bluuebunny/crossref_metadata_2025 huggingface dataset:
Synergistic Effect of Supercritical and Ultrasound-Assisted Ginger (Zingiber officinale Roscoe) Extracts
Authors: Žitek Taja , Kučuk Nika , Postružnik Vesna , Leitgeb Maja , Knez Željko , Primožič Mateja , Marevci Maša Knez
Published: October 2022

Proper processing of natural material is crucial to obtain an extract with high content of biologically active components. Dried, grinded ginger roots were extracted by ultrasonic method and supercritical extraction with CO2. The aim of the study was to determine if a mixture of the two types of extracts attained by different methods and solvents exhibits better bioavailability than each extract itself. Therefore, both extracts were analytically evaluated and then mixed in a ratio of 1:1. The supercritical extract (SCG extract) and the mixed extract (mixG extract) had high antioxidant activity (78% and 73%) and total phenols (827 mg/g ext. and 1455 mg/g ext.), which is also consistent with the levels of gingerol (303 mg/g ext. and 271 g/g ext.) and shogaol (111 mg/g ext. and 100 g/g ext.) in the extracts. In comparison to both pure extracts higher levels of total phenols were found in the extract mixG. This could be the reason for the significant inhibition of melanoma cells and antimicrobial potential (against Staphylococcus aureus, Escherichia coli, and Candida albicans). The combination of the extracts resulted in a significant increase in the inhibition of selected microbial and melanoma cells WM-266-4 compared to the control. Cell viability decreased below 60% when mixG extract was applied. Antimicrobial activity has been confirmed.
Arbuscular Mycorrhizal Symbiosis: A Strategy for Mitigating the Impacts of Climate Change on Tropical Legume Crops
Authors: Loo Wan Teng , Chua Kah-Ooi , Mazumdar Purabi , Cheng Acga , Osman Normaniza , Harikrishna Jennifer Ann
Published: October 2022

Climate change is likely to have severe impacts on food security in the topics as these regions of the world have both the highest human populations and narrower climatic niches, which reduce the diversity of suitable crops. Legume crops are of particular importance to food security, supplying dietary protein for humans both directly and in their use for feed and forage. Other than the rhizobia associated with legumes, soil microbes, in particular arbuscular mycorrhizal fungi (AMF), can mitigate the effects of biotic and abiotic stresses, offering an important complementary measure to protect crop yields. This review presents current knowledge on AMF, highlights their beneficial role, and explores the potential for application of AMF in mitigating abiotic and biotic challenges for tropical legumes. Due to the relatively little study on tropical legume species compared to their temperate growing counterparts, much further research is needed to determine how similar AMF–plant interactions are in tropical legumes, which AMF species are optimal for agricultural deployment and especially to identify anaerobic AMF species that could be used to mitigate flood stress in tropical legume crop farming. These opportunities for research also require international cooperation and support, to realize the promise of tropical legume crops to contribute to future food security.
Crop Proteomics under Abiotic Stress: From Data to Insights
Authors: Kausar Rehana , Wang Xin , Komatsu Setsuko
Published: October 2022

Food security is a major challenge in the present world due to erratic weather and climatic changes. Environmental stress negatively affects plant growth and development which leads to reduced crop yields. Technological advancements have caused remarkable improvements in crop-breeding programs. Proteins have an indispensable role in developing stress resilience and tolerance in crops. Genomic and biotechnological advancements have made the process of crop improvement more accurate and targeted. Proteomic studies provide the information required for such targeted approaches. The crosstalk among cellular components is being analyzed by subcellular proteomics. Additionally, the functional diversity of proteins is being unraveled by post-translational modifications during abiotic stress. The exploration of precise cellular responses and the networking among different cellular organelles help in the prediction of signaling pathways and protein–protein interactions. High-throughput mass-spectrometry-based protein studies are now possible due to incremental advancements in mass-spectrometry techniques, sample protocols, and bioinformatic tools as well as the increasing availability of plant genome sequence information for multiple species. In this review, the key role of proteomic analysis in identifying the abiotic-stress-responsive mechanisms in various crops was summarized. The development and availability of advanced computational tools were discussed in detail. The highly variable protein responses among different crops have provided a wide avenue for molecular-marker-assisted genetic buildup studies to develop smart, high-yielding, and stress-tolerant varieties to cope with food-security challenges.
Selection and Validation of the Most Suitable Reference Genes for Quantitative Real-Time PCR Normalization in Salvia rosmarinus under In Vitro Conditions
Authors: Bharati Rohit , Sen Madhab Kumar , Kumar Ram , Gupta Aayushi , Sur Vishma Pratap , Melnikovová Ingrid , Fernández-Cusimamani Eloy
Published: October 2022

