007 yr−1, −1.2% yr−1; p < 0.05) but enhanced surface PM2.5 between 2006 and 2017. The difference in winter is mainly attributed to the stable boundary layer that isolates surface PM2.5 from aloft. The significant decrease in AOD over the years is related to the increase in meridional wind speed at 850 hPa in NCP (p < 0.05). The increase of surface PM2.5 in NCP in winter is possibly related to the increased temperature inversion and more stable stratification in the boundary layer. This suggests that previous estimates of wintertime surface PM2.5 using satellite measurements of AOD corrected by meteorological elements should be used with caution. Our findings provide potential meteorological elements that might improve the retrieval of surface PM2.5 from satellite-observed AOD on the seasonal scale.
RANet: A Reliability-Guided Aggregation Network for Hyperspectral and RGB Fusion Tracking
Authors: Zhao Chunhui , Liu Hongjiao , Su Nan , Wang Lu , Yan Yiming
Published: June 2022

Object tracking based on RGB images may fail when the color of the tracked object is similar to that of the background. Hyperspectral images with rich spectral features can provide more information for RGB-based trackers. However, there is no fusion tracking algorithm based on hyperspectral and RGB images. In this paper, we propose a reliability-guided aggregation network (RANet) for hyperspectral and RGB tracking, which guides the combination of hyperspectral information and RGB information through modality reliability to improve tracking performance. Specifically, a dual branch based on the Transformer Tracking (TransT) structure is constructed to obtain the information of hyperspectral and RGB modalities. Then, a classification response aggregation module is designed to combine the different modality information by fusing the response predicted through the classification head. Finally, the reliability of different modalities is also considered in the aggregation module to guide the aggregation of the different modality information. Massive experimental results on the public dataset composed of hyperspectral and RGB image sequences show that the performance of the tracker based on our fusion method is better than that of the corresponding single-modality tracker, which fully proves the effectiveness of the fusion method. Among them, the RANet tracker based on the TransT tracker achieves the best performance accuracy of 0.709, indicating the effectiveness and superiority of the RANet tracker.
Full-Scale Highway Bridge Deformation Tracking via Photogrammetry and Remote Sensing
Authors: Graves William , Aminfar Kiyarash , Lattanzi David
Published: June 2022

Recent improvements in remote sensing technologies have shown that techniques such as photogrammetry and laser scanning can resolve geometric details at the millimeter scale. This is significant because it has expanded the range of structural health monitoring scenarios where these techniques can be used. In this work, we explore how 3D geometric measurements extracted from photogrammetric point clouds can be used to evaluate the performance of a highway bridge during a static load test. Various point cloud registration and deformation tracking algorithms are explored. Included is an introduction to a novel deformation tracking algorithm that uses the interpolation technique of kriging as the basis for measuring the geometric changes. The challenging nature of 3D point cloud data means that statistical methods must be employed to adequately evaluate the deformation field of the bridge. The results demonstrate a pathway from the collection of digital photographs to a mechanical analysis with results that capture the bridge deformation within one standard deviation of the mean reported value. These results are promising given that the midspan bridge deformation for the load test is only a few millimeters. Ultimately, the approaches evaluated in this work yielded errors on the order of 1 mm or less for ground truth deflections as small as 3.5 mm. Future work for this method will investigate using these results for updating finite element models.
GF-Detection: Fusion with GAN of Infrared and Visible Images for Vehicle Detection at Nighttime
Authors: Gao Peng , Tian Tian , Zhao Tianming , Li Linfeng , Zhang Nan , Tian Jinwen
Published: June 2022

