bluuebunny/crossref_metadata_2025 huggingface dataset:
The Phase Space Density Evolution of Radiation Belt Electrons under the Action of Solar Wind Dynamic Pressure
Authors: Hu Peng , Li Haimeng , Ouyang Zhihai , Tang Rongxin , Song Liangjin , Yuan An , Feng Bopu , Wang Yangyang , Zou Wenqian
Published: February 2023

Earth’s radiation belt and ring current are donut-shaped regions of energetic and relativistic particles, trapped by the geomagnetic field. The strengthened solar wind dynamic pressure (Pdyn) can alter the structure of the geomagnetic field, which can bring about the dynamic variation of radiation belt and ring current. In the study, we firstly utilize group test particle simulations to investigate the phase space density (PSD) under the varying geomagnetic field modeled by the International Geomagnetic Reference Field (IGRF) and T96 magnetic field models from 19 December 2015 to 20 December 2015. Combining the observation of the Van Allen Probe, we find that the PSD of outer radiation belt electrons evolves towards different states under different levels of Pdyn. In the first stage, the Pdyn (~7.94 nPa) results in the obvious rise of electron anisotropy. In the second stage, there is a significant reduction in PSD for energetic electrons at all energy levels and pitch angles under the action of intense Pdyn (~22 nPa), which suggests that the magnetopause shadowing and outward radial diffusion play important roles in the second process. The result of the study can help us further understand the dynamic evolution of the radiation belt and ring current during a period of geomagnetic disturbance.
Improved Vibration Suppression Strategy of Fuzzy PI Servo Control for Dual Flexible System with Flexible Joints
Authors: Liu Xiangchen , Wang Minghai , Wang Yihan
Published: February 2023

A high performance manipulator servo drive system is a double flexible system with flexible joints and flexible loads. Flexible joints are composed of elastic connecting elements, and flexible loads are flexible Euler beams with elastic deformation. The dual flexible system has highly nonlinear time-varying characteristics. This kind of characteristic will cause resonance of the double-flexible system and affect the dynamic characteristics of the system. In order to suppress the system resonance, the nonlinear dynamics model of the system with two flexible bodies is established. Then, the servo control method of double flexible body system is designed, and the range of PI controller parameters is determined by the same resistance pole assignment method. Then, a fuzzy control rule is designed to dynamically adjust PI controller parameters based on pole assignment. Finally, the improved fuzzy PI control strategy is simulated numerically. The simulation results show that the vibration of the double-flexible system can be effectively suppressed by establishing the precise dynamic model and designing PI controller parameters.
Mean-Field and Anticipated BSDEs with Time-Delayed Generator
Authors: Zhang Pei , Mohamed Nur Anisah , Ibrahim Adriana Irawati Nur
Published: February 2023

In this paper, we discuss a new type of mean-field anticipated backward stochastic differential equation with a time-delayed generator (MF-DABSDEs) which extends the results of the anticipated backward stochastic differential equation to the case of mean-field limits, and in which the generator considers not only the present and future times but also the past time. By using the fixed point theorem, we shall demonstrate the existence and uniqueness of the solutions to these equations. Finally, we shall establish a comparison theorem for the solutions.
Multimodal Movie Recommendation System Using Deep Learning
Authors: Mu Yongheng , Wu Yun
Published: February 2023

Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis.
Exponential Stability of Nonlinear Time-Varying Delay Differential Equations via Lyapunov–Razumikhin Technique
Authors: Sedova Natalya O. , Druzhinina Olga V.
Published: February 2023

In this article, some new sufficient conditions for the exponential stability of nonlinear time-varying delay differential equations are given. An extension of the classical asymptotical stability theorem in terms of a Lyapunov–Razumikhin function is obtained. The condition of non-positivity of the time derivative of a Razumikhin function is weakened. Additionally, the resulting sufficient asymptotic stability conditions allow us to guarantee uniform exponential stability and evaluate the exponential convergence rate of the system solutions. The effectiveness of the results is demonstrated by some examples.
IG-LSPIA: Least Squares Progressive Iterative Approximation for Isogeometric Collocation Method
Authors: Jiang Yini , Lin Hongwei
Published: February 2023