Salvia rosmarinus L. (rosemary) is known to have a wide range of pharmacological effects including antidiabetic, anticarcinogenic, and antitumorigenic properties owing to its secondary metabolites. Studies aiming to elevate these metabolites have utilized various elicitors and stresses under in vitro conditions, although underlying molecular mechanisms remain unexplored. Gene expression studies using RT-qPCR might provide valuable information regarding how plant and plant cells interact and perceive various treatments and elicitors. However, despite being able to calculate accurate fold changes, the accuracy of the RT-qPCR data highly depends on the expression of reference genes. To the best of our knowledge, there is no information available on the stable reference genes in rosemary under in vitro conditions. Thus, in this paper, we assessed the stability of seven commonly used reference genes under different elicitor and stress conditions using RT-qPCR. Thereafter, the five most commonly used software and algorithms (comparative ΔCt, BestKeeper, NormFinder, geNorm, and RefFinder) were used to rank the candidates based on their expression stabilities. In conclusion, we recommend using a combination of F1-ATPase, ATP synthase and ACCase to normalize the gene expression experiments in rosemary under in vitro conditions. The selected reference genes were verified using 4-coumarate-CoA ligase, a pharmacologically important gene, whose expression might alter under nanoparticle treatment. Additionally, reference genes for several plant tissues, elicitors, and stresses are also proposed. The conclusions obtained from this current study will accelerate the future molecular work in S. rosmarinus and other related species.
Special Issue: Synthesis, Processing, Structure and Properties of Polymer Materials
Authors: Puszka Andrzej , Podkościelna Beata
Published: October 2022

Polymeric materials are widely used in many different technical fields [...]
Synergistic Effect of Two Plasticizers on Thermal Stability, Transparency, and Migration Resistance of Zinc Arginine Stabilized PVC
Authors: Shi Yanqin , Yao Yuchen , Lu Songyan , Chen Lukai , Chen Si , He Huiwen , Ma Meng , Wang Xu
Published: October 2022

The effect of different plasticizers on thermal stability, transparency, and migration resistance of the PVC stabilized with zinc arginine [Zn(Arg)2] was investigated. The thermal stability, migration resistance, and transparency of PVC with tributyl citrate (TBC) were better than PVC with dioctyl phthalate (DOP) characterized by oven aging method, migration test, and near infrared-visible-ultraviolet spectrophotometer. At the same time, the longer the carbon chain in citric acid esters, the better the thermal stability and transparency of PVC sample. The hydroxyl group in citric acid esters is helpful to improve the thermal stability of PVC samples. However, the elongation at break and Tg value of PVC containing DOP were very close to those of PVC containing TBC. The calculation results of Hansen solubility parameters also illustrated that DOP had better compatibility with PVC than TBC. Therefore, the excellent transparency and thermal stability of TBC plasticized PVC were attributed to the good compatibility between TBC and Zn(Arg)2, which was verified by the solubility test. Lastly, the mixture of dioctyl terephthalate (DOTP) and TBC was used as plasticizers for Zn(Arg)2 stabilized PVC. When the ratio of TBC and DOTP was 1:1, the transparency, thermal stability, and migration resistance of Zn(Arg)2 stabilized PVC samples were better than those of PVC plasticized by DOP or TBC alone. The mechanism was that the compatibility between Zn(Arg)2 and PVC was greatly improved by the synergetic effect of TBC and DOTP, resulting in the improvement of thermal stability, migration resistance, and transparency of PVC samples.
Synthesis and Characterization of Schiff Base Polymers via Metal Coordination and Its Application in Infrared Stealth Coating
Authors: Li Xiangyu , Zong Lishuai , Li Weijie , Wang Yibo , Wang Jinyan , Jian Xigao
Published: October 2022

In order to reduce the infrared emissivity to meet the requirements of modern warfare for infrared stealth materials, we prepared the polymers containing Schiff base moieties using polyetheramine and 2,6-pyridinedicarboxaldehyde by solution polycondensation and coordinated with Ni2+, Cu2+, and Sm3+ ions to prepare organic coatings. The structure and the thermal and mechanical properties of the coatings were studied in detail. Meanwhile, the effect of the conductivity change of coordination polymers on infrared emissivity was studied systematically. The results showed the polymer coordinated with Sm3+ ions had the lowest energy band gap, which was 2.99 eV, and the best electrical conductivity of 3.54 × 10−4 S/cm compared with Ni2+ and Cu2+ coordination polymers. The infrared emissivity was the lowest in the 2–22 μm infrared waveband range, which reached 0.58, suggesting the polymers containing Schiff base moieties and their coordination polymers may have a great potential to be applied as infrared stealth materials in military applications.
Beneficiation of Magnetically Separated Iron-Containing Ore Waste
Authors: Chokin Kanat , Yedilbayev Abdraman , Yugai Vladimir , Medvedev Alexandr
Published: October 2022

