bluuebunny/crossref_metadata_2025 huggingface dataset:
Physical Significance of the Determinant of a Mueller Matrix
Authors: Gil José J. , Ossikovski Razvigor , San José Ignacio
Published: April 2022

The determinant of a Mueller matrix M plays an important role in both polarization algebra and the interpretation of polarimetric measurements. While certain physical quantities encoded in M admit a direct interpretation, the understanding of the physical and geometric significance of the determinant of M (detM) requires a specific analysis, performed in this work by using the normal form of M, as well as the indices of polarimetric purity (IPP) of the canonical depolarizer associated with M. We derive an expression for detM in terms of the diattenuation, polarizance and a parameter proportional to the volume of the intrinsic ellipsoid of M. We likewise establish a relation existing between the determinant of M and the rank of the covariance matrix H associated with M, and determine the lower and upper bounds of detM for the two types of Mueller matrices by taking advantage of their geometric representation in the IPP space.
On Optically Modulated Reflective Semiconductor Optical Amplifier Pattern-Dependent Overshoot Mitigation Using a Birefringent Fiber Loop
Authors: Avgenos Nikolaos , Zoiros Kyriakos E. , Rizou Zoe V.
Published: April 2022

Reflective semiconductor optical amplifiers (RSOAs) are key elements for modern optical communications. Despite their widespread deployment, their performance when intended for ultrafast data amplification is limited by their inherently slow gain dynamics. In this paper, we propose to employ a birefringent fiber loop (BFL) to compensate for the RSOA pattern-dependent behavior and extend its operation well beyond that allowed by its nominal optical modulation bandwidth. We apply a reduced model to describe the RSOA response and quantify the RSOA output distortion by means of a non-return-to-zero data pulse overshoot. We validate the outcomes of this model in the time domain both for the RSOA alone and with the assistance of the BFL by an extensive comparison to available measurements. The excellent matching between simulation and experimental results allows us to further investigate the impact of critical operating parameters and derive specifications for them so that the performance of the scheme against the overshoot is made acceptable. The theoretical predictions confirm the ability of the BFL to enhance the RSOA direct amplification capability and hence establish it as a frequency discriminator for complementing RSOAs’ versatile and scalable operation.
Evaluation of Chelating Agents Used in Phytoextraction by Switchgrass of Lead Contaminated Soil
Authors: Hart Genna , Koether Marina , McElroy Thomas , Greipsson Sigurdur
Published: April 2022

Soil lead (Pb) contamination is a recognized environmental and global health problem. Phytoextraction of Pb using switchgrass (Panicum virgatum L.), a second-generation biofuel crop, is typically enhanced by soil chelation. The effectiveness of four different chelating agents, phytic acid (inositol hexaphosphate), citric acid, NTA (nitrilotriacetic acid), and EDTA (ethylenediaminetetraacetic acid) was examined in pot culture. Plants treated with EDTA (1 mM) showed significantly higher shoot Pb concentrations compared to control plants and plants treated with other chelates. Lead-solubility following phytoextraction was examined by soil washing using 0.01 and 0.05 M acetic acid as an extractant solution revealed no significant differences in Pb concentrations in soil among different chelate treatments and control. Furthermore, the effects of different concentrations (1, 2, 5 and 10 mM) of NTA on Pb phytoextraction of switchgrass were examined. Plants receiving 5 mM and 10 mM NTA had significantly higher foliage concentrations of Pb compared to plants treated with lower levels (1 and 2 mM) of NTA. Moreover, the effect of NTA application alone was significantly improved by a combined application of Triton X-100, an alkyl polyglucoside (APG); the Pb concentration in the foliage of switchgrass was more than doubled when treated with NTA combined with APG. The use of NTA combined with APG has great potential in improving phytoextraction efficiencies of switchgrass on Pb-contaminated soils.
Targeted Gene Mutations in the Forest Pathogen Dothistroma septosporum Using CRISPR/Cas9
Authors: McCarthy Hannah M. , Tarallo Mariana , Mesarich Carl H. , McDougal Rebecca L. , Bradshaw Rosie E.
Published: April 2022

