bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Dynamic Characteristics of a SiC-Based Capacitive Micro-Accelerometer in Rarefied Air
Authors: Tian Xiang , Sheng Wei
Published: July 2022

In this study, we investigated the viscosity, squeeze-film damping, and a SiC-based capacitive micro-accelerometer in rarefied air. A specific expression for the effective viscosity coefficient of the air was derived, and when the air pressure drops from the standard atmospheric pressure, the viscosity of the air will decrease accordingly. Decreases in the air pressure and the viscosity of the air lead to the change in the squeeze-film air damping in the micro-accelerometer, and both the viscous damping force and the elastic damping force of the air film between the moving electrode plate and the fixed electrode plate will also decrease. The damping coefficient and relative damping ratio of the micro-accelerometer in rarefied air were calculated, which was also confirmed by simulations. The changes of the damping coefficient and the relative damping ratio of the system will directly affect the dynamic characteristics of the micro-accelerometer. When the air pressure in the working environment is below the standard atmospheric pressure, the micro-accelerometer will be in an underdamping state. With the decrease in the air pressure, the working bandwidth of the micro-accelerometer will decrease significantly, and the resonant phenomenon may appear. However, the decrease in the air pressure will not have a notable impact on the response time of the micro-accelerometer. Therefore, this work provides a theoretical basis for the study of the performance characteristics of a SiC-based capacitive accelerometer in rarefied air.
Thermal Energy Analysis of Projectiles during Ricochetting Using a Thermal Camera
Authors: Jasinski Marcin , Szczurowski Krzysztof , Wisniewski Adam , Badurowicz Przemyslaw , Bartkowiak Tadeusz , Tusnio Norbert
Published: July 2022

This paper presents the results of a study of the hazards of ground ignition and/or explosion when various small-calibre projectiles struck various solid materials placed on a test stand in environments at risk of ignition (fire) or explosion (ricochets and projectile penetration of obstacles). For projectile ricochetting tests, the following were used: an armour plate, concrete, sidewalk and granite slabs, etc., and various small-calibre projectiles: 7.62 × 51 mm SWISS PAP, 7.62 × 51T, 7.62 × 51 mm M80, 7.62 × 54R B-32, 7.62 × 54R LPS and .308 Win. Norma Ecostrike. Projectiles impacts were recorded with a high-speed camera (50,400 fps) and thermal cameras (660 fps) and (2615 fps). The ignition capability of solid flammable materials during projectile ricochetting was studied, and the temperatures and surface areas of isotherms were measured as a function of time. From the spherical distribution of thermal energy radiation in space, their volumes, masses of air occupying the studied area, masses of projectile disintegrating into fragments (after impact), thermal energies during projectile ricochetting, histograms of area temperatures and temperatures were calculated. This energy was compared with the minimum ignition energy of the selected gases and liquid vapours, and the ignition temperature were determined. The probabilities of some of the selected gases and liquid vapours which can ignite or cause an explosion were determined. The thermal energies of the 7.62 × 54R B-32 (3400–9500 J) and 7.62 × 51T (2000–3700 J) projectiles ricochetting on the Armox 600 plate was sufficient to ignite (explode) propane−butane gas. The thermal energy of 7.62 × 54R B-32 projectiles ricochets on the non-metallic components (800–1200 J) was several times lower than that of projectiles ricochets on an Armox 600 plate (3400–9500 J). This is due to the transfer of much of the kinetic energy to the crushing of these elements.
Linear Elastic Fracture Mechanics Assessment of a Gas Turbine Vane
Authors: Orenes Moreno Blanca , Bessone Andrea , Solazzi Simone , Vanti Federico , Bagnera Francesco , Riva Andrea , Botto Daniele
Published: July 2022

This work assesses the crack propagation at the most critical point of a second stage of a gas turbine blade by means of linear elastic fracture mechanics (LEFM). The most critical zone where the crack may nucleate, due to a combination of thermo-mechanical loads, is detected with an uncracked finite element (FE) model pre-analysis. Then the sub-modelling technique is used to obtain more precise results in terms of stresses within the area of interest. Simulations of the state of stress at the crack apex are performed through an FE model, using the Fracture Tool within ANSYS Workbench, and the stress intensity factors (SIFs) are determined accordingly. The Fracture Tool was previously verified on a simple model, and the results were compared with its analytical solution. Finally, the evaluation of the crack growth due to fatigue stress, creep, and oxidation is performed through in-house software called Propagangui. The crack behavior is estimated along with the component life. Results show an unexpected decrease in KI with increasing crack length and slowing of the crack growth rate with crack propagation. A detailed analysis of this behavior emphasizes that the redistribution of the stresses at the crack apex means that unstable propagation is not expected.
Fatigue Property and Small Crack Propagation Mechanism of MIG Welding Joint of 6005A-T6 Aluminum Alloy
Authors: Peng Zeng , Yang Shanglei , Wang Zhentao , Gao Zihao
Published: July 2022