The isogeometric collocation method (IGA-C), which is a promising branch of isogeometric analysis (IGA), can be considered fitting the load function with the combination of the numerical solution and its derivatives. In this study, we develop an iterative method, isogeometric least-squares progressive-iterative approximation (IG-LSPIA), to solve the fitting problem in the collocation method. IG-LSPIA starts with an initial blending function, where the control coefficients are combined with the B-spline basis functions and their derivatives. A new blending function is generated by constructing the differences for collocation points (DCP) and control coefficients (DCC), and then adding the DCC to the corresponding control coefficients. The procedure is performed iteratively until the stop criterion is reached. We prove the convergence of IG-LSPIA and show that the computation complexity in each iteration of IG-LSPIA is related only to the number of collocation points and unrelated to the number of control coefficients. Moreover, an incremental algorithm is designed; it alternates with knot refinement until the desired precision is achieved. After each knot refinement, the result of the last round of IG-LSPIA iterations is used to generate the initial blending function of the new round of iteration, thereby saving great computation. Experiments show that the proposed method is stable and efficient. In the three-dimensional case, the total computation time is saved twice compared to the traditional method.
Stability Analysis of Caputo Fractional Order Viral Dynamics of Hepatitis B Cellular Infection
Authors: Opoku Michael O. , Wiah Eric N. , Okyere Eric , Sackitey Albert L. , Essel Emmanuel K. , Moore Stephen E.
Published: February 2023

We present a Caputo fractional order mathematical model that describes the cellular infection of the Hepatitis B virus and the immune response of the body with Holling type II functional response. We study the existence of unique positive solutions and the local and global stability of virus-free and endemic equilibria. Finally, we present numerical results using the Adam-type predictor–corrector iterative scheme.
Dynamic Changes in Serum Cytokine Profile in Rats with Severe Acute Pancreatitis
Authors: Zhou Rui , Bu Wangjun , Fan Yudan , Du Ziwei , Zhang Jian , Zhang Shu , Sun Jin , Li Zongfang , Li Jun
Published: February 2023

Background and Objectives: Most published research has only investigated a single timepoint after the onset of severe acute pancreatitis (SAP), meaning that they have been unable to observe the relationship between the dynamic changes in cytokines and SAP progression. In this study, we attempted to reveal the relationship between dynamic changes in cytokine expression levels and SAP disease progression and the relationship between cytokines, using continuous large-scale cytokine detection. Materials and Methods: Seventy rats were randomly assigned to control (Con), sham operation (SO) and SAP groups. The SAP group was randomly allocated to five subgroups at 3, 6, 9, 12 and 15 h after the operation. In the SAP group, 5% sodium taurocholate was injected retrograde into the pancreatic bile duct. Animals in the SO group received a similar incision, a turning over of the pancreas. Control animals did not receive any treatment. We observed the survival, ascites fluid amount, pancreatic histopathological scores and serum amylase activity of SAP rats. We used the cytokine microarray to simultaneously detect 90 cytokines and the dynamic changes in one experiment and to analyze the correlation between cytokine expression and disease progression. Results: The mortality of SAP rats increased with an increase in time. Serum amylase activity, pancreatic histopathological scores and ascites fluid amount were time-dependent. Compared with normal rats, 69 cytokines in SAP rats were significantly changed for at least one timepoint, and 49 cytokines were significantly changed at different timepoints after SAP induction. The changes in inflammatory cytokines were significantly upregulated at 6 and 9 h and 12 h and then significantly decreased. Conclusions: The trend of cytokine expression in SAP rats was not consistent with the disease progression. The cytokine–cytokine receptor interaction and MAPK signal’s dominant cytokines were always highly expressed at various time points over the course of SAP.
Long-Term Response of Pembrolizumab in a Patient with Metastatic Squamous Non-Small Cell Lung Cancer on Hemodialysis: Case Report and Review of the Literature
Authors: Yun Jae Won , Kwon Jung , Lim Taekyu
Published: February 2023

In patients with renal failure and hemodialysis, there are difficulties in drug selection and dose adjustment for cancer treatment. The use of immune checkpoint inhibitors (ICIs), including pembrolizumab, approved by the U.S. Food and Drug Administration (FDA) for patients with metastatic non-small cell lung cancer (NSCLC) in 2015, has become an important option for the treatment of metastatic NSCLC. However, data regarding the dosage and schedule for long-term use of ICIs, especially pembrolizumab, in hemodialysis patients are limited. We present the case of a patient with metastatic squamous NSCLC who demonstrated a long-term partial response to pembrolizumab monotherapy for 45 months during hemodialysis and showed no immune-related adverse events (irAEs). To our knowledge, this is the longest remission to be reported without irAEs after discontinuation of pembrolizumab in a NSCLC patient undergoing HD. In addition, we reviewed previously reported lung cancer patients who used ICI during dialysis, comparing them with our case in clinical aspect. We believe that this report will provide clinical insights into the long-term efficacy and safety of pembrolizumab in lung cancer patients undergoing hemodialysis.
Exploring the Radioprotective Indium (III) Oxide Screens for Mammography Scans Using a Three-Layer Heterogeneous Breast Phantom and MCNPX: A Comparative Study Using Clinical Findings
Authors: ALMisned Ghada , Elshami Wiam , Kilic Gokhan , Ilik Erkan , Rabaa Elaf , Zakaly Hesham M. H. , Ene Antoaneta , Tekin Huseyin O.
Published: February 2023

