bluuebunny/crossref_metadata_2025 huggingface dataset:
Procainamide Charge Transfer Complexes with Chloranilic Acid and 2,3-Dichloro-5,6-dicyano-1,4-benzoquinone: Experimental and Theoretical Study
Authors: Rahman A. F. M. Motiur , Bakheit Ahmed H. , Rahman Shofiur , Mostafa Gamal A. E. , Alrabiah Haitham
Published: February 2023

The formation of charge transfer (CT) complexes between bioactive molecules and/or organic molecules is an important aspect in order to understand ‘molecule-receptor’ interactions. Here, we have synthesized two new CT complexes, procainamide-chloranilic acid (PA-ChA) and procainamide-2,3-dichloro-5,6-dicyano-1,4-benzoquinone (PA-DDQ), from electron donor procainamide (PA), electron acceptor chloranilic acid (ChA), and 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ). The structures of these two CT complexes were elucidated/characterized using FTIR, NMR, and many other spectroscopic methods. A stability study of each complex was conducted for the first time using various spectroscopic parameters (e.g., formation constant, molar extinction coefficient, ionization potential oscillator strength, dipole moment, and standard free energy). The formation of CT complexes in solution was confirmed by spectrophotometric determination. The molecular composition of each complex was determined using the spectrophotometric titration method and gave a 1:1 (donor:acceptor) ratio. In addition, the formation constant was determined using the Benesi–Hildebrand equation. To understand the noncovalent interactions of the complexes, density functional theory (DFT) calculations were performed using the ωB97XD/6-311++G(2d,p) level of theory. The DFT-computed interaction energies (ΔIEs) and the Gibbs free energies (ΔGs) were in the same order as observed experimentally. The DFT-calculated results strongly support our experimental results.
Connecting Fiscal Decentralization with Climate Change Mitigation in China: Directions for Carbon Capturing Systems
Authors: Deng Tao , Khan Mohammed Arshad , Uddin Moin , Haider Ahsanuddin
Published: February 2023

The research aims to assess the relationship between fiscal decentralization and climate change mitigation to warrant the direction for the carbon-capturing systems of China. The study estimated the results of China and applied unit root test, cointegration analysis, CS-ARDL test, and robustness analysis. The survey results highlighted a significant relationship between the fiscal decentralization index and climate change mitigation. More specifically, the budgetary decentralization index’s economic, governmental, and institutional factors play a substantial role in climate change mitigation in the short run. While governmental factors are found insignificant asein the long run, economic, institutional, and cultural factors revealed a significant connection in the Chinese setting. The results of the study are robust in both long-run and short-run perspectives. The study also presented prudential guidelines for using fiscal decentralization as an environmental tool for climate change mitigation and launching an effective system for carbon capture management from the Chinese perspective.
Supply Chain Management during a Public Health Emergency of International Concern: A Bibliometric and Content Analysis
Authors: Luo Jianli , Huang Minmin , Bai Yanhu , Li Jia
Published: February 2023

A public health emergency of international concern, such as a pandemic, disrupts the normal operation of the global supply chain, which necessitates in-depth research on supply chain management. In this paper, we used bibliometric and content analysis to provide a systematic analysis of the supply chain industry from this background. The descriptive analysis provides insights into the publication growth trajectory, in terms of the contributing authors, countries, and subject categories, which presents an intuitive display of previous research. In addition, the existing research mainly covers three dimensions of supply chain disruption, strategies, and sustainability, which can be clustered into supply chain disruption, disruption recovery, reconfiguration, digital intelligence, optimization, and sustainability. By revisiting the supply chain industry, we explored the transformation of its characteristics in the pandemic, covering themes ranging from expansion to contraction, from traditional to digital intelligence, and from fragile to sustainability, which suggests potential research directions for future studies. This contributes to the further research of supply chain management during the pandemic and provides supply chain managers with a practical approach for dealing with supply chain disruption risks and improving supply chain sustainability in this context.
Phase Equilibria Simulation of Biomaterial-Hydrogen Binary Systems Using a Simple Empirical Correlation
Authors: Faress Fardad , Pourahmad Afham , Abdollahi Seyyed Amirreza , Safari Mohammad Hossein , Mozhdeh Mozhgan , Alobaid Falah , Aghel Babak
Published: February 2023

