bluuebunny/crossref_metadata_2025 huggingface dataset:
Modification of Polyethersulfone Ultrafiltration Membrane Using Poly(terephthalic acid-co-glycerol-g-maleic anhydride) as Novel Pore Former
Authors: Aljanabi Ali A. Abbas , Mousa Noor Edin , Aljumaily Mustafa M. , Majdi Hasan Sh. , Yahya Ali Amer , AL-Baiati Mohammad N. , Hashim Noor , Rashid Khaild T. , Al-Saadi Saad , Alsalhy Qusay F.
Published: August 2022

In this research, poly terephthalic acid-co-glycerol-g-maleic anhydride (PTGM) graft co-polymer was used as novel water-soluble pore formers for polyethersulfone (PES) membrane modification. The modified PES membranes were characterized to monitor the effect of PTGM content on their pure water flux, hydrophilicity, porosity, morphological structure, composition, and performance. PTGM and PES/PTGM membranes were characterized by field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy (FTIR), and contact angle (CA). The results revealed that the porosity and hydrophilicity of the fabricated membrane formed using a 5 wt.% PTGM ratio exhibited an enhancement of 20% and 18%, respectively. Similarly, upon raising the PTGM ratio in the casting solution, a more porous with longer finger-like structure was observed. However, at optimum PTGM content (i.e., 5%), apparent enhancements in the water flux, bovine serum albumin (BSA), and sodium alginate (SA) retention were noticed by values of 203 L/m2.h (LMH), 94, and 96%, respectively. These results illustrated that the observed separation and permeation trend of the PES/PTGM membrane may be a suitable option for applications of wastewater treatment. The experimental results suggest the promising potential of PTGM as a pore former on the membrane properties and performance.
Production of Low Molecular Weight Chitosan Using a Combination of Weak Acid and Ultrasonication Methods
Authors: Suryani Suryani , Chaerunisaa Anis Yohana , Joni I. Made , Ruslin Ruslin , Ramadhan La Ode Ahmad Nur , Wardhana Yoga Windhu , Sabarwati Sitti Hadijah
Published: August 2022

Low molecular weight chitosan (LMWC) has higher solubility and lower viscosity allowing for a wider pharmaceutical application compared to high molecular weight chitosan. LMWC chitosan can be obtained through a chitosan depolymerization process. This research aimed to produce LWMC using the combination of formic acid and ultrasonication method with the optimal condition of the depolymerization process. The chitosan depolymerization method was performed by combining formic acid and ultrasonication. The optimum conditions of the depolymerization process were obtained using the Box–Behnken design. The LMWC obtained from depolymerization was characterized to identify its yield, degree of deacetylation, the molecular weight, structure, morphology, thermal behavior, and crystallinity index. Results: The characterization results of LWMC obtained from the depolymerization process using the optimum conditions showed that the yield was 89.398%; the degree of deacetylation was 98.076%; the molecular weight was 32.814 kDa; there was no change in the chemical structure, LWMC had disorganized shape, there was no change in the thermal behavior, and LWMC had a more amorphous shape compared to native chitosan. Conclusion: The production of LWMC involving depolymerization in the presence of weak acid and ultrasonication can be developed by using the optimal condition of the depolymerization process.
The Potential of Control Models Based on Reinforcement Learning in the Operating of Solar Thermal Cooling Systems
Authors: Diaz Juan J. , Fernández José A.
Published: August 2022

The objective of this research work was to investigate the potential of control models based on reinforcement learning in the optimization of solar thermal cooling systems (STCS) operation through a case study. In this, the performance of the installation working with a traditional predictive control approach and with a reinforcement learning (RL)-based control approach was analyzed and compared using a specific realistic simulation tool. In order to achieve the proposed objective, a control system module based on the reinforcement learning approach with the capacity for interacting with the aforementioned realistic simulation tool was developed in Python. For the studied period and the STCS operating with a control system based on RL, the following was observed: a 35% reduction in consumption of auxiliary energy, a 17% reduction in the electrical consumption of the pump that feeds the absorption machine and more precise control in the generation of cooling energy regarding the installation working under a predictive control approach. Through the obtained results, the advantages and potential of control models based on RL for the controlling and regulation of solar thermal cooling systems were verified.
Optimization of a Green Extraction of Polyphenols from Sweet Cherry (Prunus avium L.) Pulp
Authors: Clodoveo Maria Lisa , Crupi Pasquale , Corbo Filomena
Published: August 2022