Vehicles are important targets in the remote sensing applications and nighttime vehicle detection has been a hot study topic in recent years. Vehicles in the visible images at nighttime have inadequate features for object detection. Infrared images retain the contours of vehicles while they lose the color information. Thus, it is valuable to fuse infrared and visible images to improve the vehicle detection performance at nighttime. However, it is still a challenge to design effective fusion models due to the complexity of visible and infrared images. In order to improve vehicle detection performance at nighttime, this paper proposes a fusion model of infrared and visible images with Generative Adversarial Networks (GAN) for vehicle detection named GF-detection. GAN is utilized in the image reconstruction and introduced in the image fusion recently. To be specific, to exploit more features for the fusion, GAN is utilized to fuse the infrared and visible images via the image reconstruction. The generator fuses the image features and detection features, and then generates the reconstructed images for the discriminator to classify. Two branches, visible and infrared branches, are designed in the GF-detection model. Different feature extraction strategies are conducted according to the variance of the visible and infrared images. Detection features and self-attention mechanism are added to the fusion model aiming to build a detection task-driven fusion model of infrared and visible images. Extensive experiments based on nighttime images are conducted to demonstrate the effectiveness of the proposed fusion model in night vehicle detection.
Evaluating the Vulnerability of Siberian Crane Habitats and the Influences of Water Level Intervals in Poyang Lake Wetland, China
Authors: Hu Bisong , Zou Lin , Qi Shuhua , Yin Qian , Luo Jin , Zuo Lijun , Meng Yu
Published: June 2022

The hydrological situations of wetlands are critical to the habitat qualities of wintering migratory birds. It is of great value to evaluate the habitat vulnerabilities within more precise intervals of water levels and quantitatively assess the influences of water level changes. The findings are advantageous for managing wetland ecosystems and for migratory bird habitat protection. This study identified the ideal habitats for wintering Siberian cranes in Poyang Lake wetland within 1-meter water level intervals (from 5 to 16 m) based on the Landsat thematic mapper (TM), enhanced thematic mapper plus (ETM+), and operational land imager (OLI) remote sensing images taken on multiple dates in the past 30 years. Three indicators—sustainability, stability, and variety—were used to evaluate the vulnerabilities of crane habitats within various water level intervals; the spatial variations and distribution patterns of the habitat vulnerabilities were further explored. The explanatory powers of water level intervals (and others) and their paired interactive effects on the habitat vulnerabilities were quantified using the geographical detector method. The results showed that crane habitat vulnerabilities were significantly sensitive to the water level changes of Poyang Lake; the habitat vulnerabilities and their spatial distribution patterns both exhibited specific tendencies with water level increases. A water level of 12 m was identified as the potential upper threshold for the maintenance of sustainable crane habitats and a water level interval of 9–10 m was expected to be the optimal interval for facilitating the aggregation features of crane habitats. The water level interval was identified as the most dominant factor in habitat vulnerability. It explained 14.46%, 42.89%, and 21.78% of the sustainability, stability, and variety of crane habitats; the numbers were expected to increase to 22%, 49.25%, and 25.84%, respectively, with water level intervals interacting with other factors. This article provides a novel perspective in evaluating the habitat vulnerabilities of wintering migratory birds and quantifying the responses to water level changes in wetlands; the proposed approaches are applicable and practicable for habitat vulnerability assessments of other wintering birds in other typical wetlands.
Quantifying the Physical Impact of Bottom Trawling Based on High-Resolution Bathymetric Data
Authors: Schönke Mischa , Clemens David , Feldens Peter
Published: June 2022

Bottom trawling is one of the most significant anthropogenic pressures on physical seafloor integrity. The objective classification of physical impact is important to monitor ongoing fishing activities and to assess the regeneration of seafloor integrity in Marine Protected Areas. We use high-resolution bathymetric data recorded by multibeam echo sounders to parameterize the morphology of trawl mark incisions and associated mounds in the Fehmarn Belt, SW Baltic Sea. Trawl marks are recognized by continuous incisions or isolated depressions with depths up to about 25 cm. Elevated mounds fringe a subset of the trawl marks incisions. A net resuspension of sediment takes place based on the volumetric difference between trawl mark incisions and mounds. While not universally applicable, the volume of the trawl mark incisions is suggested as an indicator for the future monitoring of the physical impact of bottom trawling in the Baltic Sea basins.
Infrared and Visible Image Fusion with Deep Neural Network in Enhanced Flight Vision System
Authors: Gao Xuyang , Shi Yibing , Zhu Qi , Fu Qiang , Wu Yuezhou
Published: June 2022