Rough processing of iron ore employs dry methods which means that equipment is tuned to process large particles, but fine magnetic material less than a few tenths of a millimeter in size is not separated as efficiently. The relevance of this study is determined by the fact that dry beneficiation waste contains recoverable iron-bearing magnetite of commercial value. Commercial justification of waste beneficiation is associated with mining and grinding costs that are already included in the prime cost of the commercial concentrate. The future of tailings retreatment prospects depends on technology and efficiency of the employed equipment, the development of which is the subject of this paper. At first stage, fine iron is recovered by air sizing, with pitched curtain air classifiers embedding simple design and high performance. Powder materials were magnetically separated by a manufactured drum-type separator in which, to increase the separation efficiency, the process was performed at increased drum rotation speeds using Nd-Fe-B magnets and a drum made of electrically non-conductive materials. The separator performance was determined for various rotation speeds of the drum. Research has proven that a multi-stage magnetic separation with a consequent increase in drum rpm is reasonable. A new cascade separator was manufactured and tested for this purpose. It is shown that iron-containing ore tailings beneficiation is optimal without any additional grinding.
Chemical Constituents, Quantitative Analysis, Anti-SARS-CoV-2 and Antioxidant Activities of Herbal Formula “Ping An Fang Yu Yin”
Authors: Tsai Yun-Chen , Lee Ming-Chung , Hsieh Yu-Hui , Wang Kun-Teng , Chen Chao-Yu , Chuang Wu-Chang , Chen Jih-Jung
Published: October 2022

COVID-19 is a global pandemic infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The herbal formula, Ping An Fang Yu Yin (PAFYY), has been used to prevent respiratory viral infections for many years. This study aims to evaluate the effect of PAFYY on SARS-CoV-2 infection, oxidative stress, and inflammation via in vitro, investigate the chemical composition by full constituent quantitative analysis, and verify its anti-viral potential against SARS-CoV-2 using in silico. In this study, a total of eleven compounds, twenty amino acids, saccharide compositions, and trace elements were found and quantitatively determined by chromatographic techniques. PAFYY displayed free radical scavenging activity (DPPH, SC50: 1.24 ± 0.09 mg/mL), SOD activity (68.71 ± 1.28%), inhibition of lipoxygenase activity (75.96 ± 7.64 mg/mL) and interfered the interaction of SARS-CoV-2 spike protein and angiotensin-converting enzyme 2 (48.04 ± 3.18%). Furthermore, in-silico analysis results supported that liquiritin, 3,5-dicaffeoylquinic acid, and luteolin-7-O-glucoside with the highest affinity between SARS-CoV-2 RBD and human angiotensin-converting enzyme II (hACE2) receptor. Our findings suggest that PAFYY has the potential for anti-SARS-CoV-2 infection, anti-oxidation stress, and anti-inflammation, and may be used as supplements for amelioration or prevention of COVID-19 symptoms, as well as the representative compounds can be used for quality control of PAFYY in the future.
The Cult of Our Lady of Fátima—Modern Catholic Devotion in an Age of Nationalism, Colonialism, and Migration
Authors: Von Klimo Arpad
Published: October 2022

In 1917, in the context of extreme anxiety of a rural Catholic population during a period of war, revolutionary upheaval, and anticlerical politics, the apparition of St Mary to three children near the Portuguese village of Fátima stirred up emotions that have created a cult that is still attracting hundreds of thousands of pilgrims each year and that has found followers all over the world. How did this happen? One has to look at the history of changing meanings related to the cult of Our Lady of Fátima, its integrative energy, and the global spread of the cult. The fact that, most recently, immigrants with Hindu and Muslim background are also among the pilgrims to Fátima speaks of the unpredictable cross-cultural and cross-religious possibilities of this cult, in which gender aspects of a female deity play an important role. This paper will analyze the various periods of the cult from its beginning in 1917 and the roles of secularism, the Salazar regime, Colonialism, the Cold War, and migration from and (later) to Portugal, in order to find insights into a fascinating global cult and how it has changed and adapted to a society that has developed a new attitude towards traditional Catholicism. While the history of the Cult of Fátima has been studied before, this article seeks to introduce a global and, at the same time, long-term historical perspective.
Real-Time Source Modeling of the 2022 Mw 6.6 Menyuan, China Earthquake with High-Rate GNSS Observations
Authors: Li Zhicai , Zang Jianfei , Fan Shijie , Wen Yangmao , Xu Caijun , Yang Fei , Peng Xiuying , Zhao Lijiang , Zhou Xing
Published: October 2022

