bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Immuno-Nutrition on Malnutrition, Inflammatory Response and Clinical Course of Semi-Critically Ill COVID-19 Patients: A Pilot Perspective Study
Authors: Scarcella Marialaura , Scarpellini Emidio , Piergallini Sara , Rinninella Emanuele , Routhiaux Karen , Rasetti Carlo , Abenavoli Ludovico , De Robertis Edoardo , Manzi Pietro , Commissari Rita , Monti Riccardo , Zanetti Michela
Published: March 2023

Background: The SARS-COV 2 pandemic has hit on our lives since early 2020. During different contagion waves, both malnutrition and overweight significantly correlated with patient mortality. Immune-nutrition (IN) has shown promising results in the clinical course of pediatric inflammatory bowel disease (IBD) and in both the rate of extubation and mortality of patients admitted to an intensive care unit (ICU). Thus, we wanted to assess the effects of IN on a clinical course of patients admitted to a semi-intensive COVID-19 Unit during the fourth wave of contagion that occurred at the end of 2021. Methods: we prospectively enrolled patients admitted to the semi-intensive COVID-19 Unit of San Benedetto General hospital. All patients had a biochemical, anthropometric, high-resolution tomography chest scan (HRCT) and complete nutritional assessments at the time of admission, after oral administration of immune-nutrition (IN) formula, and at 15 days interval follow-up. Results: we enrolled 34 consecutive patients (age 70.3 ± 5.4 years, 6 F, BMI 27.0 ± 0.5 kg/m2). Main comorbidities were diabetes (20%, type 2 90 %), hyperuricemia (15%), hypertension (38%), chronic ischemic heart disease (8 %), COPD (8%), anxiety syndrome (5%), and depression (5%). 58% of patients were affected as moderately-to-severely overweight; mini nutritional assessment (MNA) score (4.8 ± 0.7) and phase angle (PA) values (3.8 ± 0.5) suggestive of malnutrition were present in 15% of patients, mainly with a history of cancer. After 15 days upon admission, we recorded 3 deaths (mean age 75.7 ± 5.1 years, BMI 26.3 ± 0.7 kg/m2) and 4 patients were admitted to the ICU. Following IN formula administration, inflammatory markers significantly decreased (p < 0.05) while BMI and PA did not worsen. These latter findings were not observed in a historical control group that did not receive IN. Only one patient needed protein-rich formula administration. Conclusions: in this overweight COVID-19 population immune-nutrition prevented malnutrition development with a significant decrease of inflammatory markers.
Processed Food–Sweets Patterns and Related Behaviors with Attention Deficit Hyperactivity Disorder among Children: A Case–Control Study
Authors: Yan Wu , Lin Shuang , Wu Dandan , Shi Yanan , Dou Lihua , Li Xiaonan
Published: March 2023

Background: Previous studies have focused on the associations between core symptoms and dietary intake in children with attention deficit hyperactivity disorder (ADHD). However, few studies have explored how dietary patterns and behaviors relate to the risk of ADHD. The aim of our study is to explore the associations between dietary patterns and behaviors and the risk of ADHD, which could provide evidence for follow-up and treatments for children with ADHD. Methods: We conducted a case–control study, including 102 children diagnosed with ADHD and 102 healthy children. The food frequency questionnaire (FFQ) and the children’s eating behavior questionnaire (CEBQ) were used to investigate food consumption and eating behaviors. We applied exploratory factor analysis for constructing dietary patterns, and the factor scores were adopted for log-binomial regression to assess the associations between how dietary patterns and eating behaviors affect the risk of ADHD. Results: We extracted five dietary patterns with a cumulative contribution rate of 54.63%. Processed food–sweets scores revealed positive associations with an increased risk of ADHD (OR = 1.451, 95% CI: 1.041–2.085). Moreover, processed food–sweets tertile 3 was associated with an increased risk of ADHD (OR = 2.646, 95% CI: 1.213–5.933). In terms of eating behaviors, the group with a higher score relating to a desire to drink was also positively correlated with the risk of ADHD (OR = 2.075, 95% CI: 1.137–3.830). Conclusions: In the treatment and follow-up of children with ADHD, dietary intake and eating behaviors should be considered.
Anti-Tuberculosis Mur Inhibitors: Structural Insights and the Way Ahead for Development of Novel Agents
Authors: Mehta Kunal , Khambete Mihir , Abhyankar Arundhati , Omri Abdelwahab
Published: March 2023