The Enhanced Flight Vision System (EFVS) plays a significant role in the Next-Generation low visibility aircraft landing technology, where the involvement of optical sensing systems increases the visual dimension for pilots. This paper focuses on deploying infrared and visible image fusion systems in civil flight, particularly generating integrated results to contend with registration deviation and adverse weather conditions. The existing enhancement methods push ahead with metrics-driven integration, while the dynamic distortion and the continuous visual scene are overlooked in the landing stage. Hence, the proposed visual enhancement scheme is divided into homography estimation and image fusion based on deep learning. A lightweight framework integrating hardware calibration and homography estimation is designed for image calibration before fusion and reduces the offset between image pairs. The transformer structure adopting the self-attention mechanism in distinguishing composite properties is incorporated into a concise autoencoder to construct the fusion strategy, and the improved weight allocation strategy enhances the feature combination. These things considered, a flight verification platform accessing the performances of different algorithms is built to capture image pairs in the landing stage. Experimental results confirm the equilibrium of the proposed scheme in perception-inspired and feature-based metrics compared to other approaches.
Land Use Dynamic Changes in an Arid Inland River Basin Based on Multi-Scenario Simulation
Authors: Hou Yifeng , Chen Yaning , Li Zhi , Li Yupeng , Sun Fan , Zhang Shuai , Wang Chuan , Feng Meiqing
Published: June 2022

The Tarim River Basin is the largest inland river basin in China. It is located in an extremely arid region, where agriculture and animal husbandry are the main development industries. The recent rapid rise in population and land demand has intensified the competition for urban land use, making the water body ecosystem increasingly fragile. In light of these issues, it is important to comprehensively grasp regional land structure changes, improve the degree of land use, and reasonably allocate water resources to achieve the sustainable development of both the social economy and the ecological environment. This study uses the CA-Markov model, the PLUS model and the gray prediction model to simulate and validate land use/cover change (LUCC) in the Tarim River Basin, based on remote sensing data. The aim of this research is to discern the dynamic LUCC patterns and predict the evolution of future spatial and temporal patterns of land use. The study results show that grassland and barren land are currently the main land types in the Tarim River Basin. Furthermore, the significant expansion of cropland area and reduction in barren land area are the main characteristics of the changes during the study period (1992–2020), when about 1.60% of grassland and 1.36% of barren land converted to cropland. Over the next 10 years, we anticipate that land-use types in the basin will be dominated by changes in grassland and barren land, with an increasing trend in land area other than for cropland and barren land. Grassland will add 31,241.96 km2, mainly in the Dina River and the lower parts of the Weigan-Kuqu, Kashgar, Kriya, and Qarqan rivers, while barren land will decline 2.77%, with significant decreases in the middle and lower reaches of the Tarim River Basin. The findings of this study will provide a solid scientific basis for future land resource planning.
Concept and Design of Martian Far-IR ORE Spectrometer (MIRORES)
Authors: Ciazela Jakub , Bakala Jaroslaw , Kowalinski Miroslaw , Plocieniak Stefan , Zalewska Natalia , Pieterek Bartosz , Mrozek Tomasz , Ciazela Marta , Paslawski Grzegorz , Steslicki Marek , Szaforz Zaneta , Barylak Jaromir , Kuzaj Mateusz , Maturilli Alessandro , Helbert Joern , Muszynski Andrzej , Rataj Miroslaw , Gburek Szymon , Jozefowicz Mateusz , Marciniak Dariusz
Published: June 2022

Sulfide ores are a major source of noble (Au, Ag, and Pt) and base (Cu, Pb, Zn, Sn, Co, Ni, etc.) metals and will, therefore, be vital for the self-sustainment of future Mars colonies. Martian meteorites are rich in sulfides, which is reflected in recent findings for surface Martian rocks analyzed by the Spirit and Curiosity rovers. However, the only high-resolution (18 m/pixel) infrared (IR) spectrometer orbiting Mars, the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM), onboard the Mars Reconnaissance Orbiter (MRO), is not well-suited for detecting sulfides on the Martian surface. Spectral interference with silicates impedes sulfide detection in the 0.4–3.9 μm CRISM range. In contrast, at least three common hydrothermal sulfides on Earth and Mars (pyrite, chalcopyrite, marcasite) have prominent absorption peaks in a narrow far-IR (FIR) wavelength range of 23–28 μm. Identifying the global distribution and chemical composition of sulfide ore deposits would help in choosing useful targets for future Mars exploration missions. Therefore, we have designed a new instrument suitable for measuring sulfides in the FIR range called the Martian far-IR Ore Spectrometer (MIRORES). MIRORES will measure radiation in six narrow bands (~0.3 µm in width), including three bands centered on the sulfide absorption bands (23.2, 24.3 and 27.6 µm), two reference bands (21.5 and 26.1) and one band for clinopyroxene interference (29.0 µm). Focusing on sulfides only will make it possible to adapt the instrument size (32 × 32 × 42 cm) and mass (<10 kg) to common microsatellite requirements. The biggest challenges related to this design are: (1) the small field of view conditioned by the high resolution required for such a study (<20 m/pixel), which, in limited space, can only be achieved by the use of the Cassegrain optical system; and (2) a relatively stable measurement temperature to maintain radiometric accuracy and enable precise calibration.
SemiSANet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention
Authors: Sun Chengzhe , Wu Jiangjiang , Chen Hao , Du Chun
Published: June 2022