On 7 January 2022, a Mw 6.6 earthquake struck Menyuan County in the Qinghai province of China and the earthquake caused severe damage to infrastructures. In this study, the performance of the high-rate global navigation satellite system (GNSS) on real-time source modeling of the 2022 Mw 6.6 Menyuan earthquake was validated. We conducted the warning magnitude calculation, centroid moment tensor (CMT) inversion, and static fault slip distribution inversion using displacements collected from 14 1-Hz GNSS stations. Our results indicate that the warning magnitude derived from the peak ground displacement (PGD) first exceeds Mw 6.0 approximately 9 s after the earthquake and tends to be stable after about 45 s. The derived finally stable magnitude is Mw 6.5, which is near the USGS magnitude of Mw 6.6. Based on the inverted CMT and static fault slip distribution results, it can be determined that the 2022 Menyuan earthquake is a left-lateral strike-slip event after about 20 s of the earthquake. Although the fault slips, inverted with the 30-s smoothed coseismic offsets, are unstable after about 40 s, all the inverted slip models after that time present the obvious surface rupture and the most fault motions are concentrated between the depth of 0 km and 8 km. Compared with the results inverted with the 30-s smoothed coseismic offsets, the CMT and fault slips inverted with the 70-s smoothed coseismic offsets are more stable. The results obtained in this study indicate that the high-rate GNSS has the potential to be used for real-time source modeling for earthquakes with a magnitude less than 7; the stability of the inverted CMT and fault slips can be improved by using the coseismic offsets averaged by a relatively long-time sliding window.
The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities: Malaria as an Example
Authors: Morlighem Camille , Chaiban Celia , Georganos Stefanos , Brousse Oscar , Van de Walle Jonas , van Lipzig Nicole P. M. , Wolff Eléonore , Dujardin Sébastien , Linard Catherine
Published: October 2022

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements.
A Novel Method to Derive Exospheric Temperatures from Swarm Thermospheric Densities during Quiet Times
Authors: Lin Yingyun , Xu Jiyao , van den IJssel Jose , Yuan Wei
Published: October 2022

One of the most important parameters in the atmosphere, the neutral temperature, becomes difficult to measure at high altitudes such as the exosphere. Therefore, based on the assumption of static equilibrium and isothermal atmosphere, a new method was developed to derive quiet-time exospheric temperatures using neutral atmospheric densities from 470 km to 550 km, which were obtained from the Swarm satellites. The derived neutral temperatures were obtained at an altitude of approximately 500 km in the low and middle latitudes from mid-April 2014 to early August 2014. The results were evaluated with nighttime temperatures from ground-based Fabry Perot Interferometers at 250 km. The mean deviation between the derived temperatures and FPI was 30.80 K and the standard deviation of the mean was 106.20 K. The diurnal variations of the exospheric temperatures, which tended to reach their maximum in the late afternoon, were in good consistency with the NRL-MSISE00 model simulations. This novel method performs well at low and middle latitudes. The greatest source of uncertainty is the mean molecular mass, which is also not well determined at these high altitudes. Hence, a measurement called “Satellite-tethered Mass Spectrometer Detection” was proposed to address this.
Research on Intelligent Crack Detection in a Deep-Cut Canal Slope in the Chinese South–North Water Transfer Project
Authors: Hu Qingfeng , Wang Peng , Li Shiming , Liu Wenkai , Li Yifan , Lu Weiqiang , Kou Yingchao , Wei Fupeng , He Peipei , Yu Anzhu
Published: October 2022

The Chinese South–North Water Transfer Project is an important project to improve the freshwater supply environment in the Chinese interior and greatly alleviates the water shortage in the Chinese North China Plain; its sustainable, healthy, and safe operation guarantees ecological protection and economic development. However, due to the special expansive soil and deep excavation structure, the first section of the South–North Water Transfer Project canal faces serious disease risk directly manifested by cracks in the slope of the canal. Currently, relying on manual inspection not only consumes a lot of human resources but also unnecessarily repeats and misses many inspection areas. In this paper, a monitoring method combining depth learning and Uncrewed Aerial Vehicle (UAV) high-definition remote sensing is proposed, which can detect the cracks of the channel slope in time and accurately and can be used for long-term health inspection of the South–North Water Transfer Project. The main contributions are as follows: (1) aiming at the need to identify small cracks in reinforced channels, a ground-imitating UAV that can obtain super-clear resolution remote-sensing images is introduced to identify small cracks on a complex slope background; (2) to identify fine cracks in massive images, a channel crack image dataset is constructed, and deep-learning methods are introduced for the intelligent batch identification of massive image data; (3) to provide the geolocation of crack-extraction results, a fast field positioning method for non-modeled data combined with navigation information is investigated. The experimental results show that the method can achieve a 92.68% recall rate and a 97.58% accuracy rate for detecting cracks in the Chinese South–North Water Transfer Project channel slopes. The maximum positioning accuracy of the method is 0.6 m, and the root mean square error is 0.21 m. It provides a new technical means for geological risk identification and health assessment of the South–North Water Transfer Central Project.
Uncertainties in Prediction of Streamflows Using SWAT Model—Role of Remote Sensing and Precipitation Sources
Authors: Chordia Jay , Panikkar Urmila R. , Srivastav Roshan , Shaik Riyaaz Uddien
Published: October 2022