Mur enzymes serve as critical molecular devices for the synthesis of UDP-MurNAc-pentapeptide, the main building block of bacterial peptidoglycan polymer. These enzymes have been extensively studied for bacterial pathogens such as Escherichia coli and Staphylococcus aureus. Various selective and mixed Mur inhibitors have been designed and synthesized in the past few years. However, this class of enzymes remains relatively unexplored for Mycobacterium tuberculosis (Mtb), and thus offers a promising approach for drug design to overcome the challenges of battling this global pandemic. This review aims to explore the potential of Mur enzymes of Mtb by systematically scrutinizing the structural aspects of various reported bacterial inhibitors and implications concerning their activity. Diverse chemical scaffolds such as thiazolidinones, pyrazole, thiazole, etc., as well as natural compounds and repurposed compounds, have been reviewed to understand their in silico interactions with the receptor or their enzyme inhibition potential. The structural diversity and wide array of substituents indicate the scope of the research into developing varied analogs and providing valuable information for the purpose of modifying reported inhibitors of other multidrug-resistant microorganisms. Therefore, this provides an opportunity to expand the arsenal against Mtb and overcome multidrug-resistant tuberculosis.
Combination of Dissolving Microneedles with Nanosuspension and Co-Grinding for Transdermal Delivery of Ketoprofen
Authors: Ramadon Delly , Ulayya Fathin , Qur’ani Annisa Sakinah , Iskandarsyah Iskandarsyah , Harahap Yahdiana , Anjani Qonita Kurnia , Aileen Vania , Hartrianti Pietradewi , Donnelly Ryan F.
Published: March 2023

Ketoprofen is an anti-inflammatory agent that may cause gastric irritation if administered orally. Dissolving microneedles (DMN) can be a promising strategy to overcome this issue. However, ketoprofen has a low solubility; therefore, it is essential to enhance its solubility using certain methods, namely nanosuspension (NS) and co-grinding (CG). This research aimed to formulate DMN containing ketoprofen-loaded NS and CG. Ketoprofen NS was formulated with poly(vinyl alcohol) (PVA) at concentrations of 0.5%, 1%, and 2%. CG was prepared by grinding ketoprofen with PVA or poly(vinyl pyrrolidone) (PVP) at different drug–polymer ratios. The manufactured ketoprofen-loaded NS and CG were evaluated in terms of their dissolution profile. The most promising formulation from each system was then formulated into microneedles (MNs). The fabricated MNs were assessed in terms of their physical and chemical properties. An in vitro permeation study using Franz diffusion cells was also carried out. The most promising MN-NS and MN-CG formulations were F4-MN-NS (PVA 5%-PVP 10%), F5-MN-NS (PVA 5%-PVP 15%), F8-MN-CG (PVA 5%-PVP 15%), and F11-MN-CG (PVA 7.5%-PVP 15%), respectively. The cumulative amounts of drug permeated after 24 h for F5-MN-NS and F11-MN-CG were 3.88 ± 0.46 µg and 8.73 ± 1.40 µg, respectively. In conclusion, the combination of DMN with nanosuspension or a co-grinding system may be a promising strategy for delivering ketoprofen transdermally.
Plausible Role of Stem Cell Types for Treating and Understanding the Pathophysiology of Depression
Authors: Sachdeva Punya , Ji Seongmin , Ghosh Shampa , Ghosh Soumya , Raghunath Manchala , Kim Hyunjin , Bhaskar Rakesh , Sinha Jitendra Kumar , Han Sung Soo
Published: March 2023

Major Depressive Disorder (MDD), colloquially known as depression, is a debilitating condition affecting an estimated 3.8% of the population globally, of which 5.0% are adults and 5.7% are above the age of 60. MDD is differentiated from common mood changes and short-lived emotional responses due to subtle alterations in gray and white matter, including the frontal lobe, hippocampus, temporal lobe, thalamus, striatum, and amygdala. It can be detrimental to a person’s overall health if it occurs with moderate or severe intensity. It can render a person suffering terribly to perform inadequately in their personal, professional, and social lives. Depression, at its peak, can lead to suicidal thoughts and ideation. Antidepressants manage clinical depression and function by modulating the serotonin, norepinephrine, and dopamine neurotransmitter levels in the brain. Patients with MDD positively respond to antidepressants, but 10–30% do not recuperate or have a partial response accompanied by poor life quality, suicidal ideation, self-injurious behavior, and an increased relapse rate. Recent research shows that mesenchymal stem cells and iPSCs may be responsible for lowering depression by producing more neurons with increased cortical connections. This narrative review discusses the plausible functions of various stem cell types in treating and understanding depression pathophysiology.
Using the Simulated Patient Methodology in the Form of Mystery Calls in Community Pharmacy Practice Research: A Scoping Review Protocol
Authors: Kunow Christian , Langer Bernhard
Published: March 2023