Change detection (CD) is one of the important applications of remote sensing and plays an important role in disaster assessment, land use detection, and urban sprawl tracking. High-accuracy fully supervised methods are the main methods for CD tasks at present. However, these methods require a large amount of labeled data consisting of bi-temporal images and their change maps. Moreover, creating change maps takes a lot of labor and time. To address this limitation, a simple semi-supervised change detection method based on consistency regularization and strong augmentation is proposed in this paper. First, we construct a Siamese nested UNet with graph attention mechanism (SANet) and pre-train it with a small amount of labeled data. Then, we feed the unlabeled data into the pre-trained SANet and confidence threshold filter to obtain pseudo-labels with high confidence. At the same time, we produce distorted images by performing strong augmentation on unlabeled data. The model is trained to make the CD results of the distorted images consistent with the corresponding pseudo-label. Extensive experiments are conducted on two high-resolution remote sensing datasets. The results demonstrate that our method can effectively improve the performance of change detection under insufficient labels. Our methods can increase the IoU by more than 25% compared to the state-of-the-art methods.
Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links
Authors: An Leike , Li Qingmei , Cheng Chengqi , Chen Bo , Qu Tengteng
Published: June 2022

With the rapid development of global satellite constellation clusters and the popularity of ground-based intelligent terminals, the interconnection between large-scale low Earth orbit satellites and ground base stations has become more frequent. The calculation of satellite position requires a large number of floating point square calculations and coordinate system conversions, with high computational complexity and long computation time. Based on the grid encoding method of GeoSOT, we propose a GeoSOT-based grid calculation for a satellite-ground position (GCSGP) model. We use binary bit operations instead of complex topological operations and floating point calculations, and use grid distance calculations instead of existing angle calculations. Thus, the relative positions of satellites to the ground can be quickly calculated and thus connected to more suitable satellites quickly. The results of the grid calculation were verified by simulation experiments. The calculation speed was improved by about 82% compared with the traditional method. The test accuracy error of the 10-level grid at a terminal 45° inclination was less than 1% at all heights, and the maximum error of the 10-level grid within a 10–45° inclination was 7.2% at 10,000 km altitude, which provides theoretical support for the subsequent grid space calculation.
Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events
Authors: Asaly Saed , Gottlieb Lee-Ad , Inbar Nimrod , Reuveni Yuval
Published: June 2022

There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).
Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images
Authors: Islam Md. Robiul , Nahiduzzaman Md. , Goni Md. Omaer Faruq , Sayeed Abu , Anower Md. Shamim , Ahsan Mominul , Haider Julfikar
Published: June 2022

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.
The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles
Authors: Jeong YiNa , Son SuRak , Lee ByungKwan , Lee SuHee
Published: June 2022

A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the safety and comfort of passengers. This paper proposes a braking-pressure and driving-direction determination system (BDDS), which computes the brake pressure and steering angle optimized for passenger safety by utilizing more diverse information than existing autonomous vehicles. The BDDS proposed in this paper consists of two modules. The road roughness classification module (RRCM) classifies the roughness of the road by using the pressure data applied to the suspension and the K-NN algorithm and computes the optimal brake pressure. The passenger recognition and sharing module (PRSM) identifies the current occupant status of the vehicle by using a body pressure sensor and CNN, shares the information with surrounding vehicles, and computes the optimal steering angle using passenger information and road information. As a result of the simulations described in this paper, the parameters of AI models were optimized. In addition, the RRCS was about 7% more accurate than the K-means clustering algorithm, and PRS was about 9% more accurate than the existing seat recognition system.
Two-Dimensional Photocatalysts for Energy and Environmental Applications
Authors: Ioannidou Thaleia , Anagnostopoulou Maria , Christoforidis Konstantinos
Published: June 2022