Watershed modelling is crucial for understanding fluctuations in water balance and ensuring sustainable water management. The models’ strength and predictive ability are heavily reliant on inputs such as topography, land use, and climate. This study mainly focuses on quantifying the uncertainty associated with the input sources of the Digital Elevation Model (DEM), Land Use Land Cover (LULC), and precipitation using the Soil and Water Assessment Tool (SWAT) model. Basin-level modelling is being carried out to analyze the impact of source uncertainty in the prediction of streamflow. The sources for DEM used are National Elevation Dataset (NED)-United States Geological Survey (USGS), Shuttle Radar Topographic Mission (SRTM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), whereas for LULC the sources were the National Land Cover Database (NLCD), Continuous Change Detection Classification (CCDC), and GAP/LANDFIRE National Terrestrial Ecosystems dataset. Observed monitoring stations (Gage), Climate Forecast System Reanalysis (CFSR), and Tropical Rainfall Measuring Mission (TRMM) satellites are the respective precipitation sources. The Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Percent Bias (PBIAS), and the ratio of Root Mean Square Error to the standard deviation (RSR) are used to assess the model’s predictive performance. The results indicated that TRMM yielded better performance compared to the CFSR dataset. The USGS DEM performs best in all four case studies with the NLCD and CCDC LULC for all precipitation datasets except Gage. Furthermore, the results show that using a DEM with an appropriate combination can improve the model’s prediction ability by simulating streamflows with lower uncertainties. TheVIKOR MCDM method is used to rank model combinations. It is observed from MCDM analysis that USGS DEM combinations with NLCD/CCDC LULC attained top priority with all precipitation datasets. Furthermore, the rankings obtained from VIKOR MCDM are in accordance with the validation analysis using SWAT.
SMOS ESA RFI Monitoring and Information Tool: Lessons Learned
Authors: Uranga Ekhi , Llorente Álvaro , González Judit , de la Fuente Antonio , Oliva Roger , Soldo Yan , Jorge Flávio
Published: October 2022

The issue of Radio Frequency Interference (RFI) is a widespread problem in most microwave Earth observation missions, and passive instruments are particularly sensitive to RFI. This is the case for SMOS, Soil Moisture and Ocean Salinity, a satellite of the European Space Agency, which operates in the 1400–1427 MHz band, where all emissions are prohibited. Notwithstanding this regulatory framework, SMOS has been affected by RFI all around the world since the beginning of operations in 2010. In the first years of operations, manual detection processes and reporting of RFI to National Regulatory Authorities were in place in order to mitigate the detected sources. After 12 years, a tool called ERMIT (ESA RFI Monitoring and Information Tool) has been developed at ESAC (European Space Astronomy Center). This tool helps the SMOS RFI team in its spectrum monitoring tasks (e.g., RFI monitoring, logging, and reporting) thus allowing it to counteract RFI pollution more efficiently, providing external users with detailed and user-friendly information on the L-band RFI observed by SMOS. The ERMIT tool is now publicly available. This document aims at describing the needs that lead to the development of ERMIT and at presenting the information made available by it.
Vegetation Landscape Changes and Driving Factors of Typical Karst Region in the Anthropocene
Authors: Yu Mingzhao , Song Shuai , He Guizhen , Shi Yajuan
Published: October 2022

Vegetation degeneration has become a serious ecological problem for karst regions in the Anthropocene. According to the deficiency of long serial and high-resolution analysis of karst vegetation, this paper reconstructed the variation of vegetation landscape changes from 1987 to 2020 in a typical karst region of China. Using Landsat time series data, the dynamic changes and driving factors of natural karst vegetation were identified at the landscape scale. On the premise of considering the time-lag effect, the main climatic factors that influence vegetation growth were presented at the interannual timescale. Then, the approach of residual analysis was adopted to distinguish the dominant factors affecting vegetation growth. Results of trend analysis revealed that 21.5% of the forestland showed an overall significant decline in vegetation growth, while only 1.5% showed an increase in vegetation growth during the study period. Precipitation and radiation were the dominant meteorological factors influencing vegetation at the interannual timescale, as opposed to temperature. More than 70% of the natural vegetation growth was dominated by climatic factors. The area percentage of negative human impact has increased gradually since 2009 and reached 18.5% in 2020, indicating the currently serious situation of vegetation protection; fortunately, in recent years, human disturbances on vegetation have been mitigated in karst areas with the promotion of ecological conservation and restoration projects.
SAR2HEIGHT: Height Estimation from a Single SAR Image in Mountain Areas via Sparse Height and Proxyless Depth-Aware Penalty Neural Architecture Search for Unet
Authors: Xue Minglong , Li Jian , Zhao Zheng , Luo Qingli
Published: October 2022