Community pharmacies (CPs) play a major role in health care delivery. The simulated patient methodology (SPM), which is considered the “gold standard”, is recommended for studying CP practice. SPM can be applied in different forms, which include visits and also calls (“mystery calls”). So far, only the role of visits in the investigation of CP practice is known. As the first study worldwide, a systematic map of such reports will be provided, which applies calls in the context of the SPM for the study of CP practice. Reports with the pharmacy staff as the population under study should be included. Reports should be included that conduct an investigation using the SPM in the form of calls to simulate patients and other customers. Reports should be included that examine CP practice as defined by the International Pharmaceutical Federation and the World Health Organization (FIP/WHO). The scoping review methodology will be applied using the associated guidelines from Joanna Briggs Institute (JBI) and PRISMA extension for Scoping Review (PRISMA-ScR). The search will be for both published and unpublished original research in English with transparent information on SPM until the end of 2022. The plan is to search Embase, MEDLINE via PubMed, and Web of Science. Directly afterward, the respective literature collection of the reviewers and the reference lists of suitable international reviews will be searched. This will be followed by a forward and backward snowballing in Google Scholar. For the inclusion of reports, a selection process and for the data extraction a data charting process with the help of variables derived from related reviews and from two SPM-form spanning international guidelines will be performed. The data extracted from the included reports should be synthesized and presented in MS Excel tabular form using the previously determined variables.
Heart Rate Variability Parameters to Evaluate Autonomic Functions in Healthy Young Subjects during Short-Term “Dry” Immersion
Authors: Gerasimova-Meigal Liudmila , Meigal Alexander , Sireneva Nadezhda , Gerasimova Maria , Sklyarova Anna
Published: March 2023

There is a gap in the current knowledge on the immediate mechanisms of cardiovascular regulation in human subjects within short-term exposure to modeled microgravity using “dry” immersion. Aim. The purpose of the study was to evaluate cardiovascular responses in young healthy subjects during a 45 min session with the help of linear and nonlinear heart rate variability and hemodynamics parameters. The research voluntarily enrolled 33 subjects (18 men, 15 women) aged 19–23 years old. Results. The study showed that systolic and diastolic blood pressure was quite stable, some time-domain parameters of heart rate variability (SDNN, RMSSD, pNN50, etc.) and the frequency-domain (TP, HF, LF, but not VLF) have significantly increased within a 45 min “dry” immersion session. Of the non-linear parameters of heart rate variability, only ApEn significantly decreased during the “dry” immersion session. Conclusion. Our results suggest that a short-term 45 min DI session provokes in young healthy subjects neurogenic autonomic reaction based on the baroreceptor reflex. This provides stable hemodynamics in these subjects along the “dry” immersion session.
Comparative Characterization of Fruit Volatiles and Volatile-Related Genes Expression of ‘Benihoppe’ Strawberry and Its Somaclonal Mutant
Authors: Zhang Zhuo , Yu Shuang , Zhang Zhihong , Zhang Junxiang , Li He
Published: March 2023

Somaclonal variations in tissue cultures can be used in plant breeding programs. However, it is still unclear whether somaclonal variations and their original parent have differences in volatile compounds, and the candidate genes which result in the differences in volatile compounds also need to be identified. In this study, we utilized the ‘Benihoppe’ strawberry and its somaclonal mutant ‘Xiaobai’, which has different fruit aromas compared with ‘Benihoppe’, as research materials. Using HS-SPME-GC-MS, 113 volatile compounds have been identified in the four developmental periods of ‘Benihoppe’ and ‘Xiaobai’. Among them, the quantity and content of some unique esters in ‘Xiaobai’ were much higher than that in ‘Benihoppe’. In addition, we found that the contents and odor activity values of ethyl isovalerate, ethyl hexanoate, ethyl butyrate, ethyl pentanoate, linalool, and nerolidol in the red fruit of ‘Xiaobai’ were much higher compared with ‘Benihoppe’, which may result from the significantly increased expression of FaLOX6, FaHPL, FaADH, FaAAT, FaAAT1, FaDXS, FaMCS, and FaHDR in ‘Xiaobai’. However, the content of eugenol in ‘Benihoppe’ was higher than that in ‘Xiaobai’, which may result from the higher expression of FaEGS1a in ‘Benihoppe’ compared with ‘Xiaobai’. The results provide insights into the somaclonal variations that affect the volatile compounds in strawberries and can be used for strawberry quality improvement.
Therapeutic Efficacy of Polymeric Biomaterials in Treating Diabetic Wounds—An Upcoming Wound Healing Technology
Authors: Sathyaraj Weslen Vedakumari , Prabakaran Lokesh , Bhoopathy Jayavardhini , Dharmalingam Sankari , Karthikeyan Ramadoss , Atchudan Raji
Published: March 2023