Dothistroma needle blight, caused by Dothistroma septosporum, has increased in incidence and severity over the last few decades and is now one of the most important global diseases of pines. Disease resistance breeding could be accelerated by knowledge of pathogen virulence factors and their host targets. However, this is hindered due to inefficient targeted gene disruption in D. septosporum, which is required for virulence gene characterisation. Here we report the first successful application of CRISPR/Cas9 gene editing to a Dothideomycete forest pathogen, D. septosporum. Disruption of the dothistromin pathway regulator gene AflR, with a known phenotype, was performed using nonhomologous end-joining repair with an efficiency of >90%. Transformants with a range of disruption mutations in AflR were produced. Disruption of Ds74283, a D. septosporum gene encoding a secreted cell death elicitor, was also achieved using CRISPR/Cas9, by using a specific donor DNA repair template to aid selection where the phenotype was unknown. In this case, 100% of screened transformants were identified as disruptants. In establishing CRISPR/Cas9 as a tool for gene editing in D. septosporum, our research could fast track the functional characterisation of candidate virulence factors in D. septosporum and helps set the foundation for development of this technology in other forest pathogens.
Allelopathy in Durum Wheat Landraces as Affected by Genotype and Plant Part
Authors: Scavo Aurelio , Pandino Gaetano , Restuccia Alessia , Caruso Paolo , Lombardo Sara , Mauromicale Giovanni
Published: April 2022

Durum wheat is one of the largest cultivated crops across Mediterranean areas. The high demand for sustainable crop productions, especially concerning weed management, is driving the return to local landraces. In the present work, the in vitro allelopathic effects of the extracts of three durum wheat landraces (‘Timilia’, ‘Russello’ and ‘Perciasacchi’) and a modern variety (‘Mongibello’), obtained from three different plant parts (ears, stems and roots), were tested on seed germination (G) and mean germination time (MGT) of Portulaca oleracea L. and Stellaria. media (L.) Vill., two weeds commonly infesting wheat fields. In addition, the total polyphenol (TPC) and total flavonoid (TFC) content of extracts was determined. All extracts reduced G and increased MGT in both weeds compared to the control. The magnitude of phytotoxicity was strongly affected by the influence of genotype, plant part and extract dilution. Overall, the landraces ‘Timilia’ and ‘Russello’ showed the highest allelopathic effects, ear extracts were the most active, and the maximum extract dilution induced higher phytotoxicity. Extracts’ TPC and TFC corroborated these results. The findings obtained here encourage the use of local landraces as a source of allelochemicals and suggest that they could be left on soil surface or soil-incorporated after harvest for a possible weed control.
Gas Chromatography-Mass Spectrometry (GC-MS) Metabolites Profiling and Biological Activities of Various Capsicum annum cultivars
Authors: Ahmad Rizwan , Alqathama Aljawharah , Aldholmi Mohammed , Riaz Muhammad , Abdalla Ashraf N. , Mostafa Ahmed , Al-Said Hamdi M. , Alqarni Abdulmalik M. , Ullah Riaz , Asgher Sami S. , Amir Mohd , Shaaban Heba , Ahmad Wasim
Published: April 2022