Height estimation from a single Synthetic Aperture Radar (SAR) image has demonstrated a great potential in real-time environmental monitoring and scene understanding. The projection of a single 2D SAR image from multiple 3D height maps is an ill-posed problem in mathematics. Although Unet has been widely used for height estimation from a single image, the ill-posed problem cannot be completely resolved, and it leads to deteriorated performance with limited training data. This paper tackles the problem by Unet with the help of supplementary sparse height information and proxyless neural architecture search (PDPNAS) for Unet. The sparse height, which can be accepted from low-resolution SRTM or LiDAR products, is included as the supplementary information and is helpful to improve the accuracy of the estimated height map, especially in mountain areas with a wide range of elevations. In order to explore the effect of sparsity of sparse height on the estimated height map, a parameterized method is proposed to generate sparse height with a different sparse ratio. In order to further improve the accuracy of the estimated height map from a single SAR imagery, PDPNAS for Unet is proposed. The optimal architecture for Unet can be searched by PDPNAS automatically with the help of a depth-aware penalty term p. The effectiveness of our approach is evaluated by visual and quantitative analysis on three datasets from mountain areas. The root mean squared error (RMSE) is reduced by 90.30% through observing only 0.0109% of height values from a low-resolution SRTM product. Furthermore, the RMSE is reduced by 3.79% via PDPNAS for Unet. The research proposes a reliable method for estimating height and an alternative method for wide-area DEM mapping from a single SAR image, especially for the implementation of real-time DEM estimation in mountain areas.
Rapid Glacier Shrinkage in the Gongga Mountains in the Last 27 Years
Authors: Zhou Shuaibo , Sun Zhangli , Sun Peijun
Published: October 2022

Glaciers are an important part of the cryosphere and important reservoirs of fresh water on Earth. Glaciers in the Gongga Mountains, located in the southeastern Tibetan Plateau, have been experiencing dramatic changes and substantially shrinking over the past two decades. We analyzed the glacier change over the Gongga Mountains using the Landsat data from 1994 to 2021 (interval of 4 or 5 years), with Gaofen-1 (GF-1) data to evaluate the uncertainty. The glacier shrinkage under different terrain conditions, including altitudes, slope, and slope direction, was further explored. Finally, we evaluated the response of glacier shrinkage to climate change using precipitation and temperature data for nearly 30 years. Results show that the glaciers in the Gongga Mountains are experiencing an accelerating ablation, with a glacier area of ~240 km2 in 1994 and ~212 km2 in 2021 (an average annual shrinkage rate of 1.04 km2/a). The shrinkage mainly occurs in areas with altitudes of 5000–5300 m and a slope of 30–40°. Moreover, the shrinkage is strongly related to the recent warming of the climate, with the warming rate being 0.19 °C/10a, while precipitation remains almost constant during 1978–2019. The results provide a scientific basis for water resources management, ecological environmental protection, and natural disaster protection in southeast Tibet for decision making.
Toward an Automatic Assessment of Cognitive Dysfunction in Relapsing–Remitting Multiple Sclerosis Patients Using Eye Movement Analysis
Authors: García Cena Cecilia E. , Gómez-Andrés David , Pulido-Valdeolivas Irene , Sánchez-Seco Victoria Galán , Domingo-Santos Angela , Moreno-García Sara , Benito-León Julián
Published: October 2022

Despite the importance of cognitive function in multiple sclerosis, it is poorly represented in the Expanded Disability Status Scale (EDSS), the commonly used clinical measure to assess disability, suggesting that an analysis of eye movement, which is generated by an extensive and well-coordinated functional network that is engaged in cognitive function, could have the potential to extend and complement this more conventional measure. We aimed to measure the eye movement of a case series of MS patients with relapsing–remitting MS to assess their cognitive status using a conventional gaze tracker. A total of 41 relapsing–remitting MS patients and 43 age-matched healthy controls were recruited for this study. Overall, we could not find a clear common pattern in the eye motor abnormalities. Vertical eye movement was more impaired in MS patients than horizontal movement. Increased latencies were found in the prosaccades and reflexive saccades of antisaccade tests. The smooth pursuit was impaired with more corrections (backup and catchup movements, p<0.01). No correlation was found between eye movement variables and EDSS or disease duration. Despite significant alterations in the behavior of the eye movements in MS patients, which are compatible with altered cognitive status, there is no common pattern of these alterations. We interpret this as a consequence of the patchy, heterogeneous distribution of white matter involvement in MS that provokes multiple combinations of impairment at different points in the different networks involved in eye motor control. Further studies are therefore required.
A Software-Defined Directional Q-Learning Grid-Based Routing Platform and Its Two-Hop Trajectory-Based Routing Algorithm for Vehicular Ad Hoc Networks
Authors: Yang Chen-Pin , Yen Chin-En , Chang Ing-Chau
Published: October 2022