This study proposes a simple correlation for approximating hydrogen solubility in biomaterials as a function of pressure and temperature. The pre-exponential term of the proposed model linearly relates to the pressure, whereas the exponential term is merely a function of temperature. The differential evolution (DE) optimization algorithm helps adjust three unknown coefficients of the correlation. The proposed model estimates 134 literature data points for the hydrogen solubility in biomaterials with an excellent absolute average relative deviation (AARD) of 3.02% and a coefficient of determination (R) of 0.99815. Comparing analysis justifies that the developed correlation has higher accuracy than the multilayer perceptron artificial neural network (MLP-ANN) with the same number of adjustable parameters. Comparing analysis justifies that the Arrhenius-type correlation not only needs lower computational effort, it also has higher accuracy than the PR (Peng-Robinson), PC-SAFT (perturbed-chain statistical associating fluid theory), and SRK (Soave-Redlich-Kwong) equations of state. Modeling results show that hydrogen solubility in the studied biomaterials increases with increasing temperature and pressure. Furthermore, furan and furfuryl alcohol show the maximum and minimum hydrogen absorption capacities, respectively. Such a correlation helps in understanding the biochemical–hydrogen phase equilibria which are necessary to design, optimize, and control biofuel production plants.
Hemp: An Alternative Source for Various Industries and an Emerging Tool for Functional Food and Pharmaceutical Sectors
Authors: Naeem Muhammad Yasir , Corbo Filomena , Crupi Pasquale , Clodoveo Maria Lisa
Published: February 2023

Hemp is a high-value crop that originated in Central Asia and is a historic but emerging cultivated plant. It may be grown for fiber, food, paper making, textiles, and therapeutic reasons. In the 21st century, market interest in hemp and its products has notably increased because seed portions can be utilized in the agri-food business, the woody component of the stem can be used in green buildings, the outer layer of the stems can be used in the textile industry, and the extraction of bioactive components from roots can play a vital role in the pharmacological industries. Hemp has recently been demonstrated to be a viable alternative for economies built on synthetic materials by the food, pharmaceutical, textiles, paper, building, and energy industries, among others. As a result, the goal of this study is to assemble the significant advancements in hemp, as well as to identify research gaps and research direction opportunities. The hemp plant will be provided more encouragement to be grown and be used. Many applications of hemp may be pushed to the next level for both producing a green environment and profit. A strong vision and a well-defined plan will pave the path for the discovery of new technologies and concepts.
Experimental Study of Acid Etching and Conductivity of High-Temperature-Resistant Cross-Linked Acid
Authors: Lin Hai , Hou Tengfei , Wang Fuguo , Yue Long , Liu Shiduo , Yuan Guide , Wang Guoqing , Liu Yong , Wang Qing , Zhou Fujian
Published: February 2023

Acid fracturing is one of the effective techniques for developing low-permeability carbonate reservoirs economically. With the increasing reservoir depth, the reservoir temperature and closure pressure increase, posing new challenges to the acid system. In this paper, a high-temperature-resistant cross-linked acid system is selected, which maintains a viscosity above 80 mPa·s in the temperature range of 120 °C to 140 °C and can effectively reduce acid leak-off. The acid system can not only open the reservoir and ensure the extension of the fracture, but also reduce the reaction rate between the acid and the reservoir and increase the etching distance. The rock slab acid etching and conductivity tests show that the optimum injection rate is 50 mL/min, the rock etching morphology is channel type, and the conductivity remains above 110 D·cm. However, as the acid concentration decreases, the rock slab conductivity decreases considerably, especially at 10% acid concentration, where the closure pressure rises to 15 MPa, and there is almost no conductivity. In particular, after the acid system is broken, the reacted acid can form a filter cake on the core surface, hindering further intrusion of the residue into the core and reducing reservoir damage. The study shows that high-temperature-resistant cross-linked acid systems can effectively improve the stimulation of deeply fractured carbonate reservoirs at high temperatures.
IoT Access Control Model Based on Blockchain and Trusted Execution Environment
Authors: Jiang Weijin , Li En , Zhou Wenying , Yang Ying , Luo Tiantian
Published: February 2023