Background: During mammography, a lead-acrylic protective screen is recommended to reduce radiation exposure to the unexposed breast. Objectives: This research study aimed to construct an Indium-(III)-oxide-rich tellurite-glass screen (TZI8) and compare its performance to that of lead acrylic. Materials and Methods: A three-layer heterogeneous-breast phantom was developed, using the MCNPX (version 2.7.0) Monte Carlo code. An MCNPX-simulation geometry was designed and implemented, using the lead-acrylic and TZI8 shielding screens between the right and left breast. Next, the reliability of the phantom and the variations in absorption between the lead-acrylic and TZI8 glass were investigated. Results: The findings show that the TZI8-protective-glass screen offers significantly greater radioprotection than the lead-acrylic material. The quantity of total dose absorbed in the unexposed breast was much lower for TZI8 than for lead-based acrylic. The TZI8-glass screen gives about 60% more radioprotection than the lead-acrylic screen. Conclusion: Considering the toxic lead in the structure that may be hazardous to the human tissues, the TZI8-glass screen may be used in mammography examination to provide greater radioprotection than the lead-acrylic screen, in order to greatly reduce the dose to the unexposed breast.
Recurrent Exacerbations and Evolution into Polymyositis in a Patient with Interstitial Pneumonia with Autoimmune Features: A Case Report and Literature Review
Authors: Huang Chien-Tzu , Ou Tsan-Teng , Hsu Jui-Sheng , Cheng Chih-Hung , Sheu Chau-Chyun
Published: February 2023

Interstitial pneumonia with autoimmune features (IPAF) is a new disease entity proposed in 2015. Numerous questions regarding IPAF require clarification, including diagnostic criteria, standard managements for stable disease and exacerbation, and prognosis. We report a case of a 67-year-old Asian woman who presented with progressive dyspnea. Chest computed tomography (CT) scans revealed nonspecific interstitial pneumonia. Serologic testing indicated positive anti-Jo-1 without presence of extrathoracic manifestations. An IPAF diagnosis was made after a multidisciplinary discussion. The patient experienced a severe exacerbation requiring mechanical ventilation, and she was successfully salvaged with methylprednisolone pulse therapy and single-dose cyclophosphamide. During the one-year follow-up, she reported bilateral leg muscle weakness with noticeably elevated serum creatine kinase, suggesting polymyositis. The development of malignancy was also noted 15 months after the initial presentation, and the patient eventually died. This report demonstrated successful salvage treatment with glucocorticoid pulse therapy for IPAF with acute exacerbation. However, the maintenance therapy failed to control disease progression. The treatment strategies for exacerbation and stable disease in IPAF remain unknown and need further studies. Given the high risk of evolution into a defined connective tissue disease (CTD), regular evaluation of the clinical features and biomarkers of CTDs is essential for patients with IPAF.
Impact of Carpal Tunnel Syndrome Surgery on Early Diagnosis and Treatment of Transthyretin Cardiac Amyloidosis
Authors: Kuznecova Inesa , Mierkyte Gerda , Janciauskas Dainius , Vajauskas Donatas , Jankauskas Antanas , Pilipaityte Loreta , Rimdeika Rytis , Tamaliunas Vytautas , Ereminiene Egle
Published: February 2023

Background and Objectives: Cardiac amyloidosis is an infiltrative, progressive, and restrictive cardiomyopathy that leads to heart failure, reduces life quality, and causes death. This is a multisystem disorder caused by mutations of the transthyretin protein and is associated not only with cardiac diseases or carpal tunnel syndrome but also with nerve, liver, lung, gastrointestinal tract, kidney, or eye pathologies. Carpal tunnel syndrome is an early red-flag symptom of transthyretin (TTR) cardiac amyloidosis; therefore, screening for unsuspected cardiac amyloidosis can be performed through histological testing of flexor retinaculum specimens gathered during carpal tunnel release surgery. Our case highlights that early detection and accurate diagnosis of a disease are important factors for improving clinical outcomes in patients with TTR amyloidosis. Case Summary: We report the case of a 71-year-old man who presented with bilateral carpal tunnel syndrome. Amyloid deposits were detected after carpal tunnel release surgery through histological testing of the synovial tissue. The patient was sent for a cardiological evaluation. Physical examination, laboratory tests, and the ECG revealed no significant changes. The diagnosis of amyloidosis was confirmed with multimodality imaging in the early stage, which helped to start specific medicamental treatment with the transthyretin stabilizer tafamidis. Conclusions: Our objective is to highlight the early recognition and specific medicamental treatment of cardiac amyloidosis for better patient prognosis and outcomes.
Comparative Proteomic Analysis of Protein Patterns of Stenotrophomonas maltophilia in Biofilm and Planktonic Lifestyles
Authors: Di Bonaventura Giovanni , Picciani Carla , Lupetti Veronica , Pompilio Arianna
Published: February 2023