Dealing with the packet-routing problem is challenging in the V2X (Vehicle-to-Everything) network environment, where it suffers from the high mobility of vehicles and varied vehicle density at different times. Many related studies have been proposed to apply artificial intelligence models, such as Q-learning, which is a well-known reinforcement learning model, to analyze the historical trajectory data of vehicles and to further design an efficient packet-routing algorithm for V2X. In order to reduce the number of Q-tables generated by Q-learning, grid-based routing algorithms such as the QGrid have been proposed accordingly to divide the entire network environment into equal grids. This paper focuses on improving the defects of these grid-based routing algorithms, which only consider the vehicle density of each grid in Q-learning. Hence, we propose a Software-Defined Directional QGrid (SD-QGrid) routing platform in this paper. By deploying an SDN Control Node (CN) to perform centralized control for V2X, the SD-QGrid considers the directionality from the source to the destination, real-time positions and historical trajectory records between the adjacent grids of all vehicles. The SD-QGrid further proposes the flows of the offline Q-learning training process and the online routing decision process. The two-hop trajectory-based routing (THTR) algorithm, which depends on the source–destination directionality and the movement direction of the vehicle for the next two grids, is proposed as a vehicle node to forward its packets to the best next-hop neighbor node in real time. Finally, we use the real vehicle trajectory data of Taipei City to conduct extensive simulation experiments with respect to four transmission parameters. The simulation results prove that the SD-QGrid achieved an over 10% improvement in the average packet delivery ratio and an over 25% reduction in the average end-to-end delay at the cost of less than 2% in average overhead, compared with two well-known Q-learning grid-based routing algorithms.
Green and Integrated Wearable Electrochemical Sensor for Chloride Detection in Sweat
Authors: Lopresti Francesco , Patella Bernardo , Divita Vito , Zanca Claudio , Botta Luigi , Radacsi Norbert , O’Riordan Alan , Aiello Giuseppe , Kersaudy-Kerhoas Maïwenn , Inguanta Rosalinda , La Carrubba Vincenzo
Published: October 2022

Wearable sensors for sweat biomarkers can provide facile analyte capability and monitoring for several diseases. In this work, a green wearable sensor for sweat absorption and chloride sensing is presented. In order to produce a sustainable device, polylactic acid (PLA) was used for both the substrate and the sweat absorption pad fabrication. The sensor material for chloride detection consisted of silver-based reference, working, and counter electrodes obtained from upcycled compact discs. The PLA substrates were prepared by thermal bonding of PLA sheets obtained via a flat die extruder, prototyped in single functional layers via CO2 laser cutting, and bonded via hot-press. The effect of cold plasma treatment on the transparency and bonding strength of PLA sheets was investigated. The PLA membrane, to act as a sweat absorption pad, was directly deposited onto the membrane holder layer by means of an electrolyte-assisted electrospinning technique. The membrane adhesion capacity was investigated by indentation tests in both dry and wet modes. The integrated device made of PLA and silver-based electrodes was used to quantify chloride ions. The calibration tests revealed that the proposed sensor platform could quantify chloride ions in a sensitive and reproducible way. The chloride ions were also quantified in a real sweat sample collected from a healthy volunteer. Therefore, we demonstrated the feasibility of a green and integrated sweat sensor that can be applied directly on human skin to quantify chloride ions.
Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction
Authors: Lin Chien-Yu , Kau Lih-Jen , Chan Ching-Yao
Published: October 2022

We propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply the dual-mode probability model to describe the state of the pedestrian. Based on this model, we construct the proposed bimodal extended Kalman filter to estimate pedestrian state distribution. The filter obtains the state distribution for each pedestrian in the scene, respectively, and use that state distribution to predict the future trajectories of all the people in the scene. This prediction method estimates the prior probability of each parameter of the model through the dataset and updates the individual posterior probability of the pedestrian state through the bimodal extended Kalman filter. Our model can predict the trajectory of every individual, by taking the social interaction of pedestrians as well as the surrounding physical obstacles into account, with less than fifty model parameters being used, while with the limited parameter, our model could be nearly accurate as other deep learning models and still be comprehensible for model users.
Analysis of IoT-Related Ergonomics-Based Healthcare Issues Using Analytic Hierarchy Process Methodology
Authors: Upadhyay Hemant K. , Juneja Sapna , Muhammad Ghulam , Nauman Ali , Awad Nancy Awadallah
Published: October 2022