With the application and popularization of the Internet of Things (IoT), while the IoT devices bring us intelligence and convenience, the privacy protection issue has gradually attracted people’s attention. Access control technology is one of the important methods to protect privacy. However, the existing IoT access control technologies have extensive problems such as coarse-grainedness, weak auditability, lack of access process control, and excessive privileges, which make the security and privacy of our IoT devices face great threats. Based on this, a blockchain-based and encrypted currency-based access control model CcBAC supported by Trusted Execution Environment (TEE) technology is proposed, which can provide fine-graininess, strong auditability, and access procedure control for the Internet of Things. In this study, the technical principle, characteristics, and research status of the control model are introduced, and the framework of the CcBAC model is expounded in detail and formally defined. Moreover, the functions in the model are described in detail, and a specific access control process in general scenarios is presented for the model. Finally, the practicability of this model is verified through theoretical analysis and experimental evaluation, which proves that this model not only enables resource owners to fully control the access to their resources, but also takes into account the fine-graininess and auditable access control.
From Change to Transformation: Living Synodality in Ministry with Young Adults
Authors: Lamont Tracey
Published: February 2023

Pope Francis is calling on Catholic ministry leaders to embrace a more synodal church, a way of listening to the fruits of the Spirit and journeying together as people of faith. Research reveals, however, that many young people have yet to experience this way of being church. This essay proposes a need to transform rather than change ministry with young people to embrace a postmodern curriculum framework rooted in a theology of synodality aimed at cultivating prophetic, transformative, communal experiences of the divine in the world.
“Building Community through Death”: Freed African Religiosity and Faith-Based Social Networks in Nineteenth-Century Salvador da Bahia, Brazil
Authors: Berktay Asligul
Published: February 2023

This article adds to existing discussions on slave religions by offering the analysis of four post-mortem testaments left behind by formerly enslaved African women in nineteenth-century Salvador da Bahia to elucidate the different roles that faith and religiosity played within their lives, communities, and understandings of self. After contextualizing these primary sources within the relevant historiography on African and African descendant religiosity in Atlantic slave societies, it focuses on the centrality of baptisms, strong pleas for forgiveness, elaborate funerary arrangements, irmandade membership, and lives spent within the precepts of Roman Catholicism present in the testaments. It also considers the realities of enslavement and the Atlantic slave trade as important factors that shaped the considerations of freed Africans when faced with the imminence of death. In a world where life was fleeting, death became a major site for community formation, for the assertion of principles, and for exercising agency. The proximity of death and the realities of Atlantic slave societies shaped libertos’ considerations of justice and honor, as well as the final rites they required for their dignified passage to the afterworld. This article concludes that Africans in the diaspora constantly managed, negotiated, and enlarged the small spaces for self-determination, and for the preservation and recreation of identities and communities, with which they were left, while they also carved out other parallel spaces for themselves. Among these, Roman Catholic-derived religious communities and affiliations, and the continuation and creative adaptation of African religious practices, were of essential importance to the identities and community formations of libertos.
The Number and Regional Distribution of Chinese Monks after the Mid-Qing Dynasty
Authors: Zhang Xuesong
Published: February 2023

The total number of ordination certificates issued between 1736 and 1739 was 340,112. Analyzing the amount and regional distribution of ordination certificates during the early Qianlong period is helpful for us in clarifying the amount and regional distribution of Chinese monks since the mid-Qing Dynasty. The total number of Buddhist monks did not change measurably during the two hundred years from Qianlong’s reign until the Republic period, remaining between 600,000 and 700,000. Although the census in the 1930s did not cover Taoist monks, as previously discussed, their number may have been similar to that during Qianlong’s reign. As a result, the number of monks (both Buddhist and Taoist) did not changed much after the mid-Qing Dynasty, despite many historical changes since the 19th century, such as population growth, the Taiping Heavenly Kingdom Movement, the promotion of education with temple property, and the warlord conflicts. The number of Buddhist monks in Northern China declined significantly from 1742 to 1936, while that in the regions along the midstream and downstream of the Yangtze River and in Southwestern China, it increased significantly. However, the geographical layout of Chinese Buddhism did not changed much, as there was neither a noticeable decline nor a noticeable revival in the number of monks and nuns.
Spiritual Care in Palliative Care
Authors: Best Megan C. , Vivat Bella , Gijsberts Marie-Jose
Published: February 2023