Stenotrophomonas maltophilia is a clinically relevant bacterial pathogen, particularly in cystic fibrosis (CF) patients. Despite the well-known ability to form biofilms inherently resistant to antibiotics and host immunity, many aspects involved in S. maltophilia biofilm formation are yet to be elucidated. In the present study, a proteomic approach was used to elucidate the differential protein expression patterns observed during the planktonic-to-biofilm transition of S. maltophilia Sm126, a strong biofilm producer causing chronic infection in a CF patient, to identify determinants potentially associated with S. maltophilia biofilm formation. In all, 57 proteins were differentially (3-fold; p < 0.01) expressed in biofilm cells compared with planktonic counterparts: 38 were overexpressed, and 19 were down-expressed. It is worth noting that 34 proteins were exclusively found in biofilm, mainly associated with quorum sensing-mediated intercellular communication, augmented glycolysis, amino acid metabolism, biosynthesis of secondary metabolites, phosphate signaling, response to nutrient starvation, and general stress. Further work is warranted to evaluate if these proteins can be suitable targets for developing anti-biofilm strategies effective against S. maltophilia.
Improvement and Validation of a Multi-Locus Variable Number of Tandem Repeats Analysis (MLVA8+) for Klebsiella pneumoniae, Klebsiella variicola, and Klebsiella quasipneumoniae
Authors: Donchev Deyan , Ivanov Ivan N. , Stoikov Ivan , Sabtcheva Stefana , Kalchev Yordan , Murdjeva Marianna , Dobreva Elina , Hristova Rumyana
Published: February 2023

The genotyping of the multidrug-resistant Klebsiella pneumoniae species complex is essential to identify outbreaks and to track their source and spread. The aim of this study was to improve and extend the typeability, availability, cost and time efficiency of an existing multi-locus VNTR analysis (MLVA). A modified scheme (MLVA8+) was adopted and validated for strain-level differentiation of the three Klebsiella species involved in human pathology. A diverse set of 465 K. pneumoniae clinical isolates from 22 hospitals and 3 outpatient laboratories in Bulgaria were studied, where 315 were carbapenem-resistant. The MLVA8+ typeability was significantly improved and the typing data were validated against 158 isolates which were previously typed by WGS. The MLVA8+ results were highly concordant with the classic 7-locus MLST and the novel K. variicola MLST, but had greater congruency coefficients (adjusted Wallace). A major advantage was the differentiation of the hybrid cluster ST258 into its corresponding clades. Furthermore, the applicability of MLVA8+ was demonstrated by conducting a retrospective investigation of the intra-hospital spread of blaKPC-, blaNDM- and blaOXA-48-like producers. The MLVA8+ has improved utility and extended typing scope to K. variicola and K. quasipneumoniae, while its cost and time-to-result were reduced.
Use of Galleria mellonella as a Model for Insect Vector Transmission of the Foodborne Pathogen Campylobacter jejuni in Broiler Chickens: A Pilot Study
Authors: Chaloner Gemma , Lacharme-Lora Lizeth , Wedley Amy , Wigley Paul
Published: February 2023

There is growing pressure to find a way to eradicate or reduce the levels of foodborne pathogens such as Campylobacter in broiler chickens, whilst limiting the use of antimicrobials. For Campylobacter, there is currently no vaccine and on-farm biosecurity alone is insufficient to prevent colonization of broiler chicken flocks. Dipteran flies are proven carriers of Campylobacter and their entry into broiler houses may contribute to its transmission to broiler chickens. As there is currently no experimental vector transmission model for Campylobacter and chickens, we decided to examine experimentally whether Galleria mellonella could be used as vector to transmit Campylobacter to broiler chickens. More recently, the use of live insect feed has been proposed both for its nutritional qualities and improving bird welfare through the encouragement of natural foraging behaviours and it is unclear any risk this poses in terms of pathogen transmission. In this study, day-old chicks (n = 29) were obtained from a commercial hatchery. At three weeks of age, birds were split into 4 This groups; Group 1 was infected via oral gavage with 106 cells of C. jejuni-M1, Group 2 was fed Galleria mellonella infected with 106 cells of C. jejuni-M1, Group 3 was fed uninfected Galleria mellonella, whilst the remaining group was unchallenged. Cloacal swabs were taken at 2, 4, and 6 days post-infection (dpi) to follow transmission and at 8 dpi birds culled and C. jejuni load quantified in the caeca and liver. At 8 dpi, all birds in both the Campylobacter gavage group and those in the group fed the Campylobacter infected Galleria mellonella were Campylobacter positive, whereas those fed uninfected Galleria mellonella and the control group were all Campylobacter negative. The mean caecal Campylobacter load in the Campylobacter gavage group was 1.7 × 1010 per gram compared with 8.6 × 109 in the group fed the Campylobacter-infected Galleria mellonella. No liver positives were found in any of the groups. Our findings indicate that feeding broiler chickens with the vector Galleria mellonella infected with C. jejuni-M1 is sufficient to establish colonisation with C. jejuni. We propose that Galleria can be used as an easy and flexible model for vector transmission of foodborne pathogens in chicken.
Planning Strategies for Distributed PV-Storage Using a Distribution Network Based on Load Time Sequence Characteristics Partitioning
Authors: Zhang Yuanbo , Yang Yiqiang , Zhang Xueguang , Pu Wei , Song Hong
Published: February 2023