In this study, metal inert gas welding (MIG) was applied to 4 mm thick 6005A-T6 aluminum alloy welding. Compared with other parts, the hardness of the weld zone (WZ) was the lowest, about 67 HV. There was the Softening in WZ, which might make WZ the weakest zone. Then, fatigue tests were carried out on MIG welded joints. All the fatigue specimens fractured at the weld toe of the lap joint, and the fracture was characterized by a cleavage fracture. Crack closure induced by oxide was observed during the steady propagation of the fatigue crack. Impurities hindered crack propagation, changed the direction of crack propagation, and appeared in stepped fatigue strip distribution morphology; in the process of the main crack propagation, the initiation and propagation of small cracks were easily restricted and hindered by the main crack, which slowed down the propagation rate and even stopped the propagation directly.
Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle
Authors: Magner Nicolás , Hardy Nicolás
Published: July 2022

This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known Meese–Rogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of BITCOIN, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and BITCOIN (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.
Understanding the Complexities of Eliminating Trans Fatty Acids: The Case of the Trans Fatty Acid Ban in Thailand
Authors: Samsiripong Weerapak , Phulkerd Sirinya , Pattaravanich Umaporn , Kanchanachitra Manasigan
Published: July 2022

Trans fatty acids (TFAs) have no known health benefits and are linked to an increased risk of noncommunicable diseases (NCDs). To eliminate TFAs from the food supply, the government of Thailand imposed a ban on partially hydrogenated oils (PHOs) in 2018. This study was aimed at analyzing the government policies and actions to eliminate TFAs in Thailand, focusing on policy content, context, process, and actors. This single-case qualitative study used a documentary review and interviews with 20 key policy actors. The data analysis was guided by thematic analysis based on the policy triangle framework. The results reveal that policy actors—government organizations, academics, civil society organizations, and the food industry—have different roles, interests, and influences with regard to eliminating TFAs in Thailand. Both formal and informal communication among policy actors aided in the policymaking process and the actions that followed. Changing perceptions of TFAs, the low intake of PHOs in Thailand, hype around trans fats, and trade dynamics shaped the government’s decision to impose the ban. As a result, the ban was selected to avoid the technical components of TFA elimination. This study suggests that eliminating TFAs in Thailand could be further enhanced by strengthening government actions in terms of enforcement and creating consumer awareness.
Impact on the Nutritional Status and Inflammation of Patients with Cancer Hospitalized after the SARS-CoV-2 Lockdown
Authors: Yárnoz-Esquíroz Patricia , Chopitea Ana , Olazarán Laura , Aguas-Ayesa Maite , Silva Camilo , Vilalta-Lacarra Anna , Escalada Javier , Gil-Bazo Ignacio , Frühbeck Gema , Gómez-Ambrosi Javier
Published: July 2022

Many studies have demonstrated that malnutrition has a negative impact on quality of life and mortality in patients with cancer. During the SARS-CoV-2 lockdown, dietary intake changes were detected in the Spanish population, reflecting an increase in the consumption of fruit, bread, flours, and eggs. The present study analyzed the nutritional status of 728 patients with cancer admitted once the SARS-CoV-2 lockdown finished, comparing it with the previous year as well as with mortality rates. The Malnutrition Universal Screening Tool (MUST) was applied in the first 24 h after admission. Age, gender, days of stay, circulating concentrations of albumin, cholesterol, C-reactive protein (CRP), lymphocytes, prealbumin, and mortality data were analyzed. Patients with cancer admitted between June and December of 2020 exhibited no statistical differences in BMI, age, or gender as compared to patients admitted in 2019. Statistically significant differences in nutritional status (p < 0.05), albumin (p < 0.001), and CRP (p = 0.005) levels regarding lockdown were observed in relation with a small non-significant reduction in mortality. In conclusion, following the SARS-CoV-2 lockdown, an improved nutritional status in cancer patients at admission was observed with a decrease in the percentage of weight loss and CRP levels together with an increase in albumin levels compared to oncological patients admitted the previous year.
Recommendations for the Diagnosis and Therapeutic Management of Hyperammonaemia in Paediatric and Adult Patients
Authors: Bélanger-Quintana Amaya , Arrieta Blanco Francisco , Barrio-Carreras Delia , Bergua Martínez Ana , Cañedo Villarroya Elvira , García-Silva María Teresa , Lama More Rosa , Martín-Hernández Elena , López Ana Moráis , Morales-Conejo Montserrat , Pedrón-Giner Consuelo , Quijada-Fraile Pilar , Stanescu Sinziana , Casanova Mercedes Martínez-Pardo
Published: July 2022