This work focused on the optimization of the ultrasound (US) extraction of polyphenols from sweet cherry pulp by monitoring cyanidin-3O-rutinoside, quercetin-3O-rutinoside, and trans-3-O-coumaroylquinic acid, representing the main anthocyanin, flavonol, and hydroxycinnamate, respectively, identified in the extracts through chromatographic analyses (HPLC-DAD), as output variables. The optimization was performed following a two-level central composite design and the influence of the selected independent variables (i.e., extraction time and solid to solvent ratio) was checked through the response surface methodology. The maximum recovery of the phenolic compounds was obtained at 3 min and 0.25 g/mL in water/ethanol (1:1, v/v) at a set temperature (25 °C), sonication power (100 W), and sonication frequency (37 kHz). Subsequent validation experiments proved the effectiveness and reliability of the gathered mathematical models in defining the best ultrasound-assisted extraction conditions.
A Systematic Investigation of American Vaccination Preference via Historical Data
Authors: Chen Jason , Chen Angie , Shi Youran , Chen Kathryn , Zhao Kevin Han , Xu Morwen , He Ricky , Huang Zuyi
Published: August 2022

While COVID-19 vaccines are generally available, not all people receive vaccines. To reach herd immunity, most of a population must be vaccinated. It is, thus, important to identify factors influencing people’s vaccination preferences, as knowledge of these preferences allows for governments and health programs to increase their vaccine coverage more effectively. Fortunately, vaccination data were collected by U.S. Census Bureau in partnership with the CDC via the Household Pulse Survey (HPS) for Americans. This study presents the first analysis of the 24 vaccination datasets collected by the HPS from January 2021 to May 2022 for 250 million respondents of different ages, genders, sexual orientations, races, education statuses, marital statuses, household sizes, household income levels, and resources used for spending needs, and with different reasons for not receiving or planning to receive a vaccine. Statistical analysis techniques, including an analysis of variance (ANOVA), Tukey multiple comparisons test, and hierarchical clustering (HC), were implemented to analyze the HPS vaccination data in the R language. It was found that sexual orientation, gender, age, and education had statistically significant influences on the vaccination rates. In particular, the gay/lesbian group showed a higher vaccination rate than the straight group; the transgender group had a lower vaccination rate than either the female or the male groups; older respondents showed greater preference for vaccination; respondents with higher education levels also preferred vaccination. As for the other factors that were not significant enough to influence vaccinations in the ANOVA, notable trends were found. Asian Americans had higher vaccination rates than other races; respondents from larger household sizes had a lower chance of getting vaccinated; the unmarried group showed the lowed vaccination rate in the marital category; the respondents depending on borrowed money from the Supplemental Nutrition Assistance Program (SNAP) showed a lower vaccination rate than people with regular incomes. Concerns regarding the side-effects and the safety of the vaccines were the two major reasons for vaccination hesitance at the beginning of the pandemic, while having no trust in the vaccines and no trust in the government became more common in the later stage of the pandemic. The findings in this study can be used by governments or organizations to improve their vaccination campaigns or methods of combating future pandemics.
Phenomenology of Immanence. Doxography on the “Idea of God” (Descartes, Kant, Schelling, Levinas)
Authors: Slama Paul
Published: August 2022

This article describes the history of modern metaphysics as the history of the immanentization of transcendence. We show this from the concept of the “idea of god”, which is the phenomenon that violently separates subjectivity from transcendence and opens up a tear in it that we call “psycho-theological”: the divine violently leaves a trace in us by its very distance. We describe this phenomenon by means of a study of four archives: Descartes’ third Metaphysical Meditations (1641), the refutation of the cosmological proof of the existence of God in Kant’s Transcendental Dialectic in the Critique of Pure Reason (1781–87), Schelling’s commentary on this Kant’s text in his Introduction to the lectures on Philosophy of Revelation (1841), and the traces of Descartes’ third Meditation in the work of Levinas.
Sacramental Engines: The Trinitarian Ontology of Computers in Charles Babbage’s Analytical Engine
Authors: Haecker Ryan
Published: August 2022