This study evaluates the quality variation for twenty-seven capsicum fruit (CF) samples, in terms of their volatile oil composition and biological activities. The GCMS analysis revealed the presence of seventy one chemical compounds from different chemical classes with an average (%) composition of: 26.13 (alcohols) > 18.82 (hydrocarbons) > 14.97 (esters) > 3.08 (ketones) > 1.14 (others) > 1.07 (acids) > 0.72 (sugar) > 0.42 (aldehydes) > 0.15 (amino compounds). Alcohols and hydrocarbons were the most abundant in these CF samples with 1-Decanol, 2-octyl- and docosanoic acid, docosyl ester as the major components, respectively. The % inhibition in cytotoxicity assays was observed in the range of 9–47 (MCF7) and 4–41 (HCT116) whereas, the zone of inhibition (mm) for the antimicrobial activity was found to be 0.0–17 (P. aeruginosa) > 0.0–13 (E. coli and S. aureus). Moreover, the samples with the largest zone of inhibition in the agar-well-diffusion method (C16, C19, and C26) upon further evaluation presented the least MIC and MBC values against P. aeruginosa with an MIC and MBC (µg/mL) of 6.3 and 12.5, respectively. The outcome for GCMS and biological activities were further supported by statistical tools of PCA and K-mean cluster analysis which confirmed the C16 CF sample with the best activity followed by C5, C13 (the best cytotoxic), and C19, C26 (the best antimicrobial). The statistical analysis exhibited a high Chi-square value of 5931.68 (GCMS) and 32.19 (biological activities) with p = 0.00 for KMO and Bartlett’s Test of Sphericity. The 27-CF samples were effectively distinguished based on quality variation, and the C16 CF sample exhibited significant potential for further study.
Seed-Encapsulation of Desiccation-Tolerant Microorganisms for the Protection of Maize from Drought: Phenotyping Effects of a New Dry Bioformulation
Authors: Rebelo Romão Inês , Rodrigues dos Santos Ana Sofia , Velasco Leonardo , Martínez-Ferri Elsa , Vilchez Juan Ignacio , Manzanera Maximino
Published: April 2022

Droughts and high temperatures deeply affect crop production. The use of desiccation-tolerant (or xerotolerant) microorganisms able to protect plants from droughts represents a promising alternative. These xerotolerant microorganisms have previously been used to modulate plant responses and improve their tolerance to drought. In addition, these microorganisms could be stored and used in dry formats, which would improve their viability and resilience at a much lower cost than current market alternatives. In the present study we analyze the possibility of using strains of xerotolerant Actinobacteria in encapsulated format on seeds. Under this formulation, we carried out greenhouse with farming soil with maize plants. Under greenhouse conditions, the plants showed greater resistance to drought, as well as increased growth and production yield, but not as well in field trials. This alternative could represent a useful tool to improve water efficiency in crops for drought-affected areas or affected by water scarcity.
Evaluation of the Strength of Slab-Column Connections with FRPs Using Machine Learning Algorithms
Authors: Salem Nermin M. , Deifalla Ahmed
Published: April 2022

Slab-column connections with FRPs fail suddenly without warning. Machine learning (ML) models can model the behavior with high precision and reliability. Nineteen ML algorithms were examined and compared. The comparisons showed that the ensembled boosted tree model showed the best, most precise prediction with the highest coefficient of determination (R2) (0.98), the lowest Root Mean Square Error (RMSE) (44.12 kN), and the lowest Mean Absolute Error (MAE) (35.95 kN). The ensembled boosted model had an average of 0.99, a coefficient of variation of 12%, and a lower 95% of 0.97, respectively, in terms of the measured strength. Thus, it was found to be more accurate and consistent compared to all implemented machine learning models and selected traditional models. In addition, the significance of various parameters with respect to the predicted strength was identified, where the effective depth was the most significant by a factor of 0.9, and the concrete compressive strength was the lowest by a factor of 0.3.
Probing the Dynamic Structural Evolution of End-Functionalized Polybutadiene/Organo-Clay Nanocomposite Gels before and after Yielding by Nonlinear Rheology and 1H Double-Quantum NMR
Authors: Peng Wansu , Feng Chengdong , Hou Jiawen , Zhang Rongchun , Sun Pingchuan , Gao Yun , Wang Xiaoliang
Published: April 2022