Diabetic wounds are one of the serious, non-healing, chronic health issues faced by individuals suffering from diabetic mellitus. The distinct phases of wound healing are either prolonged or obstructed, resulting in the improper healing of diabetic wounds. These injuries require persistent wound care and appropriate treatment to prevent deleterious effects such as lower limb amputation. Although there are several treatment strategies, diabetic wounds continue to be a major threat for healthcare professionals and patients. The different types of diabetic wound dressings that are currently used differ in their properties of absorbing wound exudates and may also cause maceration to surrounding tissues. Current research is focused on developing novel wound dressings incorporated with biological agents that aid in a faster rate of wound closure. An ideal wound dressing material must absorb wound exudates, aid in the appropriate exchange of gas, and protect from microbial infections. It must support the synthesis of biochemical mediators such as cytokines, and growth factors that are crucial for faster healing of wounds. This review highlights the recent advances in polymeric biomaterial-based wound dressings, novel therapeutic regimes, and their efficacy in treating diabetic wounds. The role of polymeric wound dressings loaded with bioactive compounds, and their in vitro and in vivo performance in diabetic wound treatment are also reviewed.
Characterizing Pure Polymers under High Speed Compression for the Micromechanical Prediction of Unidirectional Composites
Authors: Hao Pei , Spronk Siebe W. F. , Sevenois Ruben D. B. , Van Paepegem Wim , Gilabert Francisco A.
Published: March 2023

The nonlinear behaviour of fibre-reinforced polymer composites (FRPC) in transverse loading is mainly induced by the constituent polymer matrix. The thermoset and thermoplastic matrices are typically rate- and temperature-dependent, complicating the dynamic material characterization process. Under dynamic compression, the microstructure of the FRPC develops local strains and local strain rates whose values can be much higher than those applied at macroscopic level. The correlation between the local (microscopic) values and the measurable (macroscopic) ones still present challenges when applying the strain rate in the range 10−3–103 s−1. This paper presents an in-house uniaxial compression test setup to provide robust stress–strain measurements applying strain rates up to 100 s−1. A semi-crystalline thermoplastic polyetheretherketone (PEEK) and a toughened thermoset epoxy PR520 are assessed and characterized. The thermomechanical response of the polymers is further modelled using an advanced glassy polymer model, naturally capturing the isothermal to adiabatic transition. A micromechanical model of a unidirectional composite undergoing dynamic compression is developed by using both validated polymers as matrices reinforced by carbon fibres (CF) using Representative Volume Element (RVE) models. These RVEs are used to analyse the correlation between the micro- and macroscopic thermomechanical response of the CF/PR520 and CF/PEEK systems investigated at intermediate to high strain rates. Both systems experience an excessive strain localization with local plastic strain about 19% when a macroscopic strain of 3.5% is applied. The comparison of using a thermoplastic and a thermoset as a matrix in composites is discussed with regard to the rate-dependence, the interface debonding and the self-heating effect.
Composting Processes for Agricultural Waste Management: A Comprehensive Review
Authors: Waqas Muhammad , Hashim Sarfraz , Humphries Usa Wannasingha , Ahmad Shakeel , Noor Rabeea , Shoaib Muhammad , Naseem Adila , Hlaing Phyo Thandar , Lin Hnin Aye
Published: March 2023

Composting is the most adaptable and fruitful method for managing biodegradable solid wastes; it is a crucial agricultural practice that contributes to recycling farm and agricultural wastes. Composting is profitable for various plant, animal, and synthetic wastes, from residential bins to large corporations. Composting and agricultural waste management (AWM) practices flourish in developing countries, especially Pakistan. Composting has advantages over other AWM practices, such as landfilling agricultural waste, which increases the potential for pollution of groundwater by leachate, while composting reduces water contamination. Furthermore, waste is burned, open-dumped on land surfaces, and disposed of into bodies of water, leading to environmental and global warming concerns. Among AWM practices, composting is an environment-friendly and cost-effective practice for agricultural waste disposal. This review investigates improved AWM via various conventional and emerging composting processes and stages: composting, underlying mechanisms, and factors that influence composting of discrete crop residue, municipal solid waste (MSW), and biomedical waste (BMW). Additionally, this review describes and compares conventional and emerging composting. In the conclusion, current trends and future composting possibilities are summarized and reviewed. Recent developments in composting for AWM are highlighted in this critical review; various recommendations are developed to aid its technological growth, recognize its advantages, and increase research interest in composting processes.
Pachyrhizus erosus Inhibits Adipogenesis via the Leptin-PPARγ-FAS Pathway in a High-Fat Diet-Induced Mouse Model
Authors: Lee Seung-Min , Bok So-Hyeon , Kim Min-Hee , Lee Ki Sun , Park Dae-Hun
Published: March 2023