The depletion of fossil fuels and onset of global warming dictate the achievement of efficient technologies for clean and renewable energy sources. The conversion of solar energy into chemical energy plays a vital role both in energy production and environmental protection. A photocatalytic approach for H2 production and CO2 reduction has been identified as a promising alternative for clean energy production and CO2 conversion. In this process, the most critical parameter that controls efficiency is the development of a photocatalyst. Two-dimensional nanomaterials have gained considerable attention due to the unique properties that arise from their morphology. In this paper, examples on the development of different 2D structures as photocatalysts in H2 production and CO2 reduction are discussed and a perspective on the challenges and required improvements is given.
Flood–Ebb and Discharge Variations in Observed Salinity and Suspended Sediment in a Mesotidal Estuary
Authors: Liu Wen-Cheng , Liu Hong-Ming , Huang Wei-Che
Published: June 2022

To explore the spatial and temporal variations in salinity and suspended-sediment concentration in the Danshuei River estuary of northern Taiwan, two intensive field surveys were conducted in July 2016 and 2019 to assign high- and low-flow conditions, respectively. According to the analysis of tidal characteristics, the duration during ebb tide was longer than that during flood tide, while the maximum ebb discharge was higher than the maximum flood discharge, causing the occurrence of tidal asymmetry during ebb and flood tides. The barotropic forcing dominated during high flow, resulting in lower salinity and a shorter distance of saltwater intrusion. Based on the analyzed results using stratification indices, most of the time was spent in the state of partial mixing at the Guandu Bridge and good mixing at the Taipei Bridge during high flow, while most of the time was spent in the states of partial mixing and good mixing at both Guandu Bridge and Taipei Bridge during low flow. More stratification occurred during high flow at high slack tide compared to that during low flow. The freshwater discharges from upriver reaches controlled the suspended-sediment concentration (SSC) in tidal estuaries. The higher SSC appeared downstream of the tidal estuary at ebb tide during high flow. Observations also revealed that there was an estuarine turbidity maximum at the bottom layer of Guandu Bridge.
Visualization Concept of Automotive Quality Management System Standard
Authors: Pauliková Alena
Published: June 2022

In organizations that manufacture products or provide services as well as in academic practice, the visualization of management systems’ standards is presented primarily in the form of static diagrams or tables. Demands for the quality of products and services are reflected in the complexity and interconnectedness of standards, which could cause complications during the implementation phase. This article presents a visualization of the standard IATF 16949 Automotive Quality Management System Standard, Quality management system requirements for automotive production and relevant service parts organizations, which incorporates in its content ISO 9001 Quality Management Systems—Requirements using a dynamic cluster model built using software TouchGraph Navigator. The visualization process began with a detailed examination of the two-stage requirements, documented information, and notes detailed in IATF 16949 and continued with the creation of a central node to which new nodes were subsequently connected, containing the introductory parts of the standard, including clauses. The activation of clauses and sub-clauses for other parts of the document followed. The dynamic behavior of the resulting cluster can be used in the implementation, execution, maintenance, or certification process to encourage organizations to improve their operational processes by incorporating more rigorous quality considerations into their operational frameworks.
The Community Commitment to Sustainability: Forest Protection in Guatemala
Authors: Fernández Luiña Eduardo , Fernández Ordóñez Santiago , Wang William Hongsong
Published: June 2022