Hyperammonaemia is a metabolic derangement that may cause severe neurological damage and even death due to cerebral oedema, further complicating the prognosis of its triggering disease. In small children it is a rare condition usually associated to inborn errors of the metabolism. As age rises, and especially in adults, it may be precipitated by heterogeneous causes such as liver disease, drugs, urinary infections, shock, or dehydration. In older patients, it is often overlooked, or its danger minimized. This protocol was drafted to provide an outline of the clinical measures required to normalise ammonia levels in patients of all ages, aiming to assist clinicians with no previous experience in its treatment. It is an updated protocol developed by a panel of experts after a review of recent publications. We point out the importance of frequent monitoring to assess the response to treatment, the nutritional measures that ensure not only protein restriction but adequate caloric intake and the need to avoid delays in the use of specific pharmacological therapies and, especially, extrarenal clearance measures. In this regard, we propose initiating haemodialysis when ammonia levels are >200–350 µmol/L in children up to 18 months of age and >150–200 µmol/L after that age.
Temporal Association of Reduced Serum Vitamin D with COVID-19 Infection: Two Single-Institution Case–Control Studies
Authors: Gupta Diviya , Menon Sahit , Criqui Michael H. , Sun Bryan K.
Published: July 2022

(1) Background: Vitamin D supplementation has been proposed for the prevention and treatment of COVID-19, but it is not clear if reduced serum vitamin D predisposes individuals to COVID-19 and/or is a secondary consequence of infection. This study assessed the temporal association between serum vitamin D and COVID-19 with two single-institution case–control studies through the University of California San Diego (UCSD) Health System. (2) Methods: This study included patients who tested positive for COVID-19 from 1 January to 30 September 2020 with serum 25-hydroxy-vitamin D (25(OH)D) measured within 180 days of diagnosis. Patients were separated based on whether 25(OH)D was measured before (n = 107 cases, 214 controls) or after (n = 203 cases, 406 controls) COVID-19 diagnosis. COVID-19 infection status was the outcome variable in the pre-diagnosis study, whereas serum 25(OH)D level was the outcome variable in the post-diagnosis study. (3) Results: Serum 25(OH)D levels were not associated with the odds of subsequent COVID-19 infection (OR 1.0, 95% CI: 1.0 to 1.0, p = 0.98). However, COVID-19-positive individuals had serum 25(OH)D measurements that were 2.7 ng/mL lower than the controls (95% CI: −5.2 to −0.2, p = 0.03). (4) Conclusions: In our study population, serum 25(OH)D levels were not associated with the risk of acquiring COVID-19 infection but were reduced in subjects after COVID-19 infection. These results support the possibility that reduced serum 25(OH)D is a consequence and not a cause of COVID-19 infection.
Interactions between Vitamin D Genetic Risk and Dietary Factors on Metabolic Disease-Related Outcomes in Ghanaian Adults
Authors: Alathari Buthaina E. , Nyakotey David A. , Bawah Abdul-Malik , Lovegrove Julie A. , Annan Reginald A. , Ellahi Basma , Vimaleswaran Karani S.
Published: July 2022

The Ghanaian population is experiencing an upsurge in obesity and type 2 diabetes (T2D) due to rapid urbanization. Besides dietary factors, vitamin D-related genetic determinants have also been shown to contribute to the development of obesity and T2D. Hence, we aimed to examine the interactions between dietary factors and vitamin D-related genetic variants on obesity and T2D related outcomes in a Ghanaian population. Three hundred and two healthy Ghanaian adults (25–60 years old) from Oforikrom, Municipality in Kumasi, Ghana were randomly recruited and had genetic tests, dietary consumption analysis, and anthropometric and biochemical measurements of glucose, HbA1c, insulin, cholesterol, and triglycerides taken. A significant interaction was identified between vitamin D-GRS and fiber intake (g/day) on BMI (pinteraction = 0.020) where those who were consuming low fiber (≤16.19 g/d) and carrying more than two risk alleles for vitamin D deficiency (p = 0.01) had a significantly higher BMI. In addition, an interaction between vitamin D-GRS and fat intake (g/day) on HbA1c (total fat, pinteraction = 0.029) was found, where participants who had a lower total fat intake (≤36.5 g/d), despite carrying more than two risk alleles, had significantly lower HbA1c (p = 0.049). In summary, our study has identified novel gene–diet interactions of vitamin D-GRS with dietary fiber and fat intakes on metabolic traits in Ghanaian adults.
4-Substituted Thieno[3,2-d]pyrimidines as Dual-Stage Antiplasmodial Derivatives
Authors: Lagardère Prisca , Mustière Romain , Amanzougaghene Nadia , Hutter Sébastien , Franetich Jean-François , Azas Nadine , Vanelle Patrice , Verhaeghe Pierre , Primas Nicolas , Mazier Dominique , Masurier Nicolas , Lisowski Vincent
Published: July 2022