In 2016, obese patients represented 13% of the worldwide adult population, and by 2030, they are projected to make up 34%. Obesity is an incommunicable disease, but it can induce many health problems. The groups consisted of a control, a 65% high-fat group, and a 250 mg/kg P. erosus group. Several biomarkers, such as body weight gain, the presence of TC/LDL/HDL in the serum, the weight of fat tissue, and liver weight/morphology, were investigated to define the anti-obesity mechanisms of P. erosus, and the adipogenesis pathway was studied. P. erosus suppressed body weight gain, decreased TC and LDL, prevented fat tissue weight gain, and prevented liver weight gain by blocking lipid droplet accumulation. P. erosus effectively decreased the up-regulated levels of leptin, significantly controlled both C/EBPα and PPARγ levels, and prevented increased FAS expression levels. We concluded that P. erosus effectively controlled obesity by regulating leptin-C/EBPα-PPARγ and FAS and might be a promising AOM.
The Miracle of the Bloody Foreskin at the Council of Charroux in 1082: Legatine Authority, Religious Spectacle, and Charismatic Strategies of Canonical Reform in the Era of Gregory VII
Authors: Brown Peter Scott
Published: March 2023

In 1082, at the council of Charroux convened by the papal legate Amatus of Oloron, astonished witnesses observed the Holy Prepuce, a rare body relic of Christ himself, to be miraculously spotted with fresh blood. This spectacular miracle holds implications for our understanding of charismatic strategies of religious reform in France in the era of Pope Gregory VII. Gregory’s use of standing legates with regional mandates, such as Amatus, was a novelty in papal administration, but the legates, though empowered as proxies of the pope, were often weak lieutenants. When they could not induce or coerce cooperation, they frequently confronted the impotence of their legal–canonical mandates. The miracle at Charroux, I will show, exemplifies an alternative charismatic strategy, harnessing liturgical art and spectacle to magnify the legate’s stature as an authority in the context of the Eucharistic controversy and religious reform.
Forest Fire Susceptibility Zonation in Eastern India Using Statistical and Weighted Modelling Approaches
Authors: Das Jayshree , Mahato Susanta , Joshi Pawan Kumar , Liou Yuei-An
Published: March 2023

Recurring forest fires disturb ecological balance, impact socio-economic harmony, and raise global concern. This study implements multiple statistical and weighted modelling approaches to identify forest fire susceptibility zones in Eastern India. Six models, namely, Frequency Ratio (FR), Certainty Factor (CF), Natural Risk Factor (NRF), Bivariate statistical (Wi and Wf), Analytical Hierarchy Process (AHP), and Logistic Regression (LR) were used in the study. Forest fire inventory (2001 to 2018) mapping was done using forest fire points captured by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Fire responsible components, namely, topography (which has four variables), climate (5), biophysics (8) and disturbance (4) were used as inputs to the modelling approaches. Multicollinearity analysis was carried out to examine the association and remove the highly-correlated variables before performing the modeling. Validation of model prediction levels was done using Area Under the Receiver Operating Characteristic Curve (ROC curve-AUC) value. The results reveal that the areas with west and southwest orientations, and moderate slope demarcate higher susceptibility to forest fire. High precipitation areas with lower temperature but ample solar radiation increase their susceptibility to forest fire. Mixed deciduous forest type with ample solar radiation, higher NDVI, lower NDWI and lower TWI values exhibits higher susceptibility. Model validation shows that LR (with AUC = 0.809) outperforms other models used in the study. To minimize the risk of fire and frame with proper management plans for the study area, susceptibility mapping using satellite imageries, GIS technique, and modelling approaches is highly recommended.
Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning
Authors: Arias-Rodriguez Leonardo F. , Tüzün Ulaş Firat , Duan Zheng , Huang Jingshui , Tuo Ye , Disse Markus
Published: March 2023

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.
SA-GAN: A Second Order Attention Generator Adversarial Network with Region Aware Strategy for Real Satellite Images Super Resolution Reconstruction
Authors: Zhao Jiayi , Ma Yong , Chen Fu , Shang Erping , Yao Wutao , Zhang Shuyan , Yang Jin
Published: March 2023