This article covers the current research vacuum on how Guatemala partially conducts forest preservation through community concessions. Our paper starts its analysis by synthesizing the private property-rights approach environmentalist theory and the community concession theory. It is argued that the shared common private property as a community arrangement can turn conflicts into potential opportunities for the involved parties to solve the existing environmental problems by win-win games. Based on the above theoretical views, our study extends the scope to the modern and democratic municipals’ forest preservation in Guatemala, as previous research mainly focused on how the Guatemalan traditional indigenous communities have conducted forest preservation. Our empirical results show that the in-force forest concessions in the Maya Biosphere Reserve have achieved the Guatemalan government’s forest conservation target in recent years. However, as the Guatemalan forest concession arrangements are just usufructs and the state still owns forest titles, the current Guatemalan forest concession could reverse the result of the limited, decentralized forest reform. In this regard, we suggest that Guatemala state should privatize all these forests to the concessions’ communities and firms. If the results are positive, we propose the Guatemalan government further apply the decentralization forest policy to the whole country.
Quantitative Analysis of Temporal and Spatial Variations of Soil Salinization and Groundwater Depth along the Yellow River Saline–Alkali Land
Authors: Li Hongfang , Wang Jian , Liu Hu , Wei Zhanmin , Miao Henglu
Published: June 2022

To study the spatial distribution of soil salinization, the temporal and spatial variations of groundwater depth, along the Yellow River saline–alkali land, were analyzed. GIS technology was used to conduct spatial interpolation and quantitative analysis in Shahao, including salinization data obtained before summer irrigation, and groundwater depth data for one hydrological year from long-term observation wells. The results indicate that the groundwater depth gradually shallows from south to north, consistent with the topography of the area, and soil salinization is greater in the central and northern parts of the study area. Alkaline soils are widespread in the surface-to-middle soil, accounting for 55.2–62.5% of the total land area. A total of 28.6% of the surface layer is heavily salinized. The crop emergence rate of heavily salinized cropland is only 30–50%. The high salt content of the Yellow River itself, freeze–thaw change and poor drainage are three important factors causing the high soil salinity along the Yellow River saline–alkali land. The changes of groundwater depth are divided into autumn irrigation, the freezing period and the crop-growth period. The return of soil salt in spring caused by autumn irrigation mainly affects the uppermost soil layer (0–100 mm depth).
Assessment of Genotype Stress Tolerance as an Effective Way to Sustain Wheat Production under Salinity Stress Conditions
Authors: Matković Stojšin Mirela , Petrović Sofija , Banjac Borislav , Zečević Veselinka , Roljević Nikolić Svetlana , Majstorović Helena , Đorđević Radiša , Knežević Desimir
Published: June 2022

The creation of salt-tolerant wheat genotypes can provide a basis for sustainable wheat production in areas that are particularly sensitive to the impacts of climate change on soil salinity. This study aimed to select salt-tolerant wheat genotypes that could serve as a genetic resource in breeding for salinity tolerance. A two-year experiment was established with 27 wheat genotypes, grown in salinity stress and non-stress conditions. Agronomic parameters (plant height, spike weight, number of grains per spike, thousand grain weight, and grain yield/plant) were analyzed in the phenophase of full maturity, while biochemical parameters (DPPH radical scavenging activity and total phenolic content) were tested in four phenophases. Grain yield/plant was the most sensitive parameter to salinity, with a 31.5% reduction in value. Selection based on salt tolerance indices (STI, MP, and GMP) favored the selection of the genotypes Renesansa, Harmonija, Orašanka, Bankut 1205, KG-58, and Jugoslavija. Based on YI (1.30) and stability analysis, the genotype Harmonija stands out as the most desirable genotype for cultivation in saline conditions. The presence of positive correlations between grain yield/plant and biochemical parameters, in all phenophases, enables the selection of genotypes with high antioxidant activity and high yield potential, even in the early stages of plant development.
Inoculation with the pH Lowering Plant Growth Promoting Bacterium Bacillus sp. ZV6 Enhances Ni Phytoextraction by Salix alba from a Ni-Polluted Soil Receiving Effluents from Ni Electroplating Industry
Authors: Virk Zaheer Abbas , Al Farraj Dunia A. , Iqbal Muhammad , Lewińska Karolina , Hussain Sabir
Published: June 2022