Palliative care has always included spiritual care, but the provision and inclusion of spiritual care within and across palliative care services internationally is sub-optimal. In this summary overview, we address understandings and meanings of spirituality and related terms, both generally and in the context of healthcare, and outline the importance of spiritual well-being and spiritual care at the end of life. We summarise what spiritual care involves, its benefits for palliative care patients and their families, and consider how its provision might be helped or hindered. There is currently a limited evidence base for the efficacy of interventions including spiritual and/or religious care, and large-scale studies in particular are lacking. However, those mostly small-scale and/or qualitative studies which have been conducted to date show that addressing the spiritual needs of patients in palliative care is associated with many positive outcomes for both patients and their relatives. More research in this area is necessary to develop and enhance the evidence base, and optimal provision of spiritual care requires that providers explicitly recognise the need for such care, including through providing training and support for staff.
The Relationship between Religion, Substance Misuse, and Mental Health among Black Youth
Authors: Quinn Camille R. , Waller Bernadine , Hughley Ashura , Boyd Donte , Cobb Ryon , Hardy Kimberly , Radney Angelise , Voisin Dexter R.
Published: February 2023

Studies suggest that religion is a protective factor for substance misuse and mental health concerns among Black/African American youth despite reported declines in their religious involvement. However, few studies have investigated the associations among religion, substance misuse, and mental health among Black youth. Informed by Critical Race Theory, we evaluated the correlations between gender, depression, substance misuse, and unprotected sex on mental health. Using multiple linear regression, we assessed self-reported measures of drug use and sex, condom use, belief in God, and religiosity on mental health among a sample of Black youth (N = 638) living in a large midwestern city. Results indicated drug use, and sex while on drugs and alcohol, were significant and positively associated with mental health symptoms. Belief in God was negatively associated with having sex while on drugs and alcohol. The study’s findings suggest that despite the many structural inequalities that Black youth face, religion continues to be protective for Black youth against a myriad of prevalent problem behaviors.
“On Enlightenment in Religion”—Skepticism and Tolerance in Educational and Cultural Concepts within the Berlin and Breslau Haskalah
Authors: Lohmann Uta
Published: February 2023

Numerous discussions on religion were held within the communicative network among Jewish enlighteners in Berlin and Breslau. These discussions were characterized by a hitherto unknown form of skeptical and critical questioning of religious customs and practices of Ashkenazi Jewry. Moreover, they were characterized by an unprecedented skeptical questioning of religious customs and traditions of Ashkenazi Judaism. The places of these discussions were located where many different people gathered and contributed in their verbal exchanges to their mutual understanding. The experience of different opinions became the starting point for a self-reflective comparative review process of their own religious positioning and to their own stand on questions of an individual’s education and development. These oral discourses in many ways found expression in written statements, as in introductions to German translations of Biblical books and liturgical texts, in school programs, journals and modern sermons.
General Five-Component Scattering Power Decomposition with Unitary Transformation (G5U) of Coherency Matrix
Authors: Malik Rashmi , Singh Gulab , Dikshit Onkar , Yamaguchi Yoshio
Published: February 2023

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc.
Study on the Backscatter Differential Phase Characteristics of X-Band Dual-Polarization Radar and its Processing Methods
Authors: Geng Fei , Liu Liping
Published: February 2023

The differential propagation phase (ΦDP) of X-band dual-polarization weather radar (including X-band dual-polarization phased-array weather radar, X-PAR) is important for estimating precipitation and classifying hydrometeors. However, the measured differential propagation phase contains the backscatter differential phase (δ), which poses difficulties for the application of the differential propagation phase from X-band radars. This paper presents the following: (1) the simulation and characteristics analysis of the backscatter differential phase based on disdrometer DSD (raindrop size distribution) measurement data; (2) an improved method of the specific differential propagation phase (KDP) estimation based on linear programming and backscatter differential phase elimination; (3) the effect of backscatter differential phase elimination on the specific differential propagation phase estimation of X-PAR. The results show the following: (1) For X-band weather radar, the raindrop equivalent diameters D > 2 mm may cause a backscatter differential phase between 0 and 20°; in particular, the backscatter differential phase varies sharply with raindrop size between 3.2 and 4.5 mm. (2) Using linear programming or smoothing filters to process the differential propagation phase could suppress the backscatter differential phase, but it is hard to completely eliminate the effect of the backscatter differential phase. (3) Backscatter differential phase correction may improve the calculation accuracy of the specific differential propagation phase, and the optimization was verified by the improved self-consistency of polarimetric variables, correlation between specific differential propagation phase estimations from S- and X-band radar and the accuracy of quantitative precipitation estimation. The X-PAR deployed in Shenzhen showed good observation performance and the potential to be used in radar mosaics with S-band weather radar.
Spatio-Temporal Variations in Soil pH and Aluminum Toxicity in Sub-Saharan African Croplands (1980–2050)
Authors: Uwiragiye Yves , Khalaf Qahtan Abdul Wahid , Ali Hayssam M. , Ngaba Mbezele Junior Yannick , Yang Mingxia , Elrys Ahmed S. , Chen Zhujun , Zhou Jianbin
Published: February 2023