High-resolution (HR) remote sensing images have important applications in many scenarios, and improving the resolution of remote sensing images via algorithms is one of the key research fields. However, current super-resolution (SR) algorithms, which are trained on synthetic datasets, tend to have poor performance in real-world low-resolution (LR) images. Moreover, due to the inherent complexity of real-world remote sensing images, current models are prone to color distortion, blurred edges, and unrealistic artifacts. To address these issues, real-SR datasets using the Gao Fen (GF) satellite images at different spatial resolutions have been established to simulate real degradation situations; moreover, a second-order attention generator adversarial attention network (SA-GAN) model based on real-world remote sensing images is proposed to implement the SR task. In the generator network, a second-order channel attention mechanism and a region-level non-local module are used to fully utilize the a priori information in low-resolution (LR) images, as well as adopting region-aware loss to suppress artifact generation. Experiments on test data demonstrate that the model delivers good performance for quantitative metrics, and the visual quality outperforms that of previous approaches. The Frechet inception distance score (FID) and the learned perceptual image patch similarity (LPIPS) value using the proposed method are improved by 17.67% and 6.61%, respectively. Migration experiments in real scenarios also demonstrate the effectiveness and robustness of the method.
Absolute Calibration of the Chinese HY-2B Altimetric Mission with Fiducial Reference Measurement Standards
Authors: Mertikas Stelios P. , Lin Mingsen , Piretzidis Dimitrios , Kokolakis Costas , Donlon Craig , Ma Chaofei , Zhang Yufei , Jia Yongjun , Mu Bo , Frantzis Xenophon , Tripolitsiotis Achilles , Yang Lei
Published: March 2023

This research and collaboration work aims at the calibration and validation (Cal/Val) of the Chinese HY-2B satellite altimeter based upon two permanent Cal/Val facilities: (1) the China Altimetry Calibration Cooperation Plan in Qingdao, Bohai Sea and the Wanshan islands, China and (2) the permanent facility for altimetry calibration established by the European Space Agency in Crete, Greece. The HY-2B satellite altimeter and its radiometer have been calibrated and monitored using uniform, standardized procedures, as well as protocols and best practices, and they also built upon trusted and indisputable reference standards at both Cal/Val infrastructures in Europe and China. The HY-2B altimeter is thus monitored in a coordinated, absolute, homogeneous, long-term and worldwide manner. Calibration of altimeters is accomplished by examining satellite observations in open seas against reference measurements. Comparisons are established through precise satellite positioning, water level observations, GPS buoys and reference models (geoid, mean dynamic topography, earth tides, troposphere and ionosphere), all defined at the Cal/Val sites. In this work, the final uncertainty for the altimeter bias will be attributed to several individual sources of uncertainty, coming from observations in water level, atmosphere, absolute positioning, reference surface models, transfer of heights from Cal/Val sites to satellite observations, etc. Through this project, the procedures, protocols and best practices, originally developed in the course of the ESA FRM4ALT project, are updated, upgraded and followed at both Cal/Val facilities in Europe and China. All in all, the HY-2B satellite altimeter observes the sea level quite well and within its specifications.
Deep Convolutional Compressed Sensing-Based Adaptive 3D Reconstruction of Sparse LiDAR Data: A Case Study for Forests
Authors: Shinde Rajat C. , Durbha Surya S.
Published: March 2023

LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to their enormous volume. From the perspective of using LiDAR point clouds for forests, the challenge lies in learning local and global features, as the number of points in a typical 3D LiDAR point cloud is in the range of millions. In this research, we present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements. ADCoSNet uses empirical mode decomposition (EMD), a data-driven signal processing approach with Deep Learning, to decompose input signals into intrinsic mode functions (IMFs). These IMFs capture hierarchical implicit features in the form of decreasing spatial frequency. This research proposes using the last IMF (least varying component), also known as the Residual function, as a statistical prior for capturing local features, followed by fusing with the hierarchical convolutional features from the deep compressive sensing (CS) network. The central idea is that the Residue approximately represents the overall forest structure considering it is relatively homogenous due to the presence of vegetation. ADCoSNet utilizes this last IMF for generating sparse representation based on a set of CS measurement ratios. The research presents extensive experiments for reconstructing 3D LiDAR point clouds with high fidelity for various CS measurement ratios. Our approach achieves a maximum peak signal-to-noise ratio (PSNR) of 48.96 dB (approx. 8 dB better than reconstruction without data-dependent transforms) with reconstruction root mean square error (RMSE) of 7.21. It is envisaged that the proposed framework finds high potential as an end-to-end learning framework for generating adaptive and sparse representations to capture geometrical features for the 3D reconstruction of forests.
Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation
Authors: Zhou Feng , Chen Yangbo , Liu Jun
Published: March 2023

Runoff forecasting is important for water resource management. Although deep learning models have substantially improved the accuracy of runoff prediction, the temporal and feature dependencies between rainfall–runoff time series elements have not been effectively exploited. In this work, we propose a new hybrid deep learning model to predict hourly streamflow: SA-CNN-LSTM (self-attention, convolutional neural network, and long short-term memory network). The advantages of CNN and LSTM in terms of data extraction from time series data are combined with the self-attention mechanism. By considering interdependences of the rainfall–runoff sequence between timesteps and between features, the prediction performance of the model is enhanced. We explored the performance of the model in the Mazhou Basin, China; we compared its performance with the performances of LSTM, CNN, ANN (artificial neural network), RF (random forest), SA-LSTM, and SA-CNN. Our analysis demonstrated that SA-CNN-LSTM demonstrated robust prediction with different flood magnitudes and different lead times; it was particularly effective within lead times of 1–5 h. Additionally, the performance of the self-attention mechanism with LSTM and CNN alone, respectively, was improved at some lead times; however, the overall performance was unstable. In contrast, the hybrid model integrating CNN, LSTM, and the self-attention mechanism exhibited better model performance and robustness. Overall, this study considers the importance of temporal and feature dependencies in hourly runoff prediction, then proposes a hybrid deep learning model to improve the performances of conventional models in runoff prediction.
Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica
Authors: Chen Zhiwei , Jin Rong , Zhang Liqiang , Chen Ke , Li Qingxia
Published: March 2023