At present, due to the fact that large-scale distributed photovoltaics can access distribution networks and that there is a mismatch between load demand and photovoltaic output time, it is difficult for traditional distributed photovoltaic planning to meet the partition-based control of high permeability photovoltaic grid-connected operations. As a solution to this problem, this paper proposes a planning method for photovoltaic storage partitions. First of all, a partitioning method for electrical distance modularity based on voltage/active power and voltage/reactive power is presented, along with a modified AP-TD-K-medoids trilevel clustering algorithm that was designed to cluster and partition the distribution network. In addition, according to the partitioning results, a bilevel co-ordination planning model for distributed photovoltaic storage was developed. The upper level aimed to minimize the annual comprehensive cost for which the decision variables are the photovoltaic capacity, energy storage capacity, and power of each partition. The lower level aimed to minimize system network losses, and the decision variables for this are the photovoltaic installation capacity and energy storage installation location of the intrapartition node. Finally, we adopted the particle swarm algorithm with bilevel iterative adaptive weight to solve the planning model, and the simulation was carried out on the distribution system of the IEEE33 nodes. The results show the rationality of the model and the effectiveness of the solution method.
Computer-Guided Osteotomy with Simultaneous Implant Placement and Immediately Loaded Full-Arch Fixed Restoration: A Case Report
Authors: Todaro Claudia , Cerri Michael , Isola Gaetano , Manazza Andrea , Storelli Stefano , Rodriguez y Baena Ruggero , Lupi Saturnino Marco
Published: February 2023

Aim: This case report aims to illustrate a clinical protocol that allows for the rehabilitation of patients requiring extensive osteotomy, simultaneous implant placement, and full-arch, screwed-in prosthetics in one session. This protocol allows for the improvement of the aesthetics and functionality of the fixed implant-supported prosthesis through the preoperative planning of all surgical procedures, including osteotomy, and of the prosthesis through the application of 3D-printing technology for the creation of surgical templates and prostheses. Methods: This case report concerns a 72-year-old patient, ASA1, who, following diagnosis, the establishment of a treatment plan, and the provision of informed consent, opted for an immediate, full-arch rehabilitation of the lower arch. The digital planning stage started with the correct positioning of the fixtures. The proper bone levels were found and used to guide the creation of the provisional screwed-in prothesis. Two templates with the same supports (landmarks/pins) were then 3D-printed: a positioning template, including a slit to assist the surgeon during the osteotomy, and a surgery template to assist the surgeon during the implants’ positioning. A screwed-in prosthesis encased in resin C&B MFH (NEXTDENT®, Soesterberg, The Netherlands) was delivered. Minimal occlusal adjustments were performed. Results: In a single clinical session, through careful planning and the pre-operative 3D printing of a prosthesis, a temporary implant-supported prosthetic rehabilitation was possible in a case that required an extended osteotomy. Clinically, the correspondence between the virtual design phase and the final realization was consistent. At a functional level, the provisional prosthesis required minimal occlusal adjustments and the DVO values obtained in the immediate post-operative period were found to be comparable to those of the virtual design. By planning the final position of the bone and the implants in advance, it was possible to deliver a full-arch prothesis with proper implant emergence, occlusal vertical dimensions, and occlusal relationship. Conclusion: This fully digital protocol allows the clinician to preview and plan the osteotomy and implant surgery as well as the delivery of the temporary, immediately loaded, complete, fixed prosthesis in patients who are candidates for post-extraction surgery with the need for severe osteotomy.
Cramér–Rao, Fisher–Shannon and LMC–Rényi Complexity-like Measures of Multidimensional Hydrogenic Systems with Application to Rydberg States
Authors: Dehesa Jesús S.
Published: February 2023