Malaria remains one of the major health problems worldwide. The increasing resistance of Plasmodium to approved antimalarial drugs requires the development of novel antiplasmodial agents that can effectively prevent and/or treat this disease. Based on the structure of Gamhepathiopine, a 2-tert-butylaminothieno[3,2-d]pyrimidin-4(3H)-one hit, active on the sexual and asexual stages of the parasite and thanked for the introduction of various substituents at position 4 of the thienopyrimidine core by nucleophilic aromatic substitution and pallado-catalyzed coupling reactions, a series of 4-substituted thieno[3,2-d]pyrimidines were identified as displaying in vitro activities against both the erythrocytic stage of P. falciparum and the hepatic stage of P. berghei. Among the 28 compounds evaluated, the chloro analogue of Gamhepathiopine showed good activity against the erythrocytic stage of P. falciparum, moderate toxicity on HepG2, and better activity against hepatic P. berghei parasites, compared to Gamhepathiopine.
Electrospray Deposition of Polyvinylidene Fluoride (PVDF) Microparticles: Impact of Solvents and Flow Rate
Authors: Joaquim Akinwunmi , Paul Omari , Ibezim Michael , Johnson Dewayne , Falconer April , Wu Ying , Williams Frances , Mu Richard
Published: July 2022

Polymeric microparticles have been shown to have great impacts in the area of drug delivery, biosensing, and tissue engineering. Electrospray technology, which provides a simple yet effective technique in the creation of microparticles, was utilized in this work. In addition, altering the electrospray experimental parameters such as applied voltage, flow rate, collector distance, solvents, and the polymer-solvent mixtures can result in differences in the size and morphology of the produced microparticles. The effects of the flow rate at (0.15, 0.3, 0.45, 0.6, 0.8, and 1 mL/h) and N, N-Dimethylformamide (DMF)/acetone solvent ratios (20:80, 40:60, 60:40, 80:20, 100:0 v/v) in the production of polyvinylidene fluoride (PVDF) microparticles were studied. Scanning electron microscopy (SEM) was used to observe changes in the morphology of the microparticles, and this revealed that a higher acetone to DMF ratio produces deformed particles, while flow rates at (0.3 and 0.45 mL/h) and a more optimized DMF to acetone solvent ratio (60:40 v/v) produced uniform spherical particles. We discovered from the Raman spectroscopy results that the electrosprayed PVDF microparticles had an increase in piezoelectric β phase compared to the PVDF pellet used in making the microparticles, which in its original form is α phase dominant and non-piezoelectric.
Analytical and Numerical Modeling of Degradation and Pyrolysis of Polyethylene: Measuring Aging with Thermogravimetry
Authors: Verho Tuukka , Vaari Jukka
Published: July 2022

Aging reactions due to heat and radiation cause chain scissions and cross-linking in cross-linked polyethylene (XLPE). We have developed theoretical and numerical graph models to study the evolution of the gel fraction and network properties during aging as well as the mass loss during thermogravimetric analysis (TGA). Our analytical and kinetic Monte Carlo (KMC) based models that combine degradation reactions and a simple vaporization model can quantitatively predict TGA curves for aged XLPE. Fitting the model to experimental TGA data yields the number of scission reactions, showing that thermogravimetry combined with our models can present a nondestructive aging characterization tool for lifetime prediction.
The Design of Sulfated Ce/HZSM-5 for Catalytic Decomposition of CF4
Authors: Zheng Xie , Chen Shijie , Liu Wanning , Xiang Kaisong , Liu Hui
Published: July 2022

CF4 has a global warming potential of 6500 and possesses a lifetime of 50,000 years. In this study, we modified the HZSM-5 catalyst with Ce and sulfuric acid treatment. The S/Ce/HZSM-5 catalyst achieves 41% of CF4 conversion at 500 °C, which is four times higher than that over Ce/HZSM-5, while the HZSM-5 exhibits no catalytic activity. The effects of modification were studied by using NH3-TPD, FT-IR of pyridine adsorption, and XPS methods. The results indicated that the modification, especially the sulfuric acid treatment, strongly increased the Lewis acidic sites, strong acidic sites, and moderate acidic sites on catalysts, which are the main active centers for CF4 decomposition. The mechanism of acidic sites increases by modification and CF4 decomposition is clarified. The results of this work will help the development of more effective catalysts for CF4 decomposition.
Using Sodium Polyacrylate to Gel-Spin Lignin/Poly(Vinyl Alcohol) Fiber at High Lignin Content
Authors: Biswas Manik Chandra , Ford Ericka
Published: July 2022