Understanding the structural evolution process after the yielding of networks in polymer nanocomposites can provide significant insights into the design and fabrication of high-performance nanocomposites. In this work, using hydroxyl-terminated 1,4-polybutadiene (HTPB)/organo-clay nanocomposite gel as a model, we explored the yielding and recovery process of a polymer network. Linear rheology results revealed the formation of a nanocomposite gel with a house-of-cards structure due to the fully exfoliated 6 to 8 wt% organo-clays. Within this range, nonlinear rheologic experiments were introduced to yield the gel network, and the corresponding recovery processes were monitored. It was found that the main driving force of network reconstruction was the polymer–clay interaction, and the rotation of clay sheets played an important role in arousing stress overshoots. By proton double-quantum (1H DQ) NMR spectroscopy, residual dipolar coupling and its distribution contributed by HTPB segments anchored on clay sheets were extracted to unveil the physical network information. During the yielding process of a house-of-cards network, e.g., 8 wt% organo-clay, nearly one-fourth of physical cross-linking was broken. Based on the rheology and 1H DQ NMR results, a tentative model was proposed to illustrate the yielding and recovery of the network in HTPB/organo-clay nanocomposite gel.
Characterisation of Selected Materials in Medical Applications
Authors: Kroczek Kacper , Turek Paweł , Mazur Damian , Szczygielski Jacek , Filip Damian , Brodowski Robert , Balawender Krzysztof , Przeszłowski Łukasz , Lewandowski Bogumił , Orkisz Stanisław , Mazur Artur , Budzik Grzegorz , Cebulski Józef , Oleksy Mariusz
Published: April 2022

Tissue engineering is an interdisciplinary field of science that has developed very intensively in recent years. The first part of this review describes materials with medical and dental applications from the following groups: metals, polymers, ceramics, and composites. Both positive and negative sides of their application are presented from the point of view of medical application and mechanical properties. A variety of techniques for the manufacture of biomedical components are presented in this review. The main focus of this work is on additive manufacturing and 3D printing, as these modern techniques have been evaluated to be the best methods for the manufacture of medical and dental devices. The second part presents devices for skull bone reconstruction. The materials from which they are made and the possibilities offered by 3D printing in this field are also described. The last part concerns dental transitional implants (scaffolds) for guided bone regeneration, focusing on polylactide–hydroxyapatite nanocomposite due to its unique properties. This section summarises the current knowledge of scaffolds, focusing on the material, mechanical and biological requirements, the effects of these devices on the human body, and their great potential for applications.
The Engine Combustion Phasing Prediction Based on the Support Vector Regression Method
Authors: Wang Qifan , Yang Ruomiao , Sun Xiaoxia , Liu Zhentao , Zhang Yu , Fu Jiahong , Li Ruijie
Published: April 2022

While traditional one-dimensional and three-dimensional numerical simulation techniques require a lot of tests and time, emerging Machine Learning (ML) methods can use fewer data to obtain more information to assist in engine development. Combustion phasing is an important parameter of the spark-ignition (SI) engine, which determines the emission and power performance of the engine. In the engine calibration process, it is necessary to determine the maximum brake torque timing (MBT) for different operating conditions to obtain the best engine dynamics performance. Additionally, the determination of the combustion phasing enables the Wiebe function to predict the combustion process. Existing studies have unacceptable errors in the prediction of combustion phasing parameters. This study aimed to find a solution to reduce prediction errors, which will help to improve the calibration accuracy of the engine. In this paper, we used Support Vector Regression (SVR) to reconstruct the mapping relationship between engine inputs and responses, with the hyperparametric optimization method Gray Wolf Optimization (GWO) algorithm. We chose the engine speed, load, and spark timing as engine inputs. Combustion phasing parameters were selected as engine responses. After machine learning training, we found that the prediction accuracy of the SVR model was high, and the R2 of CA10−ST, CA50, CA90, and DOC were all close to 1. The RMSE of these indicators were close to 0. Consequently, SVR can be applied to the prediction of combustion phasing in SI gasoline engines and can provide some reference for combustion phasing control.
Transfer of Potentially Toxic Elements in the Soil-Plant System in Magnesite Mining and Processing Areas
Authors: Štofejová Lenka , Fazekaš Juraj , Fazekašová Danica
Published: April 2022