Soil acidity threatens food production in the tropics. The effect of increasing ammonium-based fertilizer (INF) on soil pH was assessed in sub-Saharan Africa (SSA). A total of 9043 soil data from Africa soil information services, past INF use, and two future scenarios of INF use (business as usual (BAU) and equitable diet (EqD)) were used to determine soil pH variations from 1980 to 2022 and to predict soil PH variations from 2022 to 2050. Random forest and extreme gradient boosting algorithms and soil-forming factor covariates were used for the spatio-temporal soil pH predictions. Topsoil acidification was shown to be significant, with mean annual decrements of 0.014, 0.024, and 0.048 from 1980 to 2022, 2022 to 2050 (BAU), and 2022 to 2050 (EqD), respectively. Over the past 42 years, croplands with soil pH < 6.5 have declined significantly, and soil acidification is predicted to become severe by 2050 in the BAU and EqD scenarios. This was indicated by a predicted 3% increase in croplands at risk of aluminum toxicity (soil pH < 5.5) from 66 × 106 ha in 2022 to 78.5 × 106 ha in 2050. The drivers of the spatial variations in the soil pH between 1980 and 2050 were the MAP, basic cation, clay content, SOC, and nitrogen fertilizers. The evaluation metrics of the 10-fold cross-validation showed that the root mean squared errors (RMSEs) of the soil pH from 1980 to 2022, as well as the predicted soil PH from 2022 to 2050 (BAU) and 2022 to 2050 (EqD), were 0.53 pH units, 0.54 pH units, and 0.56 pH units, respectively, with coefficients of determination (R2) of 0.63, 0.64, and 0.66. The findings of this study can be used for the establishment of management strategies for increasing INF use in acidic soils.
AOD Derivation from SDGSAT-1/GLI Dataset in Mega-City Area
Authors: Wang Ning , Hu Yonghong , Li Xiao Ming , Kang Chuanli , Yan Lin
Published: February 2023

Aerosol optical depth (AOD) is the key parameter for determining the aerosol radiative effects and air quality variation. It is important to quantify nighttime aerosols using satellite-based night light images to understand their diurnal variations. This study selected high-resolution low light images from the Glimmer Imager (GLI) aboard the SDGSAT-1 satellite to examine spatial–temporal changes in night light emitted from the urban surface of Beijing. The radiance observed by SDGSAT-1/GLI was used to discern the AOD changes using the radiance background method (RB) and standard deviation method (SD) based on the characterization of the radiance from artificial light sources. Cloud cleaning processes were conducted to reduce the influence of the cloud cover in the glimmer images of the derived AOD. The results showed that there are good consistencies between the derived AOD results from the remote sensing and nighttime site observations. The radiance background method is better than the standard deviation method for deriving AOD using SDGSAT-1/GLI with the RMSE of its RB (0.0984) being greater than that of the SD (0.7653). The influence of moonlight, atmospheric absorption, and positioning errors on the results is briefly discussed. This paper shows that SDGSAT-1 can obtain relatively reliable night AOD values based on our investigations using the available satellite images taken in winter and spring, and that it has the potential to provide the scientific products of nighttime AOD.
A Decade from the Costa Concordia Shipwreck: Lesson Learned on the Contribution of Infrared Thermography during the Maritime Salvage Operations
Authors: Frodella William , Rossi Guglielmo , Tanteri Luca , Rosi Ascanio , Lombardi Luca , Mugnai Francesco , Fanti Riccardo , Casagli Nicola
Published: February 2023