Lignin is the world’s most naturally abundant aromatic polymer, which makes it a sustainable raw material for engineered polymers and fiber manufacturing. Dry-jet gel-spinning was used to fabricate poly(vinyl alcohol) (PVA) fibers having 30% or more of the lignin biopolymer. To achieve this goal, 0.45 wt.% of aqueous sodium polyacrylate (SPA, at 0.55 wt.% solids) was added to spinning dopes of PVA dissolved in dimethylsulfoxide (DMSO). SPA served to enable the spinning of fibers having high lignin content (i.e., above 30%) while eliminating the aging of as-spun gel fiber prior to elevated temperature drawing. SPA impedes the migration of acetone soluble lignin from the skin of as-spun gel fibers, because SPA is insoluble in acetone, which is also a nonsolvent coagulant for PVA. PVA fibers having 30% lignin exhibited the highest tenacity of 1.3 cN/dtex (centinewton/decitex) and specific modulus 35.7 cN/dtex. The drawn fiber of 70% lignin to PVA, showed tenacity and specific modulus values of 0.94 cN/dtex and 35.3 cN/dtex, respectively. Fourier Transform Infrared (FTIR) spectroscopy showed evidence of hydrogen bonding between lignin and PVA among the drawn fibers. The modification of PVA/lignin dopes with SPA, therefore, allowed for the fabrication of gel-spun biobased fibers without the previously required step of gel aging.
A Compensation Method of Saturated Waveform for Space-Borne Laser Altimeter
Authors: Li Shaoning , Fan Xiufang , Pan Hongbo , Yu Qifan
Published: July 2022

Due to the difference in surface reflectivity, the laser measurement waveform data recorded in full waveform have a saturation phenomenon. When the signal is saturated, the echo waveform produces peak clipping and pulse spreading, which seriously restrict the accuracy of laser measurement results and the usability of data. Therefore, we conducted a ranging investigation on the “peak clipping” phenomenon of the saturated waveform and found a nonlinear time delay in the range, which is between the two extreme cases of saturated “dead time” and Gaussian fitting peak time as pulse signal reception time. Subsequently, based on the consistent relationship between the geometric characteristics of the high- and low-gain channels of the space-borne laser altimeter, we constructed a laser waveform saturation compensation model, namely, the laser pulse flight time delay compensation and the laser waveform peak intensity compensation, and carried out the data saturation compensation and validation with the dual-channel measurement data from the GaoFen-7 (GF-7) satellite. The experimental results showed that the saturation compensation model (SCM) proposed in this paper could restore the features of the saturated waveform signal and effectively improve the accuracy of the laser ranging. The accuracy of the laser waveform fitting result after saturation compensation improved from 0.7 ns (0.11 m) to 0.14 ns (0.02 m), which greatly improved the usability of the saturated laser measurement waveform data.
Forest Fire Segmentation from Aerial Imagery Data Using an Improved Instance Segmentation Model
Authors: Guan Zhihao , Miao Xinyu , Mu Yunjie , Sun Quan , Ye Qiaolin , Gao Demin
Published: July 2022

In recent years, forest-fire monitoring methods represented by deep learning have been developed rapidly. The use of drone technology and optimization of existing models to improve forest-fire recognition accuracy and segmentation quality are of great significance for understanding the spatial distribution of forest fires and protecting forest resources. Due to the spreading and irregular nature of fire, it is extremely tough to detect fire accurately in a complex environment. Based on the aerial imagery dataset FLAME, this paper focuses on the analysis of methods to two deep-learning problems: (1) the video frames are classified as two classes (fire, no-fire) according to the presence or absence of fire. A novel image classification method based on channel domain attention mechanism was developed, which achieved a classification accuracy of 93.65%. (2) We propose a novel instance segmentation method (MaskSU R-CNN) for incipient forest-fire detection and segmentation based on MS R-CNN model. For the optimized model, the MaskIoU branch is reconstructed by a U-shaped network in order to reduce the segmentation error. Experimental results show that the precision of our MaskSU R-CNN reached 91.85%, recall 88.81%, F1-score 90.30%, and mean intersection over union (mIoU) 82.31%. Compared with many state-of-the-art segmentation models, our method achieves satisfactory results on forest-fire dataset.
Improving the SSH Retrieval Precision of Spaceborne GNSS-R Based on a New Grid Search Multihidden Layer Neural Network Feature Optimization Method
Authors: Wang Qiang , Zheng Wei , Wu Fan , Zhu Huizhong , Xu Aigong , Shen Yifan , Zhao Yelong
Published: July 2022