Charles Babbage’s Analytical Engine can be recollected as a fossilized image of the first digital computer. It is essentially distinguished from all prior and analog computers by the transcription of the ‘mechanical notation’, the separation of the mnemonic ‘store’ from the cybernetic ‘mill’, and the infinite miniaturization of its component parts. This substitution of finite space for an accelerating singularity of time creates the essential rupture of the digital, in which a singular calculation of mechanical force stands opposed to the universal totality of space. Babbage’s criticism of Christian doctrine to preserve the mathematical consistency of mechanics and computing would result in the collapse of the Christian Trinity into a digital theology. This Arian subordinate difference of the Son to the Father would then be infinitely transcribed in a technical contradiction that would threaten to annul the metaphysical ground of any machine. Against digital and postdigital theologies alike, this rupture can only be repaired by a dialectical analysis of the digital into a hyperdigital grammar, which is created by Christ the Logos in a trinitarian ontology of computers. Digital computers can thus be vindicated from theological suspicion as incarnationally accelerated calculators of the sacraments, or ‘sacramental engines’ of the digital age.
The Right to Have Places of Worship: The Cemevi Case in Turkey
Authors: Aydin Omur , Gurpinar Bulut
Published: August 2022

This study discusses the obligation of the state to provide places of worship to religious communities in society, or to grant such existing places a specific status in law and thus entitle them to benefit from some public privileges. The study finds that international human rights law does not impose direct positive obligations on the state in this context. If, however, a state has granted such public privileges and statuses to some religious communities in the society, or has developed a concordat-type relationship with them, then it should base this differential treatment between religious communities on objective and reasonable justifications. Cemevis, which Alevis accept as their places of worship, do not have the status of a place of worship in Turkey. In the official discourse, the difference between Alevism and Sunnism is approached from a cultural, not religious, perspective. The study determines that practices of secularism in Turkey have atypical appearances in some issues. There is an implicit concordat relationship between the state and the Sunni/Hanafi community, although this is not expressed in the official discourse, and Turkish-style secularism is reluctant to formalize this relationship or to establish similar concordat-type relationships with other religious communities. Due to this preference, Alevis cannot reach the status of a recognized religious society in Turkey, and cemevis cannot be granted the status of places of worship that are entitled to benefit from public privileges.
Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV)
Authors: Wang Yilin , Yin Dongxu , Lou Liming , Li Xinying , Cheng Pengle , Huang Ying
Published: August 2022

Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation.
Assessing Surface Urban Heat Island Related to Land Use/Land Cover Composition and Pattern in the Temperate Mountain Valley City of Kathmandu, Nepal
Authors: Karunaratne Siri , Athukorala Darshana , Murayama Yuji , Morimoto Takehiro
Published: August 2022

Rapid urban growth has coincided with a substantial change in the environment, including vegetation, soil, and urban climate. The surface urban heat island (UHI) is the temperature in the lowest layers of the urban atmosphere; it is critical to the surface’s energy balance and makes it possible to determine internal climates that affect the livability of urban residents. Therefore, the surface UHI is recognized as one of the crucial global issues in the 21st century. This phenomenon affects sustainable urban planning, the health of urban residents, and the possibility of living in cities. In the context of sustainable landscapes and urban planning, more weight is given to exploring solutions for mitigating and adapting to the surface UHI effect, currently a hot topic in urban thermal environments. This study evaluated the relationship between land use/land cover (LULC) and land surface temperature (LST) formation in the temperate mountain valley city of Kathmandu, Nepal, because it is one of the megacities of South Asia, and the recent population increase has led to the rapid urbanization in the valley. Using Landsat images for 2000, 2013, and 2020, this study employed several approaches, including machine learning techniques, remote sensing (RS)-based parameter analysis, urban-rural gradient analysis, and spatial composition and pattern analysis to explore the surface UHI effect from the urban expansion and green space in the study area. The results revealed that Kathmandu’s surface UHI effect was remarkable. In 2000, the higher mean LST tended to be in the city’s core area, whereas the mean LST tended to move in the east, south, north, and west directions by 2020, which is compatible with urban expansion. Urban periphery expansion showed a continuous enlargement, and the urban core area showed a predominance of impervious surface (IS) on the basis of urban-rural gradient analysis. The city core had a lower density of green space (GS), while away from the city center, a higher density of GS predominated at the three time points, showing a lower surface UHI effect in the periphery compared to the city core area. This study reveals that landscape composition and pattern are significantly correlated with the mean LST in Kathmandu. Therefore, in discussing these findings in order to mitigate and adapt to prominent surface UHI effects, this study provides valuable information for sustainable urban planning and landscape design in mountain valley cities like Kathmandu.
Comparison of UAS and Sentinel-2 Multispectral Imagery for Water Quality Monitoring: A Case Study for Acid Mine Drainage Affected Areas (SW Spain)
Authors: Isgró Melisa A. , Basallote M. Dolores , Caballero Isabel , Barbero Luis
Published: August 2022