On 13 January 2012, the Italian vessel Costa Concordia wrecked on the shore of Giglio Island, about 15 km off the coast of southern Tuscany (Italy), causing the loss of 32 lives. It is considered one of the worst disasters in maritime history. Salvage operations started immediately after the wreck with the coordination of the Italian National Civil Protection Department and the technological support of several Research Centers, which were activated for the management of the consequent emergency phase. A multi-parametric and multiplatform monitoring system was promptly implemented, involving several advanced remote sensing techniques, among which was Infrared Thermography (IRT). In this framework, IRT monitoring was performed during a 35-day period (25 January–1 March 2012), using a terrestrial, hand-held thermal camera. Six different thermal images were acquired daily from the island’s coastline in three different positions, both in daylight and night times. The aim was to detect thermal anomalies connected to possible deformations of the vessel and oil spills. Between 3–4 February, IRT successfully revealed on oil spill drifting from the stern of the wreck towards the island harbor. Furthermore, the wreck’s thermal dilatation was also analyzed during a 24-day close-range monitoring, providing interesting insights for the interpretation of the deformation monitoring results. This paper presents the outcomes of these innovative and experimental monitoring activities, with the aim of testing the potential of IRT as a versatile and operative tool to be used in maritime and environmental disaster response.
VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO
Authors: Yost Christopher R. , Minnis Patrick , Sun-Mack Sunny , Smith William L. , Trepte Qing Z.
Published: February 2023

The decades-long Clouds and Earth’s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed.
Oriented Object Detection in Aerial Images Based on the Scaled Smooth L1 Loss Function
Authors: Wei Linhai , Zheng Chen , Hu Yijun
Published: February 2023

Although many state-of-the-art object detectors have been developed, detecting small and densely packed objects with complicated orientations in remote sensing aerial images remains challenging. For object detection in remote sensing aerial images, different scales, sizes, appearances, and orientations of objects from different categories could most likely enlarge the variance in the detection error. Undoubtedly, the variance in the detection error should have a non-negligible impact on the detection performance. Motivated by the above consideration, in this paper, we tackled this issue, so that we could improve the detection performance and reduce the impact of this variance on the detection performance as much as possible. By proposing a scaled smooth L1 loss function, we developed a new two-stage object detector for remote sensing aerial images, named Faster R-CNN-NeXt with RoI-Transformer. The proposed scaled smooth L1 loss function is used for bounding box regression and makes regression invariant to scale. This property ensures that the bounding box regression is more reliable in detecting small and densely packed objects with complicated orientations and backgrounds, leading to improved detection performance. To learn rotated bounding boxes and produce more accurate object locations, a RoI-Transformer module is employed. This is necessary because horizontal bounding boxes are inadequate for aerial image detection. The ResNeXt backbone is also adopted for the proposed object detector. Experimental results on two popular datasets, DOTA and HRSC2016, show that the variance in the detection error significantly affects detection performance. The proposed object detector is effective and robust, with the optimal scale factor for the scaled smooth L1 loss function being around 2.0. Compared to other promising two-stage oriented methods, our method achieves a mAP of 70.82 on DOTA, with an improvement of at least 1.26 and up to 16.49. On HRSC2016, our method achieves an mAP of 87.1, with an improvement of at least 0.9 and up to 1.4.
IFormerFusion: Cross-Domain Frequency Information Learning for Infrared and Visible Image Fusion Based on the Inception Transformer
Authors: Xiong Zhang , Zhang Xiaohui , Hu Qingping , Han Hongwei
Published: February 2023

The current deep learning-based image fusion methods can not sufficiently learn the features of images in a wide frequency range. Therefore, we proposed IFormerFusion, which is based on the Inception Transformer and cross-domain frequency fusion. To learn features from high- and low-frequency information, we designed the IFormer mixer, which splits the input features through the channel dimension and feeds them into parallel paths for high- and low-frequency mixers to achieve linear computational complexity. The high-frequency mixer adopts a convolution and a max-pooling path, while the low-frequency mixer adopts a criss-cross attention path. Considering that the high-frequency information relates to the texture detail, we designed a cross-domain frequency fusion strategy, which trades high-frequency information between the source images. This structure can sufficiently integrate complementary features and strengthen the capability of texture retaining. Experiments on the TNO, OSU, and Road Scene datasets demonstrate that IFormerFusion outperforms other methods in object and subject evaluations.
Unveiling the Subsurface of Late Amazonian Lava Flows at Echus Chasma, on Mars
Authors: Mansilla Federico , Zorzano María-Paz , Giannakis Iraklis , Ruiz Javier
Published: February 2023