The altimetry precision of conventional spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) is limited, and the error models are complicated. To compensate for the shortcomings of conventional methods, we present a new grid search multihidden layer neural network feature optimization method (GSMHLFO) for sea surface height (SSH) retrieval. Firstly, the GSMHLFO is constructed by combining the multihidden layer neural network, feature engineering, and a grid search algorithm. Moreover, the retrieval performance of the GSMHLFO and its sensitivity to various features are analyzed. By analyzing 14 feature sets with different information details, we concluded that the elevation, signal-to-noise ratio (SNR), atmospheric delay, and ocean wind speed can provide essential contributions to the SSH retrieval based on GSMHLFO. Secondly, the Technical University of Denmark 18 mean sea surface (DTU18 MSS), which is corrected by the TPXO8 global tide model, was used to verify the GSMHLFO. The number of hidden layers and neurons was optimized using the grid search algorithm. The experimental results show that the proposed GSMHLFO with four hidden layers and 200 neurons per layer has a better retrieval performance. Compared with DTU18, the mean absolute difference (MAD), the root mean square error (RMSE), and the Pearson correlation coefficient (PCC) equal 4.23 m, 5.94 m, and 0.98, respectively. The retrieval precision obtained is significantly improved compared to that reported in the literature for the TDS-1 SSH retrieval. Finally, the retrieval performance of the GSMHLFO and the traditional HALF single-point retracking method were compared. The precision of GSMHLFO is higher than that of traditional retracking method according to MAD, RMSE, and PCC, which are increased by 32.86, 25.00, and 8.99%. The GSMHLFO will provide innovative theoretical and methodological support for the high-precision SSH retrieval of GNSS-R altimetry satellites in the future.
Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration
Authors: Ikkala Lauri , Ronkanen Anna-Kaisa , Ilmonen Jari , Similä Maarit , Rehell Sakari , Kumpula Timo , Päkkilä Lassi , Klöve Björn , Marttila Hannu
Published: July 2022

Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.
A New Coherence Detection Method for Mapping Inland Water Bodies Using CYGNSS Data
Authors: Wang Ji , Hu Yufeng , Li Zhenhong
Published: July 2022

Inland water is an important part of the Earth’s water cycle. Mapping inland water is vital for understanding surface hydrology and climate change. Spaceborne global navigation satellite systems reflectometry (GNSS-R) has been proven to be an effective technique to detect inland water bodies. This paper proposes a new method to map inland water bodies using the delay-Doppler map (DDM) measurements provided by the GNSS-R platform Cyclone GNSS (CYGNSS). In this new method, we develop a refined power ratio to identify the coherence in DDM caused by the inland water. Processed with an image segmentation method, the refined power ratio is then applied to discriminate the permanent inland water bodies from the land. Using CYGNSS data over the Amazon Basin and the Congo Basin in 2020, we successfully generated water masks with a spatial resolution of 0.01°. Compared with the reference optical water masks, the overall detection accuracy in the Amazon Basin is 94.48% and the water detection accuracy is 92.23%, and the corresponding accuracies in the Congo Basin are 96.12% and 93.16%, respectively. Compared with the previous DDM power-spread detector (DPSD) method, the new method’s false alarms and misses in the Amazon Basin are reduced by 17.1% and 9.1%, respectively, while the false alarms and misses in the Congo Basin are reduced by 10.2% and 22%, respectively. Moreover, our method is proven to be useful for detecting short-term flood inundation.
A Classifying-Inversion Method of Offshore Atmospheric Duct Parameters Using AIS Data Based on Artificial Intelligence
Authors: Han Jie , Wu Jiaji , Zhang Lijun , Wang Hongguang , Zhu Qinglin , Zhang Chao , Zhao Hui , Zhang Shoubao
Published: July 2022

Atmospheric duct parameters inversion is an important aspect of microwave-band radar and communication system performance evaluation. AIS (Automatic Identification System) is one of the signal sources used for atmospheric duct parameters inversion. Before the inversion of atmospheric duct parameters, determining the type of atmospheric duct plays an important role in the inversion results, but the current inversion methods ignore this point. We outlined a classifying-inversion method of atmospheric duct parameters using AIS signals combined with artificial intelligence. The method consists of an atmospheric duct classification model and a parameter inversion model. The classification model judges the type of atmospheric duct, and the inversion model inverts the atmospheric duct parameters according to the type of atmospheric duct. Our findings demonstrated that the accuracy of the atmospheric duct classification model based on deep neural network (DNN) even exceeds 97%, and the atmospheric duct parameters inversion model has better inversion accuracy than that of the traditional method, thereby illustrating the effectiveness and accuracy of this novel method.
Physics-Driven Deep Learning Inversion with Application to Magnetotelluric
Authors: Liu Wei , Wang He , Xi Zhenzhu , Zhang Rongqing , Huang Xiaodi
Published: July 2022