Uncrewed Aerial Systems (UAS) and satellites are used for monitoring and assessing the quality of surface waters. Combining both sensors in a joint tool may scale local water quality retrieval models to regional and global scales by translating UAS-based models to satellite imagery. The main objective of this study is to examine whether Sentinel-2 (S2) data can complement UAS data, specifically from the MicaSense RedEdge MX-Dual sensor, for inland water quality monitoring in mining environments affected by acid mine drainage (AMD). For this purpose, a comparison between UAS reflectance maps and atmospherically corrected S2 imagery was performed. S2 data were processed with Case 2 Regional Coast Colour (C2RCC) and Case 2 Regional Coast Colour for Complex waters (C2X) atmospheric correction (AC) processors. The correlation between the UAS data and the atmospherically corrected S2 data was evaluated on a band-by-band and a pixel-by-pixel basis, and the compatibility of the spectral data was analyzed through statistical methods. The results showed C2RCC and C2X performed better for acidic greenish-blue and non-acidic greenish-brown water bodies concerning the UAS data than for acidic dark reddish-brown waters. However, significant differences in reflectance between the UAS sensor and both S2 AC processors have been detected. The poor agreement between sensors should be considered when combining data from both instruments since these could have further consequences in developing multi-scale models.
FusionNet: A Convolution–Transformer Fusion Network for Hyperspectral Image Classification
Authors: Yang Liming , Yang Yihang , Yang Jinghui , Zhao Ningyuan , Wu Ling , Wang Liguo , Wang Tianrui
Published: August 2022

In recent years, deep-learning-based hyperspectral image (HSI) classification networks have become one of the most dominant implementations in HSI classification tasks. Among these networks, convolutional neural networks (CNNs) and attention-based networks have prevailed over other HSI classification networks. While convolutional neural networks with perceptual fields can effectively extract local features in the spatial dimension of HSI, they are poor at capturing the global and sequential features of spectral–spatial information; networks based on attention mechanisms, for example, Transformer, usually have better ability to capture global features, but are relatively weak in discriminating local features. This paper proposes a fusion network of convolution and Transformer for HSI classification, known as FusionNet, in which convolution and Transformer are fused in both serial and parallel mechanisms to achieve the full utilization of HSI features. Experimental results demonstrate that the proposed network has superior classification results compared to previous similar networks, and performs relatively well even on a small amount of training data.
A Gamma-Log Net for Oil Spill Detection in Inhomogeneous SAR Images
Authors: Liu Jundong , Ren Peng , Lyu Xinrong , Grecos Christos
Published: August 2022

Due to the complexity of ocean environments, inhomogeneous phenomenon always exist in SAR images of oil spills on the sea surface. In order to address this issue, a universal parameter adaptive Gamma-Log net for detecting oil spills in inhomogeneous SAR images is proposed in this paper. The Gamma-Log net consists of an image feature division module, a correction parameter extraction module, a Gamma-Log correction module and a feature integration module. The normalized input image features are divided into four blocks for correction in the image feature division module. According to the input characteristics, the Gamma-Log correction input parameters are obtained in the correction parameter extraction module. Subsequently, an adaptive method is introduced to adjust the parameters independently by the network to improve efficiency. Then, the input features are corrected in the Gamma-Log correction module by Gamma correction and logarithmic correction. Both correction methods can adjust the gray imbalance in the image and change the overall gray value and contrast. The separated feature blocks are finally reunited together by the feature integration module. In order to avoid information loss, an attention mechanism is added to this module. In the experiments, by adding Gamma-Log Net to multiple semantic segmentation networks, the MIoU and dice indicators increased to some extent, and the HD distance(Hausdorff-95) decreased. Our work demonstrates that the Gamma-Log net can be helpful for oil spill detection in inhomogeneous SAR images.
Multitemporal Glacier Mass Balance and Area Changes in the Puruogangri Ice Field during 1975–2021 Based on Multisource Satellite Observations
Authors: Ren Shanshan , Li Xin , Wang Yingzheng , Zheng Donghai , Jiang Decai , Nian Yanyun , Zhou Yushan
Published: August 2022

Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field has attracted a lot of attention from the scientific community. However, relevant studies that are based on satellite data have mainly focused on a few periods from 2000–2016. Long-term and multiperiod observations remain to be conducted. To this end, we estimated the changes in the glacier area and mass balance of the Puruogangri Ice Field over five subperiods between 1975 and 2021, based on multisource remote sensing data. Specifically, we employed KH-9 and Landsat images to estimate the area change from 1975 to 2021 using the band ratio method. Subsequently, based on KH-9 DEM, SRTM DEM, Copernicus DEM and ZY-3 DEM data, we evaluated the glacier elevation changes and mass balance over five subperiods during 1975–2021. The results showed that the total glacier area decreased from 427.44 ± 12.43 km2 to 387.87 ± 11.02 km2 from 1975 to 2021, with a decrease rate of 0.86 km2 a−1. The rate of area change at a decade timescale was −0.74 km2 a−1 (2000–2012) and −1.00 km2 a−1 (2012–2021). Furthermore, the rates at a multiyear timescale were −1.23 km2 a−1, −1.83 km2 a−1 and −0.42 km2 a−1 for 2012–2015, 2015–2017 and 2017–2021, respectively. In terms of the glacier mass balance, the region-wide results at a two-decade timescale were −0.23 ± 0.02 m w.e. a−1 for 1975–2000 and −0.29 ± 0.02 m w.e. a−1 for 2000–2021, indicating a sustained and relatively stable mass loss over the past nearly five decades. After 2000, the loss rate at a decade timescale was −0.04 ± 0.01 m w.e. a−1 for 2000–2012 and −0.17 ± 0.01 m w.e. a−1 for 2012–2021, indicating an increasing loss rate over recent decades. It was further found that the mass loss rate was −0.12 ± 0.02 m w.e. a−1 for 2012–2015, −0.03 ± 0.01 m w.e. a−1 for 2015–2017 and −0.40 ± 0.03 m w.e. a−1 for 2017–2021. These results indicated that a significant portion of the glacier mass loss mainly occurred after 2017. According to our analysis of the meteorological measurements in nearby regions, the trends of precipitation and the average annual air temperature both increased. Combining these findings with the results of the glacier changes implied that the glacier changes seemed to be more sensitive to temperature increase in this region. Overall, our results improved our understanding of the status of glacier changes and their reaction to climate change in the central Tibetan Plateau.
Forest Fire Effects on Landscape Snow Albedo Recovery and Decay
Authors: Gersh Max , Gleason Kelly E. , Surunis Anton
Published: August 2022

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA).
An Algorithm for Retrieving the 2-D Distribution of Moderate Rain by X-SAR
Authors: Lu Shuyuan , Xie Yanan , Wang Rui , Luo Ting , Xu Zhenbin , Yu Xueying
Published: August 2022

Synthetic aperture radar (SAR) can detect ground information with high precision, which provides another opportunity for the retrieval of rain. Rainfall intensities in East Asia are mainly moderate. The current retrieval algorithms have high accuracy in rainstorms, but they overestimate the rainfall intensity greatly in moderate rain. Therefore, it is very important to reduce the retrieval error of SAR in moderate rain. After analyzing the scattering model of precipitation, this paper proposes an algorithm for retrieving 2-D moderate rain distribution (MRA). Since the 2-D distribution of rain is related to the vertical and horizontal distributions, MRA combines the empirical regression equation with the directional model of rain rates at different levels to retrieve the vertical distribution of precipitation. Compared with the model-oriented statistical (MOS) algorithm, MRA reduces the root mean square error when retrieving the surface rain rate from 2.6 to 0.1. In addition, based on the high-precision rain parameters retrieved by MRA, the horizontal distribution is retrieved through the likelihood distance. This horizontal distribution retrieval method not only has less amount of calculation but also avoids the difficulties of mathematical analysis.
High-Resolution Daily Emission Inventory of Biomass Burning in the Amur-Heilong River Basin Based on MODIS Fire Radiative Energy Data
Authors: Lv Zhenghan , Shi Yusheng , Guo Dianfan , Zhu Yue , Man Haoran , Zhang Yang , Zang Shuying
Published: August 2022

Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude boreal forests. In this study, we used fire radiative power (FRP) obtained from a Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate OBB emissions from the ARB and established a long-term series (2003–2020) with a high spatiotemporal resolution and a daily 1 km emissions inventory. The results show that the annual average emissions of CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10 were estimated to be 153.57, 6.16, 0.21, 0.78, 0.28, 0.08, 0.06, 0.04, 0.39, 0.66, and 0.85 Tg/a, respectively. Taking CO2 as an example, grassland fire in the dry season (mainly in April and October) was the largest contributor (87.18 Tg/a), accounting for 56.77% of the total CO2 emissions from the ARB, followed by forest fire prone to occur in April–May (56.53 Tg/a, 36.81%) and crop fire during harvest season (9.86 Tg/a, 6.42%). Among the three countries in the ARB, Russia released the most total CO2 emissions (2227.04 Tg), much higher than those of China (338.41 Tg) and Mongolia (198.83 Tg). The major fire types were crop fires (40.73%) on the Chinese side and grass fires on the Russian (56.67%) and Mongolian (97.56%) sides. Over the past decade, OBB CO2 emissions have trended downward (−0.79 Tg/a) but crop burning has increased significantly (+0.81 Tg/a). Up to 83.7% of crop fires occurred in China (2010–2020), with a concentrated and southward trend. Comparisons with the Global Fire Emission Dataset (GFED4.1s), the Fire INventory from NCAR (FINNv2.2), and the Global Fire Assimilation System (GFASv1.2) showed that our newly established emission inventory was in good agreement with these three datasets in the ARB. However, this multi-year, daily 1 km high-resolution emission inventory has the advantages of detecting more small fire emissions that were overlooked by coarse-grid datasets. The methods described here can be used as an effective means of estimating greenhouse gas and aerosol emissions from biomass combustion.
Elevation Extraction from Spaceborne SAR Tomography Using Multi-Baseline COSMO-SkyMed SAR Data
Authors: Feng Lang , Muller Jan-Peter , Yu Chaoqun , Deng Chao , Zhang Jingfa
Published: August 2022

SAR tomography (TomoSAR) extends SAR interferometry (InSAR) to image a complex 3D scene with multiple scatterers within the same SAR cell. The phase calibration method and the super-resolution reconstruction method play a crucial role in 3D TomoSAR imaging from multi-baseline SAR stacks, and they both influence the accuracy of the 3D SAR tomographic imaging results. This paper presents a systematic processing method for 3D SAR tomography imaging. Moreover, with the newly released TanDEM-X 12 m DEM, this study proposes a new phase calibration method based on SAR InSAR and DEM error estimation with the super-resolution reconstruction compressive sensing (CS) method for 3D TomoSAR imaging using COSMO-SkyMed Spaceborne SAR data. The test, fieldwork, and results validation were executed at Zipingpu Dam, Dujiangyan, Sichuan, China. After processing, the 1 m resolution TomoSAR elevation extraction results were obtained. Against the terrestrial Lidar ‘truth’ data, the elevation results were shown to have an accuracy of 0.25 ± 1.04 m and a RMSE of 1.07 m in the dam area. The results and their subsequent validation demonstrate that the X band data using the CS method are not suitable for forest structure reconstruction, but are fit for purpose for the elevation extraction of manufactured facilities including buildings in the urban area.
Seasonal Variation of Stratospheric Gravity Waves in the Asian Monsoon Region Derived from COSMIC-2 Data
Authors: Qu Tao , Zhang Lifeng , Wang Yuan , Wang Xu , Guan Jiping
Published: August 2022

COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere and Climate- 2) dry temperature profile data from December 2019 to November 2021 are used to study stratospheric gravity waves (GWs) in the Asian monsoon region. The stratosphere between 20 and 50 km is divided into the lower, middle, and high layers based on the vertical distribution of the mean potential energy (Ep) and the horizontal distribution of GW Ep in these three layers, and their seasonal changes are analyzed. The source and propagating mechanism of GWs in middle latitudes in winter are revealed. The results show that GWs in the stratosphere have distinct distribution features during different seasons. The significant Ep in winter appears mainly in middle latitudes north of 30°N, whereas in summer, it appears in the low latitudes south of 30°N. There are significant areas of GW activity in both low and middle latitudes in spring and autumn, but their intensity is significantly weaker than in winter and summer. Areas with significant GWs and the seasonal variation of their intensity are accompanied by the Asian monsoon activity. In winter, there is a northward and upward propagating column for GWs above the Sichuan Basin, and in summer, there is an eastward and upward propagating column for GWs in the zonal band 15–25°N. The occurrence of GWs in northwestern China in winter is the result of the subtropical jet stream and topography. Once GWs enter the stratosphere, they are regulated by the winter stratospheric environment, and the GWs acquire a northerly component by the wind shear. The meridional wind shear in the background field is an important factor affecting the development and propagation of GWs.
Mapping Two Decades of New York State Forest Aboveground Biomass Change Using Remote Sensing
Authors: Tamiminia Haifa , Salehi Bahram , Mahdianpari Masoud , Beier Colin M. , Johnson Lucas
Published: August 2022