Mining activities, ore concentrations, and transport processes generate large amounts of pollutants, including hazardous elements, which are released into the environment. This work presents the results of experimental research aimed at evaluating the environmental risks of soil and plant contamination in two magnesite mining and processing areas in the Slovak Republic, and assesses the phytoremediation potential of dominant plant species. Eleven potentially toxic elements in the soil were investigated using X-ray fluorescence spectrometry (Cd, Pb, Cr, Zn, Cu, As, Ni, Mn, Mg, Fe) and atomic absorption spectrometry (Hg). In plants, potentially toxic elements were investigated using inductively coupled plasma mass spectrometry (Cu, As, Cd, Pb) and inductively coupled plasma atomic emission spectrometry (Cr, Zn, Mn, Mg). Selected soil parameters (pH, redox potential, and soil organic matter) were also investigated. Soil contamination was evaluated using environmental indices (geoaccumulation index—Igeo, enrichment factor—EF, contamination factor—Cf, degree of contamination—Cd). The phytoremediation potential of plants was evaluated using the bioconcentration factor (BCF) and the translocation factor (TF). The soil reaction in the studied areas indicated a strong alkalization of the soil. The soils in Jelšava-Lubeník were significantly contaminated with Cr, As, Mn, and Mg. The most significant enrichment based on the average values of EF was found to be in the order of Cd > Mg > Zn > Cu > As > Cr > Ni > Pb > Fe > Hg > Mn. The observed values of Cf and Cd indicated a high degree of soil contamination. In Košice, the soils were found to be significantly contaminated with Cr, Mn, Mg, and Ni. The most significant enrichment was found in the order of Cd > Mn > Ni > Pb > Zn > Mg > Cu > As > Fe > Cr > Hg. Very high Cf was found for Pb and Cr. The results of correlation and hierarchical cluster analyses suggest a similar origin of pollutants caused by significant anthropogenic interventions due to magnesite mining and processing. The investigated dominant plant species, Phragmites australis, Agrostis stolonifera, Elytrigia repens, and Taraxacum officinale are able to accumulate high concentrations of the monitored potentially toxic elements without more serious load or damage. The results of BCF and TF confirmed that P. australis and T. officinale appeared to be suitable accumulators in the phytoextraction process. In the case of E. repens and A. stolonifera it was confirmed that they accumulate and immobilize high concentrations of potentially toxic elements, especially in the roots, establishing the suitability of their use in phytostabilization processes.
What Is the Optimal Method for Cleaning Screen-Printed Electrodes?
Authors: Stan Dana , Mirica Andreea-Cristina , Iosub Rodica , Stan Diana , Mincu Nicolae Bogdan , Gheorghe Marin , Avram Marioara , Adiaconita Bianca , Craciun Gabriel , Bocancia Mateescu Andreea Lorena
Published: April 2022

Screen-printed electrodes-based sensors can be successfully used to determine all kinds of analytes with great precision and specificity. However, obtaining a high-quality sensor can be difficult due to factors such as lack of reproducibility, surface contamination or other manufacturing challenges. An important step in ensuring reproducible results is the cleaning step. The aim of the current work is to help researchers around the world who struggle with finding the most suitable method for cleaning screen-printed electrodes. We evaluated the cleaning efficiency of different chemical compounds and cleaning methods using cyclic voltammetry and electrochemical impedance spectroscopy. The percentage differences in polarization resistance (Rp) before and after cleaning were as follows: acetone—35.33% for gold and 49.94 for platinum; ethanol—44.50% for gold and 81.68% for platinum; H2O2—47.34% for gold and 92.78% for platinum; electrochemical method—3.70% for gold and 67.96% for platinum. Thus, we concluded that all the evaluated cleaning methods seem to improve the surface of both gold and platinum electrodes; however, the most important reduction in the polarization resistance (Rp) was obtained after treating them with a solution of H2O2 and multiple CV cycles with a low scanning speed (10 mV/s).
An Enhanced Stacking Ensemble Method for Granule Moisture Prediction in Fluidized Bed Granulation
Authors: Chen Binbin , Huang Panling , Zhou Jun , Li Mindong
Published: April 2022