Statistical measures of complexity hold significant potential for applications in D-dimensional finite fermion systems, spanning from the quantification of the internal disorder of atoms and molecules to the information–theoretical analysis of chemical reactions. This potential will be shown in hydrogenic systems by means of the monotone complexity measures of Cramér–Rao, Fisher–Shannon and LMC(Lopez-Ruiz, Mancini, Calbet)–Rényi types. These quantities are shown to be analytically determined from first principles, i.e., explicitly in terms of the space dimensionality D, the nuclear charge and the hyperquantum numbers, which characterize the system’ states. Then, they are applied to several relevant classes of particular states with emphasis on the quasi-spherical and the highly excited Rydberg states, obtaining compact and physically transparent expressions. This is possible because of the use of powerful techniques of approximation theory and orthogonal polynomials, asymptotics and generalized hypergeometric functions.
Monitoring Building Activity by Persistent Scatterer Interferometry
Authors: Letsios Vasilis , Faraslis Ioannis , Stathakis Demetris
Published: February 2023

In many countries globally, information for new buildings is either scarce or incomplete. In an effort to bridge this information gap an approach based on public domain synthetic aperture radar (SAR) satellite data is introduced. The method is based on the persistent scatterer interferometry (PSI) technique in order to detect newly constructed buildings and estimate their heights in a selected case study area in Athens, Greece. The overall objective is to derive timely spatial information for building activity. A key concept of the approach is the residual height, i.e., the difference in height of each point compared to a reference point that is suitably selected. The data used are acquired by the Sentinel-1 satellite. Both ascending and descending orbits and dual polarizations (VV/VH) are used. The results show that as much as 70% of new buildings can be detected at a vertical accuracy approximately of 2.5 m, which is sufficient to determine the number of stories per building. Overall, the proposed method can provide an efficient insight regarding building activity and provide a significant information layer for urban studies in a rapidly changing world.
Remote Sensing-Based Approach for the Assessing of Ecological Environmental Quality Variations Using Google Earth Engine: A Case Study in the Qilian Mountains, Northwest China
Authors: Wang Hong , Liu Chenli , Zang Fei , Liu Youyan , Chang Yapeng , Huang Guozhu , Fu Guiquan , Zhao Chuanyan , Liu Xiaohuang
Published: February 2023

Due to climate change and human activities, the eco-environment quality (EEQ) of eco-fragile regions has undergone massive change, especially in the Tibet Plateau. The Qilian Mountains (QLM) region is an essential ecological function zone in the northeastern Tibet Plateau, which plays a vital role in northwestern China’s eco-environmental balance. However, EEQ changes in the QLM during the 21st century remain poorly understood. In this study, the spatiotemporal variations of the EEQ in the QLM were analyzed from 2000 to 2020 using a remote sensing ecological index (RSEI). The EEQ driving factors are identified by the geographic detector, and the spatial influence of critical factors is represented by a geographically weighted regression model. The results show low EEQ in the QLM. From 2000 to 2020, the EEQ initially slightly improved, then deteriorated, and finally gradually recovered. Spatially, the EEQ shows an increasing trend from northwest to southeast. Moran’s I of EEQ remains at around 0.95, representing high spatial aggregation. “High–High” and “Low–Low” clustering features dominate in the local spatial autocorrelation, indicating the EEQ of the QLM is polarized. Precipitation is the dominant positive factor in the EEQ, with a q statistics exceeding 0.644. Furthermore, the key factors (precipitation, distance to towns, distance to roads) affecting EEQ in different periods vary significantly in space. From results we can draw the conclusion that the natural factors mainly control the spatial patterns of EEQ, while the human factors mainly impact the temporal trend of EEQ, the EEQ in the QLM has been significantly improved since 2015. Our findings can provide theoretical support for future eco-environmental protection and restoration in the QLM.
UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep Learning
Authors: Bajić Milan , Potočnik Božidar
Published: February 2023

A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People’s Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 × 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants’ detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model’s complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems.
Time Ring Data: Definition and Application in Spatio-Temporal Analysis of Urban Expansion and Forest Loss
Authors: Liu Xin , Li Xinhu , Bao Haijun
Published: February 2023