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State’s forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 106 Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 106 Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 106 Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale.
Quantifying the Relationship between 2D/3D Building Patterns and Land Surface Temperature: Study on the Metropolitan Shanghai
Authors: Zhou Rui , Xu Hongchao , Zhang Hao , Zhang Jie , Liu Miao , He Tianxing , Gao Jun , Li Chunlin
Published: August 2022

In the context of urban warming associated with rapid urbanization, the relationship between urban landscape patterns and land surface temperature (LST) has been paid much attention. However, few studies have comprehensively explored the effects of two/three-dimensional (2D/3D) building patterns on LST, particularly by comparing their relative contribution to the spatial variety of LST. This study adopted the ordinary least squares regression, spatial autoregression and variance partitioning methods to investigate the relationship between 2D/3D building patterns and summertime LST across 2016–2017 in Shanghai. The 2D and 3D building patterns in this study were quantified by four 2D and six 3D metrics. The results showed that: (1) During the daytime, 2D/3D building metrics had significant correlation with LST. However, 3D building patterns played a significant role in predicting LST. They explained 51.0% and 10.2% of the variance in LST, respectively. (2) The building coverage ratio, building density, mean building projection area, the standard deviation of building height, and mean building height highly correlated with LST. Specifically, the building coverage ratio was the main predictor, which was obviously positively correlated with LST. The correlation of building density and average projected area with LST was positive and significant, while the correlation of building height standard deviation and average building height with LST was negative. The increase in average height and standard deviation of buildings and the decrease in building coverage ratio, average projected area, and density of buildings, can effectively improve the urban thermal environment at the census tract level. (3) Spatial autocorrelation analysis can elaborate the spatial relationship between building patterns and LST. The findings from our research will provide important insights for urban planners and decision makers to mitigate urban heat island problems through urban planning and building design.
A Robust Underwater Multiclass Fish-School Tracking Algorithm
Authors: Liu Tao , He Shuangyan , Liu Haoyang , Gu Yanzhen , Li Peiliang
Published: August 2022

State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos or videos, underwater fish tracking is challenging due to variations in light conditions, water turbidity levels, shape deformations, and the similar appearances of fish. This article proposes a robust underwater fish-school tracking algorithm (FSTA). The FSTA is based on the tracking-by-detection paradigm. To solve the problem of low recognition accuracy in an underwater environment, we add an amendment detection module that uses prior knowledge to modify the detection result. Second, we introduce an underwater data association algorithm for aquatic non-rigid organisms that recombines representation and location information to refine the data matching process and improve the tracking results. The Resnet50-IBN network is used as a re-identification network to track fish. We introduce a triplet loss function based on a centroid to train the feature extraction network. The multiple-object tracking accuracy (MOTA) of the FSTA is 79.1% on the underwater dataset, which shows that it can achieve state-of-the-art performance in a complex real-world marine environment.
IoT Enabled Deep Learning Based Framework for Multiple Object Detection in Remote Sensing Images
Authors: Ahmed Imran , Ahmad Misbah , Chehri Abdellah , Hassan Mohammad Mehedi , Jeon Gwanggil
Published: August 2022

Advanced collaborative and communication technologies play a significant role in intelligent services and applications, including artificial intelligence, Internet of Things (IoT), remote sensing, robotics, future generation wireless, and aerial access networks. These technologies improve connectivity, energy efficiency, and quality of services of various smart city applications, particularly in transportation, monitoring, healthcare, public services, and surveillance. A large amount of data can be obtained by IoT systems and then examined by deep learning methods for various applications, e.g., object detection or recognition. However, it is a challenging and complex task in smart remote monitoring applications (aerial and drone). Nevertheless, it has gained special consideration in recent years and has performed a pivotal role in different control and monitoring applications. This article presents an IoT-enabled smart surveillance solution for multiple object detection through segmentation. In particular, we aim to provide the concept of collaborative drones, deep learning, and IoT for improving surveillance applications in smart cities. We present an artificial intelligence-based system using the deep learning based segmentation model PSPNet (Pyramid Scene Parsing Network) for segmenting multiple objects. We used an aerial drone data set, implemented data augmentation techniques, and leveraged deep transfer learning to boost the system’s performance. We investigate and analyze the performance of the segmentation paradigm with different CNN (Convolution Neural Network) based architectures. The experimental results illustrate that data augmentation enhances the system’s performance by producing good accuracy results of multiple object segmentation. The accuracy of the developed system is 92% with VGG-16 (Visual Geometry Group), 93% with ResNet-50 (Residual Neural Network), and 95% with MobileNet.
A Novel Grayscale Image Encryption Scheme Based on the Block-Level Swapping of Pixels and the Chaotic System
Authors: Hanif Muhammad , Iqbal Nadeem , Ur Rahman Fida , Khan Muhammad Adnan , Ghazal Taher M. , Abbas Sagheer , Ahmad Munir , Al Hamadi Hussam , Yeun Chan Yeob
Published: August 2022