Moisture is a crucial quality property for granules in fluidized bed granulation (FBG) and accurate prediction of the granule moisture is significant for decision making. This study proposed a novel stacking ensemble method to predict the granule moisture based on granulation process parameters. The proposed method employed k-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LightGBM) and deep neural networks (DNNs) as the base learners, and ridge regression (RR) as the meta learner. To improve the diversity of the base learners, perturbations of the input variables and network structures were adopted in the proposed method, implemented by feature construction and combination of multiple DNNs with a different number of hidden layers, respectively. In the feature construction, a SHapley Additive exPlanations (SHAP) approach was innovatively utilized to construct effective synthetic features, which enhanced the prediction performance of the base learners. The cross-validation results demonstrated that the proposed stacking ensemble method outperformed other machine learning (ML) algorithms in terms of performance evaluation criteria, for which the parameters MAE, MAPE, RMSE, and Adj. R2 were 0.0596, 1.5819, 0.0844, and 0.99485, respectively.
Correction: Konidaris et al. Dating of the Lower Pleistocene Vertebrate Site of Tsiotra Vryssi (Mygdonia Basin, Greece): Biochronology, Magnetostratigraphy, and Cosmogenic Radionuclides. Quaternary 2021, 4, 1
Authors: Konidaris George E. , Kostopoulos Dimitris S. , Maron Matteo , Schaller Mirjam , Ehlers Todd A. , Aidona Elina , Marini Mattia , Tourloukis Vangelis , Muttoni Giovanni , Koufos George D. , Harvati Katerina
Published: April 2022

The authors wish to make the following corrections to their paper [...]
The Evolution of an Ancient Coastal Lake (Lerna, Peloponnese, Greece)
Authors: Koskeridou Efterpi , Thivaiou Danae , Psarras Christos , Rentoumi Evangelia , Evelpidou Niki , Saitis Giannis , Petropoulos Alexandros , Ioakim Chryssanthi , Katopodis George , Papaspyropoulos Konstantinos , Plessas Spyros
Published: April 2022

Degradation of coastal environments is an issue that many areas in Europe are facing. In the present work, an ancient coastal lake wetland is investigated, the so-called Lake Lerna in NE Peloponnese, Greece. The area hosted early agricultural populations of modern Greece that started modifying their environment as early as the early–middle Neolithic. Two drill cores in the area of the ancient lake were analysed to establish the sedimentological succession and the depositional environments using sub-fossil assemblages (molluscs and ostracods). Three lithological and faunal units were recovered, the latter being confirmed by the statistical ordination method (non-metric multidimensional scaling). The usage of sub-fossil mollusc species for the first time in the region enriched the dataset and contributed significantly to the delimitation of the faunas. These consist of environments characterised by various levels of humidity (from stagnant waters to freshwater lake) and salinity, with ephemeral intrusions of salt water to the lake, documented by mollusc and ostracod populations. We conclude that the lake and its included fauna and flora were mostly affected by climatic fluctuations rather than human intervention in the area.
Late-Glacial and Holocene Lake-Level Fluctuations on the Kenai Lowland, Reconstructed from Satellite-Fen Peat Deposits and Ice-Shoved Ramparts, Kenai Peninsula, Alaska
Authors: Berg Edward E. , Kaufman Darrell S. , Anderson R. Scott , Wiles Gregory C. , Lowell Thomas V. , Mitchell Edward A. D. , Hu Feng Sheng , Werner Alan
Published: April 2022