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm.
A Semantic Segmentation Framework for Hyperspectral Imagery Based on Tucker Decomposition and 3DCNN Tested with Simulated Noisy Scenarios
Authors: Padilla-Zepeda Efrain , Torres-Roman Deni , Mendez-Vazquez Andres
Published: March 2023

The present work, unlike others, does not try to reduce the noise in hyperspectral images to increase the semantic segmentation performance metrics; rather, we present a classification framework for noisy Hyperspectral Images (HSI), studying the classification performance metrics for different SNR levels and where the inputs are compressed. This framework consists of a 3D Convolutional Neural Network (3DCNN) that uses as input data a spectrally compressed version of the HSI, obtained from the Tucker Decomposition (TKD). The advantage of this classifier is the ability to handle spatial and spectral features from the core tensor, exploiting the spatial correlation of remotely sensed images of the earth surface. To test the performance of this framework, signal-independent thermal noise and signal-dependent photonic noise generators are implemented to simulate an extensive collection of tests, from 60 dB to −20 dB of Signal-to-Noise Ratio (SNR) over three datasets: Indian Pines (IP), University of Pavia (UP), and Salinas (SAL). For comparison purposes, we have included tests with Support Vector Machine (SVM), Random Forest (RF), 1DCNN, and 2DCNN. For the test cases, the datasets were compressed to only 40 tensor bands for a relative reconstruction error less than 1%. This framework allows us to classify the noisy data with better accuracy and significantly reduces the computational complexity of the Deep Learning (DL) model. The framework exhibits an excellent performance from 60 dB to 0 dB of SNR for 2DCNN and 3DCNN, achieving a Kappa coefficient from 0.90 to 1.0 in all the noisy data scenarios for a representative set of labeled samples of each class for training, from 5% to 10% for the datasets used in this work. The source code and log files of the experiments used for this paper are publicly available for research purposes.
Detection of Soil Erosion Hotspots in the Croplands of a Typical Black Soil Region in Northeast China: Insights from Sentinel-2 Multispectral Remote Sensing
Authors: Qi Lulu , Shi Pu , Dvorakova Klara , Van Oost Kristof , Sun Qi , Yu Hanqing , van Wesemael Bas
Published: March 2023

Global efforts to restore the world’s degraded croplands require knowledge on the degree and extent of accelerated soil organic carbon (SOC) loss induced by soil erosion. However, the methods for assessing where and to what extent erosion takes place are still inadequate for precise detection of erosion hotspots at high spatial resolution. Drawing on recent advances in multitemporal Sentinel-2 remote sensing to create a bare soil composite that reflects erosion-induced variations in soil spectral signatures, this study attempted to develop a spectra-based soil erosion mapping approach to pinpoint eroded hotspots in a typical catchment located in the black soil region of northeast China as characterized by undulating landscapes. We built a ground-truth dataset consisting of three classes of soils representing Severe, Moderate and Low erosion intensity because of their inter-class contrasts in estimated erosion rates from 137Cs tracing. The spectral separability of different erosion classes was first tested by a combined principal component and linear discriminant analysis (PCA-LDA) against laboratory hyperspectral data and then validated against Sentinel-2-derived broadband spectra. The results show that PCA-LDA produced excellent classification accuracy (Kappa coefficient > 0.9) for both data sources and even more so for Sentinel-2 spectra, highlighting the effectiveness of the multitemporal approach to extract bare soil pixels. Further investigations into the spectral curves enabled identification of distinctive spectral features representative of shifting soil albedo and biochemical composition due to erosion-induced SOC mobilization. A classification scheme comprising the spectral features was applied to the Sentinel-2 bare soil composite for pixel-wise soil erosion mapping, in which 15.9% of the cropland area was detected as erosion hotspots, while the Moderate class occupied 65.4%. Comparing the erosion map to a NDVI map demonstrated the negative impact of soil erosion on crop growth from a spatial perspective, highlighting the potential of the proposed approach to aid targeted cropland management for food security and climate.
Performance of Multiple Models for Estimating Rodent Activity Intensity in Alpine Grassland Using Remote Sensing
Authors: Dong Guang , Xian Wei , Shao Huaiyong , Shao Qiufang , Qi Jiaguo
Published: March 2023