Remote sensing can provide spatio-temporal continuous Earth observation data and is becoming the main data source for spatial and temporal analysis. Remote sensing data have been widely used in applications such as meteorological monitoring, forest investigation, environmental health, urban planning, and water conservancy. While long-time-series remote sensing data are used for spatio-temporal analysis, this analysis is usually limited because of the large data volumes and complex models used. This study intends to develop an innovative and simple approach to reveal the spatio-temporal characteristics of geographic features from the perspective of remote sensing data themselves. We defined an efficient remote sensing data structure, namely time ring (TR) data, to depict the spatio-temporal dynamics of two common geographic features. One is spatially expansive features. Taking nighttime light (NTL) as an example, we generated a NTL TR map to exhibit urban expansion with spatial and temporal information. The speed and acceleration maps of NTL TR data indicated extraordinary expansion in the last 10 years, especially in coastal cities and provincial capitals. Beijing, Tianjin, Hebei Province, Shandong Province, and Jiangsu Province exhibited fast acceleration of urbanization. The other is spatially contractive features. We took forest loss in the Amazon basin as an example and produced a forest cover TR map. The speed and acceleration were mapped in two 10-year periods (2000–2010 and 2010–2020) in order to observe the changes in Amazon forest cover. Then, combining cropland TR data, we determined the consistency of the spatio-temporal variations and used a linear regression model to detect the association between the acceleration of cropland and forest. The forest TR map showed that, spatially, there was an apparent phenomenon of forest loss occurring in the southern and eastern Amazon basin. Temporally, the speed of forest loss was more drastic between 2000 and 2010 than that in 2010–2020. In addition, the acceleration of forest loss showed a dispersed distribution, except for in Bolivia, which demonstrated a concentrated regional acceleration. The R-squared value of the linear regression between forest and cropland acceleration reached 0.75, indicating that forest loss was closely linked to the expansion of cropland. The TR data defined in this study not only optimized the use of remote sensing data, but also facilitated their application in spatio-temporal integrative analysis. More importantly, multi-field TR data could be jointly applied to explore the driving force at spatial and temporal scales.
A Novel Deep Learning Method for Automatic Recognition of Coseismic Landslides
Authors: Yang Qiyuan , Wang Xianmin , Zhang Xinlong , Zheng Jianping , Ke Yu , Wang Lizhe , Guo Haixiang
Published: February 2023

Massive earthquakes generally trigger thousands of coseismic landslides. The automatic recognition of these numerous landslides has provided crucial support for post-earthquake emergency rescue, landslide risk mitigation, and city reconstruction. The automatic recognition of coseismic landslides has always been a difficult problem due to the relatively small size of a landslide and various complicated environmental backgrounds. This work proposes a novel semantic segmentation network, EGCN, to improve the landslide identification accuracy. EGCN conducts coseismic landslide recognition by a recognition index set as the input data, CGBlock as the basic module, and U-Net as the baseline. The CGBlock module can extract the relatively stable global context-dependent features (global context features) and the unstable local features by the GNN Branch and CNN Branch (GNN Branch contains the proposed EISGNN) and integrates them via adaptive weights. This method has four advantages. (1) The recognition indices are established according to the causal mechanism of coseismic landslides. The rationality of the indices guarantees the accuracy of landslide recognition. (2) The module of EISGNN is suggested based on the entropy importance coefficient and GATv2. Owing to the feature aggregation among nodes with high entropy importance, global and useful context dependency can be synthesized and the false alarm of landslide recognition can be reduced. (3) CGBlock automatically integrates context features and local spatial features, and has strong adaptability for the recognition of coseismic landslides located in different environments. (4) Owing to CGBlock being the basic module and U-Net being the baseline, EGCN can integrate the context features and local spatial characteristics at both high and low levels. Thus, the accuracy of landslide recognition can be improved. The meizoseismal region of the Ms 7.0 Jiuzhaigou earthquake is selected as an example to conduct coseismic landslide recognition. The values of the precision indices of Overall Accuracy, mIoU, Kappa, F1-score, Precision, and Recall reached 0.99854, 0.99709, 0.97321, 0.97396, 0.97344, and 0.97422, respectively. The proposed method outperforms the current major deep learning methods.
Drought Monitoring Using Landsat Derived Indices and Google Earth Engine Platform: A Case Study from Al-Lith Watershed, Kingdom of Saudi Arabia
Authors: Ejaz Nuaman , Bahrawi Jarbou , Alghamdi Khalid Mohammed , Rahman Khalil Ur , Shang Songhao
Published: February 2023

Precise assessment of drought and its impact on the natural ecosystem is an arduous task in regions with limited climatic observations due to sparsely distributed in situ stations, especially in the hyper-arid region of Kingdom of Saudi Arabia (KSA). Therefore, this study investigates the application of remote sensing techniques to monitor drought and compare the remote sensing-retrieved drought indices (RSDIs) with the standardized meteorological drought index (Standardized Precipitation Evapotranspiration Index, SPEI) during 2001–2020. The computed RSDIs include Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), which are derived using multi-temporal Landsat 7 ETM+, Landsat 8 OLI/TIRS satellites, and the Google Earth Engine (GEE) platform. Pearson correlation coefficient (CC) is used to find the extent of agreement between the SPEI and RSDIs. The comparison showed CC values of 0.74, 0.67, 0.57, and 0.47 observed for VHI/SPEI-12, VHI/SPEI-6, VHI/SPEI-3, and VHI/SPEI-1, respectively. Comparatively low agreement was observed between TCI and SPEI with CC values of 0.60, 0.61, 0.42, and 0.37 observed for TCI/SPEI-12, TCI/SPEI-6, TCI/SPEI-3, and TCI/SPEI-1. A lower correlation with CC values of 0.53, 0.45, 0.33 and 0.24 was observed for VCI/SPEI-12, VCI/SPEI-6, VCI/SPEI-3, and VCI/SPEI-1, respectively. Overall, the results suggest that VHI and SPEI are better correlated drought indices and are suitable for drought monitoring in the data-scarce hyper-arid regions. This research will help to improve our understanding of the relationships between meteorological and remote sensing drought indices.
Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis
Authors: Anderson Connor J. , Heins Daniel , Pelletier Keith C. , Knight Joseph F.
Published: February 2023