Due to the strong capability of building complex nonlinear mapping without involving linearization theory and high prediction efficiency; the deep learning (DL) technique applied to solve geophysical inverse problems has been a subject of growing interest. Currently, most DL-based inversion approaches are fully data-driven (namely standard deep learning), the performance of which largely depends on the training sample sets. However, due to the heavy burden of time and computational resources, it can be challenging to supply such a massive and exhaustive training dataset for generic realistic exploration scenarios and to perform network training. In this work, based on the recent advances in physics-based networks, the physical laws of magnetotelluric (MT) wave propagation is incorporated into a purely data-driven DL approach (PlainDNN) and thus builds a physics-driven DL MT inversion scheme (PhyDNN). In this scheme, the forward operator modeling MT wave propagation is integrated into the network training loop, in the form of minimizing a hybrid loss objective function composed of the data-driven model misfit and physics-based data misfit, to guide the network training. Consequently, the proposed PhyDNN method will take the advantage of the fully data-driven DL and conventional physics-based deterministic methods, allowing it to deal with complex realistic exploration scenarios. Quantitative and qualitative analysis results demonstrate that the PhyDNN can honor the physical laws of the MT inverse problem, and with other conditions unchanged, the PhyDNN outperforms the PlainDNN and the classical deterministic Occam inversion method. When processing field data, the PhyDNN method yields considerably impressive inversion results compared to the Occam method, and the corresponding simulated MT responses agree well with the real measurements, which confirms the effectiveness and applicability of the PhyDNN method.
CharAs-CBert: Character Assist Construction-Bert Sentence Representation Improving Sentiment Classification
Authors: Chen Bo , Peng Weiming , Song Jihua
Published: July 2022

In the process of semantic capture, traditional sentence representation methods tend to lose a lot of global and contextual semantics and ignore the internal structure information of words in sentences. To address these limitations, we propose a sentence representation method for character-assisted construction-Bert (CharAs-CBert) to improve the accuracy of sentiment text classification. First, based on the construction, a more effective construction vector is generated to distinguish the basic morphology of the sentence and reduce the ambiguity of the same word in different sentences. At the same time, it aims to strengthen the representation of salient words and effectively capture contextual semantics. Second, character feature vectors are introduced to explore the internal structure information of sentences and improve the representation ability of local and global semantics. Then, to make the sentence representation have better stability and robustness, character information, word information, and construction vectors are combined and used together for sentence representation. Finally, the evaluation and verification are carried out on various open-source baseline data such as ACL-14 and SemEval 2014 to demonstrate the validity and reliability of sentence representation, namely, the F1 and ACC are 87.54% and 92.88% on ACL14, respectively.
The Development Trend of the Occupational Health and Safety in the Context of ISO 45001:2018
Authors: Šolc Marek , Blaško Peter , Girmanová Lenka , Kliment Juraj
Published: July 2022

The main task of safety and health at work is to protect the most important thing we have, the health of each of us. Employers are able to anticipate and prevent risks by properly implemented occupational safety and health management systems. The basic task of the article is to describe the history of safety management systems to identify the state of implementation of the ISO 45001 system in the world. Subsequently, the article describes the ISO 45001 standard from the perspective of the PDCA cycle and describes the benefits and importance of implementing the ISO 45001 standard. The conclusion of the article deals with the development trend of the occupational health and safety management system according to STN ISO 45001:2019 in the context of occupational accidents in the conditions of the Slovak Republic.
Conflict-Sensitive Climate Change Adaptation: A Review
Authors: Nadiruzzaman Md , Scheffran Jürgen , Shewly Hosna J. , Kley Stefanie
Published: July 2022

Climate change adaptation (CCA) evolved in the global policy framework in the early 1990s. However, it began to flourish about a decade later through a subsequent development of institutions, policies and supporting financial mechanisms. Various adaptation approaches and development practices have been evolving over the last couple of decades through a process of scrutiny, debates, and critiques. One such recent approach is called conflict-sensitive adaptation, which encompasses concepts to understand potential conflict-inflicting elements and peacebuilding aspects of adaptation interventions. This paper examines 35 peer-reviewed research articles that have analysed field data with the notion of conceptualising conflict-sensitive CCA initiatives. Emerging key results were presented and discussed in different academic forums to stimulate peer reflections and debates. We found that the understanding of conflict-sensitive adaptation has its universality in engaging with diverse stakeholders. However, practicalities were different in the cases of the global north and the global south. In the global south, there is a concentration of research in areas of pre-existing conflict in Africa and Asia, where climate change links are often assumed from IPCC reports in order to compensate for the unavailability of field data. However, from the perspective of adaptation politics and governance, there is a concerted urge for the emancipatory participation of local and marginalised populations. We argue for a need to pretest adaptation projects through a conflict lens. Decolonising the adaptation and understanding of local geography is critical in such planning.
A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events
Authors: Imanian Hanifeh , Hiedra Cobo Juan , Payeur Pierre , Shirkhani Hamidreza , Mohammadian Abdolmajid
Published: July 2022