Rodents are a vital part of the natural succession chain of the alpine grassland ecosystem, and rodent activities have an important impact on alpine grassland ecology. Moderate rodent population activities positively improve soil permeability, promote nutrient cycling, and promote biodiversity. However, too much rodent population or excessive activity intensity will bring negative effects on the ecological environment. Therefore, it is of great significance to accurately grasp the rodent activity intensity (RAI) in alpine grassland to cope with the changes in rodent populations and maintain the stability of the alpine grassland ecosystem. The Zoige alpine grassland was used as the study area in this study. In addition, UAV was sent to sample the rodent activity area in the alpine grassland. With the aid of field survey data, the surface information of rodent activity in the experimental area was identified, and the RAI index in the sample plot was calculated. Then, based on Sentinel-2A satellite remote sensing multi-spectral data and spectral index, multiple linear regression (MLR), multi-layer perceptron neural networks (MPL neural nets), random forest (RF), and support vector regression (SVR) were used to construct four models for RAI and Sentinel-2 datasets. The accuracy of the four models was compared and analyzed. The results showed that the RF model had the highest prediction accuracy (R2 = 0.8263, RWI = 0.8210, LCCC = 0.8916, RMSE = 0.0840, MAE = 0.0549), followed by the SVR model, the MLP neural nets model, and the MLR model. Overall, the nonlinear relationship between rodent activity intensity and satellite remote sensing images is obvious. Machine learning with strong nonlinear fitting ability can better characterize the RAI in alpine grassland. The RF model, with the best accuracy, can quantitatively estimate RAI in the alpine grassland, providing theoretical and technical support for monitoring RAI and rodent control in the alpine grassland.
Use-Specific Considerations for Optimising Data Quality Trade-Offs in Citizen Science: Recommendations from a Targeted Literature Review to Improve the Usability and Utility for the Calibration and Validation of Remotely Sensed Products
Authors: Schacher Alice , Roger Erin , Williams Kristen J. , Stenson Matthew P. , Sparrow Ben , Lacey Justine
Published: March 2023

The growth of citizen science presents a valuable potential source of calibration and validation data for environmental remote sensing at greater spatial and temporal scales, and with greater cost efficiency than is achievable by professional in situ reference-data collection alone. However, the frequent mismatch between in situ data-quality requirements for remote-sensing-product development and current data quality assurance in citizen science presents a significant challenge if widespread use of these complementary data sources is to be achieved. To evaluate the scope of this challenge, we conducted a targeted literature review into the nature of data-quality issues faced by citizen-science projects for routine incorporation into terrestrial environmental-monitoring systems. From the literature, we identify the challenges and trade-offs to inform best-practice implementation of data quality assurance in citizen-science projects. To assist practitioners in implementing our findings, we grouped these themes by stage of citizen-science project: (1) program planning and design; (2) participant engagement; (3) data collection; and (4) data processing. As a final step, we used our findings as the basis to formulate guiding questions that can be used to inform decision making when choosing optimal data-quality-improvement and assurance strategies for use of citizen science in remote-sensing calibration and/or validation. Our aim is to enhance future development of citizen-science projects for use with remote sensing in environmental monitoring.
Parametric Dynamic Distributed Containment Control of Continuous-Time Linear Multi-Agent Systems with Specified Convergence Speed
Authors: Yan Fei , Feng Siyi , Liu Xiangbiao , Feng Tao
Published: March 2023

This paper focuses on the distributed containment control of continuous-time linear multi-agent systems (MASs) with multiple leaders over fixed topology. A parametric dynamic compensated distributed control protocol is proposed in which both the information from the observer in the virtual layer and actual adjacent agents are employed. The necessary and sufficient conditions of the distributed containment control are derived based on the standard linear quadratic regulator (LQR). On this basis, the dominant poles are configured by using the modified linear quadratic regulator (MLQR) optimal control and Geršgorin’s circle criterion, hence the containment control with specified convergence speed of the MAS is achieved. Another main advantage of the proposed design is, in the case of virtual layer failure, by adjusting parameters the dynamic control protocol reduces to static, and the convergence speed can still be specified through the dominant pole assignment method combined with inverse optimal control. Finally, typical numerical examples are presented to demonstrate the effectiveness of theoretical results.
Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling
Authors: Korivand Soroush , Jalili Nader , Gong Jiaqi
Published: March 2023

Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR) and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Inertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. We also adapted the reward function by leveraging previous research on walking simulations for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means that the simulated human locomotion was more realistic. As a bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence was faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quickly and in a broader range of environments, with a better simulation performance.