Uncrewed aircraft systems (UASs) are a popular tool when surveilling for invasive alien plants due to their high spatial and temporal resolution. This study investigated the efficacy of a UAS equipped with a three-band (i.e., red, green, blue; RGB) sensor to identify invasive Phragmites australis in multiple Minnesota wetlands using object-based image analysis (OBIA) and machine learning (ML) algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The addition of a post-ML classification OBIA workflow was tested to determine if ML classifications can be improved using OBIA techniques. Results from each ML algorithm were compared across study sites both with and without the post-ML OBIA workflow. ANN was identified as the best classifier when not incorporating a post-ML OBIA workflow with a classification accuracy of 88%. Each of the three ML algorithms achieved a classification accuracy of 91% when incorporating the post-ML OBIA workflow. Results from this study suggest that a post-ML OBIA workflow can increase the ability of ML algorithms to accurately identify invasive Phragmites australis and should be used when possible. Additionally, the decision of which ML algorithm to use for Phragmites mapping becomes less critical with the addition of a post-ML OBIA workflow.
Calibrating Laser Three-Dimensional Projection Systems Using Binocular Vision
Authors: Lao Dabao , Wang Yukai , Wang Fang , Gao Chao
Published: February 2023

A laser three-dimensional (3D) projection system is an auxiliary system in intelligent manufacturing. It works with a positioning system in practical applications. This study proposes a calibration method for laser 3D projection systems based on binocular vision. The significance of the binocular vision positioning function for the calibration process was analyzed. Two calibration methods for laser 3D projection systems based on the binocular vision positioning function were proposed. One method involves simplified calculation models and another used data to solve the conversion relationship. The experimental calibration of the projection system was performed using data to directly solve the conversion relationship. The experiment demonstrated the simplicity of the proposed calibration method. The calculation time was less under the 3D laser projection system based on binocular vision. Moreover, the mean calibration error was 0.38 mm at a working distance of 1.8–2.2 m.
A Sensor-Aided System for Physical Perfect Control Applications in the Continuous-Time Domain
Authors: Majewski Paweł , Hunek Wojciech P. , Pawuś Dawid , Szurpicki Krzysztof , Wojtala Tomasz
Published: February 2023

The recently introduced continuous-time perfect control algorithm has revealed a great potential in terms of the maximum-speed and maximum-accuracy behaviors. However, the discussed inverse model-originated control strategy is associated with considerable energy consumption, which has exceeded a technological limitation in a number of industrial cases. In order to prevent such an important drawback, several solutions could be considered. Therefore, an innovative perfect control scheme devoted to the multivariable real-life objects is investigated in this paper. Henceforth, the new IMC-related approach, strongly supported by the vital sensor-aided system, can successfully be employed in every real-time engineering task, where the precision of conducted processes plays an important role. Theoretical and practical examples strictly confirm the big implementation potential of the new established method over existing ones. It has been seen that the new perfect control algorithm outperforms the classical control law in the form of LQR (considered in two separate ways), which is clearly manifested by almost all simulation examples. For instance, in the case of the multi-tank system, the performance indices ISE, RT, and MOE for LQR without an integration action have been equal to 2.431, 2.4×102, and 3.655×10−6, respectively, whilst the respective values 1.638, 1.58×102, and 1.514×10−7 have been received for the proposed approach.
Contributions of Support Point Number to Mirror Assembly Thermal Sensitivity Control
Authors: Li Haixing , Zhang Hongwen , Ding Yalin , Zhang Jichao , Cai Yuqi
Published: February 2023

Due to the extreme environmental temperature variations, solutions that enable ultra-low thermal sensitivity in a mirror assembly are crucial for high-performance aerial optical imaging sensors (AOIS). Strategies such as the elimination of the coefficient of thermal expansion (CTE) mismatch and the employment of a flexure connection at the interface cannot be simply duplicated for the application involved, demanding specific design constraints. The contributions of support point number to the surface thermal sensitivity reduction and support stiffness improvement have been studied. A synthetic six-point support system that integrates equally spaced multiple ultra-low radial stiffness mirror flexure units and assembly external interface flexure units has been demonstrated on a 260 mm apertured annular mirror that involves significant CTE mismatch and demanding support stiffness constraint. The surface deformation RMS, due to the 35 °C temperature variation, is 16.7 nm.