Soil contamination with Ni poses serious ecological risks to the environment. Several members of the Salix genus have the ability to accumulate high concentrations of Ni in their aerial parts, and thus can be used for the remediation of Ni-contaminated soils. Interestingly, the efficacy of Ni phytoextraction by Salix may be improved by the acidification of rhizosphere with rhizosphere acidifying bacterial strains. Therefore, the aim of this study was to assess the efficacy of bacterial strain Bacillus sp. ZV6 in the presence of animal manure (AM) and leaf manure (LM) for enhancing the bioavailability of Ni in the rhizosphere of Salix alba via reducing the pH of rhizosphere and resultantly, enhanced phytoextraction of Ni. Inoculation of Ni-contaminated soil with strain ZV6 significantly increased plant growth as well as Ni uptake by alba. It was found that the addition of AM and LM resulted into a significant increase in plant growth and Ni uptake by alba in Ni-contaminated soil inoculated with ZV6 stain. However, the highest improvements in diethylene triamine penta-acetic acid (DTPA) extractable Ni (10%), Ni removal from soil (54%), Ni bioconcentration factor (26%) and Ni translocation factor (13%) were detected in the soil inoculated with ZV6 along with the addition of LM, compared to control. Similarly, the enhancements in microbial biomass (92%), bacterial count (348%), organic carbon (organic C) (57%) and various enzymatic activities such as urease (56%), dehydrogenase (32%), β-glucosidase (53%), peroxidase (26%) and acid phosphatase (38%) were also significantly higher in the soil inoculated with ZV6 along with the addition of LM. The findings of this study suggest that the inoculation of Ni-contaminated soils with rhizosphere acidifying bacteria can effectively improve Ni phytoextraction and, in parallel, enhance soil health.
Influencing Factors of Farmers’ Land Circulation in Mountainous Chongqing in China Based on A Multi-Class Logistic Model
Authors: Zhu Xusen , Wei Chaofu , Zhang Fengtai , Zhang Junyi , Xiao Yuedong , Yang Xingyu
Published: June 2022

The orderly circulation of land can affect the structure of the agricultural industry, improve the level of agricultural industrialization, and realize the sustainable development of agriculture. Located in the inland of southwest China, Chongqing is the core area of China’s Three Gorges Reservoir area, with obvious mountain characteristics. The characteristics and influencing factors of land transfer here can guide the reformation of land policy in other rural areas. Therefore, based on the survey data of 1015 mountain farmers in Chongqing, this paper employs a multi-class logistic model to analyze the above issues. The results show the following: (1) The phenomena of “zero rent” and “non-agreement” are widespread, and the spontaneous internal transfer among farmers is the main influencing factor. The decline in land value, the low degree of foreign investment, and the low average level of farmers’ understanding of land transfer policies are quite different from the economically developed plains in the east. (2) Different directions of land circulation have different influencing factors. (3) The main factors are the total population of rural households, the proportion of non-agricultural household income, the age of the head of the household, the education level of the head of the household, the degree of land division, and the quality of land grades that affect land transfer and development in mountainous areas. Thus, improving the education level or technological training of farmers, establishing effective market mechanisms, and increasing income from non-agricultural employment can effectively promote land transfer. Moreover, age-oriented land policy is easier to implement.
Collaborative Renewable Energy Generation among Industries: The Role of Social Identity, Awareness and Institutional Design
Authors: Eslamizadeh Sina , Ghorbani Amineh , Araghi Yashar , Weijnen Margot
Published: June 2022

Like many other sectors, climate change strategies have put various restrictions on industry, the most prominent one being caps on CO2 and other energy-related emissions. At the same time, and especially in many developing economies, the industry struggles with an increasing gap between the fast development of the sector and lagging energy supply capacity. Collective generation of renewable energy is seen as a promising means of transition, next to other forms of renewable energy generation (centralised, individual). The aim of this research is to investigate factors influencing willingness to participate in Industrial Community Energy Systems (InCES). Using existing literature on Industrial Symbiosis and Community Energy Systems, we formulate plausible hypotheses on the most relevant factors for the willingness of industries to join such initiatives. As one of the largest and most diversified industrial clusters in Iran, Arak industrial park is selected as the case study. Data were collected from the CEOs of 96 companies through survey research. Our results highlight the crucial role of awareness about the benefits of renewable power generation in an InCES. Social identity among industries and trust between them are also determining factors for their willingness to join InCES. Finally, proper institutional design for overcoming the partnership complexities (e.g., conflict resolution) was highlighted as a crucial factor for industries. It can be concluded from the results of this study that policymakers should avoid one-size-fits-all incentive design approaches and reach out to larger companies with targeted incentives, introduce specially designed bank loans for different target groups, and make use of consulting companies as intermediaries to increase the awareness of the industries regarding the benefits of investing in an InCES.