The Echus-Kasei region on Mars has been exposed to different episodic volcanic, fluvial, and glacial events in Amazonian time. The goal of the present work is to demonstrate the usefulness of radar instruments to find preserved late Amazonian subsurface structures that may have been encapsulated underneath recent lava flows on Mars. We have analysed 27 radar observations of the SHAllow RADar (SHARAD) instrument on board the Mars Reconnaissance Orbiter (MRO), over the region of Echus Chasma. We discovered the presence of subsurface reflectors in five consecutive SHARAD radargrams at a depth from 35 to 79 m beneath the structure of a lava fan that formed about 59 ± 4 Ma ago. Some vents are preserved above the surface of this lava flow, which stands at a height of 80 m above the surrounding surface. A few kilometres to the north, we find other subsurface reflectors at a depth of about 30 m and a long pit chain formed by the collapse of a lava tube. These kinds of subsurface late Amazonian structures are of interest for astrobiology because they date from the last period when the planet still experienced intense volcanic activity over regions that were previously extensively covered by water.
Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data
Authors: Cai Yiheng , Wan Fuxing , Lang Shinan , Cui Xiangbin , Yao Zijun
Published: February 2023

Bed topography and roughness play important roles in numerous ice-sheet analyses. Although the coverage of ice-penetrating radar measurements has vastly increased over recent decades, significant data gaps remain in certain areas of subglacial topography and need interpolation. However, the bed topography generated by interpolation such as kriging and mass conservation is generally smooth at small scales, lacking topographic features important for sub-kilometer roughness. DeepBedMap, a deep learning method combined with multiple surface observation inputs, can generate high-resolution (250 m) bed topography with realistic bed roughness but produces some unrealistic artifacts and higher bed elevation values in certain regions, which could bias ice-sheet models. To address these issues, we present MB_DeepBedMap, a multi-branch deep learning method to generate more realistic bed topography. The model improves upon DeepBedMap by separating inputs into two groups using a multi-branch network structure according to their characteristics, rather than fusing all inputs at an early stage, to reduce artifacts in the generated topography caused by earlier fusion of inputs. A direct upsampling branch preserves large-scale subglacial landforms while generating high-resolution bed topography. We use MB_DeepBedMap to generate a high-resolution (250 m) bed elevation grid product of Antarctica, MB_DeepBedMap_DEM, which can be used in high-resolution ice-sheet modeling studies. Moreover, we test the performance of MB_DeepBedMap model in Thwaites Glacier, Gamburtsev Subglacial Mountains, and several other regions, by comparing the qualitative topographic features and quantitative errors of MB_DeepBedMap, BEDMAP2, BedMachine Antarctica, and DeepBedMap. The results show that MB_DeepBedMap can provide more realistic small-scale topographic features and roughness compared to BEDMAP2, BedMachine Antarctica, and DeepBedMap.
Validation of a New Scintillating Fiber Dosimeter for Radiation Dose Quality Control in Computed Tomography
Authors: Guillochon Nicolas , Balde Mamoutou , Popotte Christian , Pondard Selena , Desport Corentin , Kien Nicolas , Carbillet Fanny , Moreno Ramiro , Munier Mélodie
Published: February 2023

(1) Background: The IVIscan is a commercially available scintillating fiber detector designed for quality assurance and in vivo dosimetry in computed tomography (CT). In this work, we investigated the performance of the IVIscan scintillator and associated method in a wide range of beam width from three CT manufacturers and compared it to a CT chamber designed for Computed Tomography Dose Index (CTDI) measurements. (2) Methods: We measured weighted CTDI (CTDIw) with each detector in accordance with the requirements of regulatory tests and international recommendations for the minimum, maximum and the most used beam width in clinic and investigated the accuracy of the IVIscan system based on the assessment of the CTDIw deviation from the CT chamber. We also investigated the IVIscan accuracy for the whole range of the CT scans kV. (3) Results: We found excellent agreement between the IVIscan scintillator and the CT chamber for the whole range of beam widths and kV, especially for wide beams used on recent technology of CT scans. (4) Conclusions: These findings highlight that the IVIscan scintillator is a relevant detector for CT radiation dose assessments, and the method associated with calculating the CTDIw saves a significant amount of time and effort when performing tests, especially with regard to new CT technologies.
3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference
Authors: Park Minseuk , Memon Sufyan Ali , Kim Geunhwan , Choo Youngmin
Published: February 2023