Soil temperature is a fundamental parameter in water resources and irrigation engineering. A cost-effective model that can accurately forecast soil temperature is urgently needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature predictions. In the present study, attempts are made to deliver a comprehensive and detailed assessment of the performance of a wide range of AI approaches in soil temperature prediction. In this regard, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to more advanced AI techniques, such as multi-layer perceptron and deep learning, are taken into account. Meanwhile, great varieties of land and atmospheric variables are applied as model inputs. A sensitivity analysis was conducted on input climate variables to determine the importance of each variable in predicting soil temperature. This examination reduced the number of input variables from 8 to 7, which decreased the simulation load. Additionally, this showed that air temperature and solar radiation play the most important roles in soil temperature prediction, while precipitation can be neglected in forecast AI models. The comparison of soil temperature predicted by different AI models showed that deep learning demonstrated the best performance with R-squared of 0.980 and NRMSE of 2.237%, followed by multi-layer perceptron with R-squared of 0.980 and NRMSE of 2.266%. In addition, the performance of developed AI models was evaluated in extremely hot events since heat warnings are essential to protect lives and properties. The assessment showed that deep learning and multi-layer perceptron methods still have the best prediction. However, their R-squared decreased to 0.862 and 0.859, and NRMSE increased to 6.519% and 6.601%, respectively.
A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System
Authors: Das Arup , Dawn Subhojit , Gope Sadhan , Ustun Taha Selim
Published: July 2022

Electricity demand is sharply increasing with the growing population of human beings. Due to financial, social, and political barriers, there are lots of difficulties when building new thermal power plants and transmission lines. To solve this problem, renewable energy sources and flexible AC transmission systems (FACTS) can operate together in a power network. Renewable energy sources can provide additional power to the grid, whereas FACTS devices can increase the thermal limit of existing transmission lines. It is always desirable for an electrical network to operate under stable and secure conditions. The system runs at risk if any abnormality occurs in the generation, transmission, or distribution sections. This paper outlines a strategy for reducing system risks via the optimal operation of wind farms and FACTS devices. Here, a thyristor-controlled series compensator (TCSC) and a unified power flow controller (UPFC) have both been considered for differing the thermal limit of transmission lines. The impact of the wind farm, as well as the combined effect of the wind farm and FACTS devices on system economy, were investigated in this work. Both regulated and deregulated environments have been chosen to verify the proposed approach. Value at risk (VaR) and cumulative value at risk (CVaR) calculations were used to evaluate the system risk. The work was performed on modified IEEE 14 bus and modified IEEE 30-bus systems. A comparative study was carried out using different optimization techniques, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Honey Badger Algorithm (HBA), and Sequential Quadratic Programming (SQP) to check the effect of renewable integration in the regulated and deregulated power systems in terms of system risk and operating cost.
Design for Social Innovation: A Systemic Design Approach in Creative Higher Education toward Sustainability
Authors: Peng Fanke , Altieri Benjamin , Hutchinson Todd , Harris Anthony J. , McLean Daniel
Published: July 2022

The role and responsibilities of our creative Higher Education are evolving in our ever-changing society. Systemic design thinking equips our design students with the means to promote sustainable development objectives via engagement, community building and discourse. This study reflects on interdisciplinary resource recovery projects for the City of Adelaide (CoA) during a final-year systemic design course in the Product Design programme at the University of South Australia. Since 2015, the core design team has collaborated with external partners outside of HE sectors, including NGOs, local councils, the Australian Capital Territory (ACT) government and the South Australia (SA) government, to promote social innovation towards sustainability. Systemic design is one of the core courses of the Bachelor of Design, Product Design programme within the faculty of Creative, University of South Australia. In this course, system thinking combined with project-based learning (PBL) was adapted and illustrated to demonstrate good systemic design practice for social innovation. This study was focussed on how we can collaborate on a variety of interdisciplinary projects to contribute to the realisation of the United Nations’ (UN) Sustainable Development Goals (SDGs) from a human-centred systemic design perspective.
Litterfall and Associated Macrozoobenthic of Restored Mangrove Forests in Abandoned Aquaculture Ponds
Authors: Pradisty Novia Arinda , Sidik Frida , Bimantara Yuntha , Susetya Ipanna Enggar , Basyuni Mohammad
Published: July 2022

Mangrove restoration projects are now widely established, aiming to regain the carbon benefit of the mangrove ecosystem that is essential for climate change mitigation. This study aimed to investigate mangrove litter as the source of carbon in restored mangrove forests in Perancak Estuary, Bali, Indonesia, which previously experienced substantial mangrove loss due to shrimp aquaculture development. We assessed the production and decomposition of mangrove litter and associated macrozoobenthic biodiversity in restored forests with plantation age ≥14 years and intact mangrove forests as the reference. The monthly production of three groups of mangrove litter (leaf, reproductive, and wood) was assessed over 12 months. A leaf litter decomposition experiment was performed to inspect the interspecific and disturbance history variation in organic matter formation among four major mangrove species: Rhizophora apiculata, Bruguiera gymnorhiza, Avicennia marina, and Sonneratia alba. Our results showed that annual litterfall production from restored and intact mangroves in Perancak Estuary were 13.96 and 10.18 Mg ha−1 year−1, which is equivalent to approximately 6282 and 4581 kg C ha−1 year−1 of annual litterfall carbon sink, respectively. Although restored mangroves had significantly higher plant litterfall production than intact mangroves, no significant difference was detected in leaf litter decomposition and macrozoobenthic biodiversity between these forest types.