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.
Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers
Authors: Li Chen , Bian Shijie , Wu Tongzi , Donovan Richard P. , Li Bingbing
Published: August 2022

With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through the deployment of consumer-grade electronics and a novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM) and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU).
Real Time Monitoring of Wine Vinegar Supply Chain through MOX Sensors
Authors: Genzardi Dario , Greco Giuseppe , Núñez-Carmona Estefanía , Sberveglieri Veronica
Published: August 2022

Vinegar is a fermented product that is appreciated world-wide. It can be obtained from different kinds of matrices. Specifically, it is a solution of acetic acid produced by a two stage fermentation process. The first is an alcoholic fermentation, where the sugars are converted in ethanol and lower metabolites by the yeast action, generally Saccharomyces cerevisiae. This was performed through a technique that is expanding more and more, the so-called “pied de cuve”. The second step is an acetic fermentation where acetic acid bacteria (AAB) action causes the conversion of ethanol into acetic acid. Overall, the aim of this research is to follow wine vinegar production step by step through the volatiloma analysis by metal oxide semiconductor MOX sensors developed by Nano Sensor Systems S.r.l. This work is based on wine vinegar monitored from the grape must to the formed vinegar. The monitoring lasted 4 months and the analyses were carried out with a new generation of Electronic Nose (EN) engineered by Nano Sensor Systems S.r.l., called Small Sensor Systems Plus (S3+), equipped with an array of six gas MOX sensors with different sensing layers each. In particular, real-time monitoring made it possible to follow and to differentiate each step of the vinegar production. The principal component analysis (PCA) method was the statistical multivariate analysis utilized to process the dataset obtained from the sensors. A closer look to PCA graphs affirms how the sensors were able to cluster the production steps in a chronologically correct manner.
Four Unity/Variable Gain First-Order Cascaded Voltage-Mode All-Pass Filters and Their Fully Uncoupled Quadrature Sinusoidal Oscillator Applications
Authors: Chen Hua-Pin , Wang San-Fu , Ku Yitsen , Yi Yuan-Cheng , Li Yi-Fang , Chen Yu-Hsi
Published: August 2022

This paper presents four new designs for a first-order voltage-mode (VM) all-pass filter (APF) circuit based on two single-output positive differential voltage current conveyors (DVCCs). The first two proposed VMAPFs with unity-gain, high-input (HI) impedance and low-output (LO) impedance use two DVCCs, a grounded capacitor, and a grounded resistor. The last two proposed first-order VMAPFs with HI impedance and variable-gain control are two resistors added to each of the first two VMAPFs. The last two proposed first-order VMAPFs with variable-gain control use two DVCCs, one grounded capacitor, and three grounded resistors and provide HI impedances, so that VMAPFs can be directly cascaded to obtain high-order filters without additional voltage buffers. The four implementation circuits based only on grounded passive components are particularly applicable for integrated circuits (ICs). To confirm the cascading characteristics, an application example of a fully-uncoupled quadrature sinusoidal oscillator (FQSO) is also proposed. PSpice simulation results have confirmed the feasibility of the proposed structures. VMAPF and FQSO circuits are also constructed from commercial AD8130 and AD844 ICs, and their experimentally measured time and frequency responses are compared to theoretical values. The supply voltages for both the AD8130 and AD844 ICs were ±5 V. The measured power dissipation of the proposed first-order VMAPF and second-order FQSO circuits is 0.6 W. The measured input 1-dB compression point for the four VMAPFs is about 19 dB. The measured total harmonic distortion of the four VMAPFs is less than 0.67% when the input voltage reaches 2.5 Vpp. The calculated figures of merit for the four VMAPFs are 628.2 × 103, 603.06 × 103, 516.53 × 103, and 496.42 × 103, respectively.