The propeller tip vortex cavitation (TVC) localization problem involves the separation of noise sources in proximity. This work describes a sparse localization method for off-grid cavitations to estimates their precise locations while keeping reasonable computational efficiency. It adopts two different grid (pairwise off-grid) sets with a moderate grid interval and provides redundant representations for adjacent noise sources. To estimate the position of the off-grid cavitations, a block-sparse Bayesian learning-based method is adopted for the pairwise off-grid scheme (pairwise off-grid BSBL), which iteratively updates the grid points using Bayesian inference. Subsequently, simulation and experimental results demonstrate that the proposed method achieves the separation of adjacent off-grid cavitations with reduced computational cost, while the other scheme suffers from a heavy computational burden; for the separation of adjacent off-grid cavitations, the pairwise off-grid BSBL took significantly less time (29 s) compared with the time taken by the conventional off-grid BSBL (2923 s).
A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems
Authors: Liu Qingli , Li Yangyang , Sun Jiaxu
Published: February 2023

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the “beam squint” effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the “beam squint” effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.
Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis
Authors: Mahmud Bahar Uddin , Hong Guan Yue , Mamun Abdullah Al , Ping Em Poh , Wu Qingliu
Published: February 2023

As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.
Role of Connectors in Corporate Fraud and Corruptions in Era of Circular Economy
Authors: Nagnonhou Salomon Ricardo Bignon , Imoniana Joshua Onome , Reginato Luciane , Silva Washington Lopes
Published: February 2023

This paper examines the role of connectors in a fraud and corruption-prone environment of the circular economy. A qualitative approach and critical perspective were adopted. In the same line of thought, this study drew on narratives of fraud experiences from the Criminalistica Symposium between 2017 and 2022, as well as interviews with forensic accountants, auditing experts, and police investigators, to foment discourse analysis derived from Machiavellian theory. Thematic coding was selected for the presentation of results. Our findings lead us to the conclusion that there are five identified connector roles in an organization. The connectors have the role of mainly influencing acts of corporate fraud and corruption, which have the characteristics of a double-edged sword. Connectors serve as team motivators and sometimes as manipulators, and they also have the role of spearheading negotiations in tumultuous times of employee disorder and employers’ disagreements, almost leading to uneasy truces. Additionally, in a bid to perpetrate fraud, the connector assists in the role of overriding controls. Inasmuch as it bears the trait of acting as an agent of knowledge building by facilitating learning and communication in organizations in a CE environment, this study contributes to the literature showing various roles of connectors in fraud schemes. Finally, bearing the originality, understanding the role of connectors in circular economy fraud and corruption schemes clarifies our path to expected wellbeing in the concept of sustainability in our ways of life in the coming decades attuned to policymakers and regulators.
COVID-19 Pandemic and the Crisis of Care: Wellness Discourses, Neoliberal Self-Care, and (Dis)Infodemic
Authors: Rahbari Ladan
Published: February 2023

The COVID-19 pandemic resulted in an influx of misinformation surrounding the virus and its origins. This paper examines the negative consequences of neoliberal self-care discourses related to COVID-19 that contributed to the disinfodemic, focusing on the wellness industry. Some health gurus and wellness instructors promoted lifestyle adjustments and self-care to prevent contracting the virus and sometimes blamed COVID-19 patients for contracting it. These claims contributed to vaccine hesitancy and pseudo-scientific beliefs. The misinformation was attractive partly because it promised individual control and independence from mainstream (scientific) advice. These discourses existed before the pandemic but were amplified within the wellness community after the COVID-19 pandemic. The paper concludes by exploring the feminist notions of care and solidarity as a counter to the neoliberal notion of self-care.
Using Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation
Authors: Salehi Reza , Krishnan Santhana , Nasrullah Mohd , Chaiprapat Sumate
Published: February 2023

This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures—obtained in the production of xylitol—by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system based on grid partitioning of the input space and a boosted regression tree were developed, validated, and tested. The models’ inputs were cross-flow velocity, transmembrane pressure, and filtration time, whereas the membrane permeability (called membrane flux) and xylitol concentration were considered as the outputs. According to the results, the boosted regression tree model demonstrated the highest predictive performance in forecasting the membrane flux and the amount of xylitol produced with a coefficient of determination of 0.994 and 0.967, respectively, against 0.985 and 0.946 for the grid partitioning-based adaptive neuro-fuzzy inference system, 0.865 and 0.820 for the best nonlinear regression picked from among 143 different equations, and 0.815 and 0.752 for the linear regression. The boosted regression tree modeling approach demonstrated a superior capability of predictive accuracy of the critical separation performances in the enzymatic-based cross-flow ultrafiltration membrane for xylitol synthesis.