The objective of the present work is for assessing ergonomics-based IoT (Internet of Things) related healthcare issues with the use of a popular multi-criteria decision-making technique named the analytic hierarchy process (AHP). Multiple criteria decision making (MCDM) is a technique that combines alternative performance across numerous contradicting, qualitative, and/or quantitative criteria, resulting in a solution requiring a consensus. The AHP is a flexible strategy for organizing and simplifying complex MCDM concerns by disassembling a compound decision problem into an ordered array of relational decision components (evaluation criteria, sub-criteria, and substitutions). A total of twelve IoT-related ergonomics-based healthcare issues have been recognized as Lumbago (lower backache), Cervicalgia (neck ache), shoulder pain; digital eye strain, hearing impairment, carpal tunnel syndrome; distress, exhaustion, depression; obesity, high blood pressure, hyperglycemia. “Distress” has proven itself the most critical IoT-related ergonomics-based healthcare issue, followed by obesity, depression, and exhaustion. These IoT-related ergonomics-based healthcare issues in four categories (excruciating issues, eye-ear-nerve issues, psychosocial issues, and persistent issues) have been compared and ranked. Based on calculated mathematical values, “psychosocial issues” have been ranked in the first position followed by “persistent issues” and “eye-ear-nerve issues”. In several industrial systems, the results may be of vital importance for increasing the efficiency of human force, particularly a human–computer interface for prolonged hours.
Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics
Authors: Sushkov Nikolai I. , Galbács Gábor , Janovszky Patrick , Lobus Nikolay V. , Labutin Timur A.
Published: October 2022

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.
Physical Load While Using a Tablet at Different Tilt Angles during Sitting and Standing
Authors: Tomita Yosuke , Suzuki Yoshitaka , Shibagaki Akari , Takahashi Shingo , Matsuka Yoshizo
Published: October 2022

Few standards and guidelines to prevent health problems have been associated with tablet use. We estimated the effects of posture and tablet tilt angle on muscle activity and posture in healthy young adults. Seventeen healthy young adults (age: 20.5 ± 3 years) performed a cognitive task using a tablet in two posture (sitting and standing) and tablet tilt angle (0 degrees and 45 deg) conditions. Segment and joint kinematics were evaluated using 16 inertial measurement unit sensors. Neck, trunk, and upper limb electromyography (EMG) activities were monitored using 12 EMG sensors. Perceived discomfort, kinematics, and EMG activities were compared between conditions using the Friedman test. The perceived discomfort in the standing-0 deg condition was significantly higher than in the remaining three conditions. Standing posture and tablet inclination significantly reduced the sagittal segment and joint angles of the spine, compared with sitting and flat tablet conditions. Similarly, standing posture and tablet inclination significantly reduced EMG activities of the dorsal neck, upper, and lower trunk muscles, while increasing EMG activity of shoulder flexors. Standing posture and tablet inclination reduced the sagittal flexion angle, and dorsal neck, upper, and lower trunk muscle activities, while potentially increasing the muscle activity of arm flexors.
Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM
Authors: Usama Muhammad , Lee In-Young
Published: October 2022

This study designs a simple current controller employing deep symbolic regression (DSR) in a surface-mounted permanent magnet synchronous machine (SPMSM). A novel DSR-based optimal current control scheme is proposed, which after proper training and fitting, generates an analytical dynamic numerical expression that characterizes the data. This creates an understandable model and has the potential to estimate data that have not been seen before. The goal of this study was to overcome the traditional linear proportional–integral (PI) current controller because the performance of the PI is highly dependent on the system model. Moreover, the outer speed control loop gains are tuned using the cuckoo search algorithm, which yields optimal gain values. To demonstrate the efficacy of the proposed design, we apply the control design to different test cases, that is varied speed and load conditions, as well as sinusoidal speed reference, and compare the results with those of a traditional vector control design. Compared with traditional control approaches, we deduce that the DSR-based control design could be extrapolated far beyond the training dataset, laying the foundation for the use of deep learning techniques in power conversion applications.
The Detection of Foreign Items in Laundry Industry by Dual-Energy X-ray Transmission—Advantages and Limits
Authors: Bauer Christine , Wagner Rebecca , Leisner Johannes
Published: October 2022

Firefighters, paramedics, nursing staff, and other occupational groups are in constant need of fast and proper cleaning of their professional workwear, not only during a pandemic. Thus, laundry technology needs to become more efficient and automated. Unfortunately, some steps of the cleaning process, such as finding and removing foreign items from pockets or belts, are still completed manually. This is not just time-consuming but potentially dangerous for the workers due to the hazardous nature of items such as scissors, scalpels, or syringes. Additionally, some items may damage the garments by staining or harm the laundry machines, causing malfunctions and process failure. On the one hand, these foreign items are often hidden inside the clothes, making detection very challenging with conventional superficial sensors. On the other hand, these items can be diverse and cannot be detected by metal detectors alone. X-ray transmission has proven to be a powerful tool for detecting items inside of objects. The dual-energy approach (DE-XRT) even allows obtaining quantitative information about the chemical composition of the measured materials. In this study, working garments were accompanied and filled with realistic foreign items. The potential of DE-XRT to detect those items was successfully shown.