Recent decades of warmer climate have brought drying wetlands and falling lake levels to southern Alaska. These recent changes can be placed into a longer-term context of postglacial lake-level fluctuations that include low stands that were as much as 7 m lower than present at eight lakes on the Kenai Lowland. Closed-basin lakes on the Kenai Lowland are typically ringed with old shorelines, usually as wave-cut scarps, cut several meters above modern lake levels; the scarps formed during deglaciation at 25–19 ka in a kettle moraine topography on the western Kenai Lowland. These high-water stands were followed by millennia of low stands, when closed-basin lake levels were drawn down by 5–10 m or more. Peat cores from satellite fens near or adjoining the eight closed-basin lakes show that a regional lake level rise was underway by at least 13.4 ka. At Jigsaw Lake, a detailed study of 23 pairs of overlapping sediment cores, seismic profiling, macrofossil analysis, and 58 AMS radiocarbon dates reveal rapidly rising water levels at 9–8 ka that caused large slabs of peat to slough off and sink to the lake bottom. These slabs preserve an archive of vegetation that had accumulated on a lakeshore apron exposed during the preceding drawdown period. They also preserve evidence of a brief period of lake level rise at 4.7–4.5 ka. We examined plant succession using in situ peat sequences in nine satellite fens around Jigsaw Lake that indicated increased effective moisture between 4.6 and 2.5 ka synchronous with the lake level rise. Mid- to late-Holocene lake high stands in this area are recorded by numerous ice-shoved ramparts (ISRs) along the shores. ISRs at 15 lakes show that individual ramparts typically record several shove events, separated by hundreds or thousands of years. Most ISRs date to within the last 5200 years and it is likely that older ISRs were erased by rising lake levels during the mid- to late Holocene. This study illustrates how data on vegetation changes in hydrologically coupled satellite-fen peat records can be used to constrain the water level histories in larger adjacent lakes. We suggest that this method could be more widely utilized for paleo-lake level reconstruction.
Digital Islam and Muslim Millennials: How Social Media Influencers Reimagine Religious Authority and Islamic Practices
Authors: Zaid Bouziane , Fedtke Jana , Shin Don Donghee , El Kadoussi Abdelmalek , Ibahrine Mohammed
Published: April 2022

Digital platforms have empowered individuals and communities to re-negotiate long-established notions of religion and authority. A new generation of social media influencers has recently emerged in the Muslim world. They are western-educated, unique storytellers, and savvy in digital media production. This raises new questions on the future of Islam in the context of emerging challenges, such as the openness of technology and the often-perceived closedness of religious and cultural systems within Muslim societies. This paper uses a multiple case research design to examine the roles of social media influencers in reimagining Islam and reshaping spiritual beliefs and religious practices among young people in the Gulf Region, the Arab world, and beyond. We used thematic analysis of the Instagram and YouTube content of four social media influencers in the Gulf Region: Salama Mohamed and Khalid Al Ameri from the United Arab Emirates, Ahmad Al-Shugairi from Saudi Arabia, and Omar Farooq from Bahrain. The study found that social media influencers are challenging traditional religious authorities as they reimagine Muslim identities based on a new global lifestyle.
A Bridge Life Insurance for Households—Diagnosis and Motives
Authors: Jędrzychowska Anna
Published: April 2022

Purpose: The purpose of this article is to describe the initial concept of household bridging insurance. Design/methodology/approach: In the first part of the article, an extensive literature review is made. This is made to show the research gap of insufficient protection of households against destabilization resulting from the lost personal contribution. Data shown in the text present the scale of the loss of lost unpaid work (based on household time budgets). The existing methods of managing this loss, based on social insurance, are also shown. Findings: This paper discusses the possibility of creating a new insurance. Its need is indicated (research gap, the scale of the problem, and insufficient protection by the social insurance system) and a preliminary outline of its structure is indicated (annuities character, dynamic sum insured related to the lifecycle of the household). The article contains the theoretical background of the new product, and introduces further research on the use of multistate models in the construction and calculation of insurance premiums. Originality/value: So far, studies concerning, inter alia, personal damage indicate the lost personal contribution (unpaid work for household members) and even try to evaluate it. However, no private insurance has been proposed to mitigate the destabilization resulting from the death of an adult household member. The article therefore proposes a new life insurance (a separated policy or as an extension option) that would help the household to return to normal operation after the death of one of the household members.
Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission
Authors: Candiani Gabriele , Tagliabue Giulia , Panigada Cinzia , Verrelst Jochem , Picchi Valentina , Rivera Caicedo Juan Pablo , Boschetti Mirco
Published: April 2022

In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This mission will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the “agriculture and food security” domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R2 = 0.79, RMSE = 0.38 g m−2 for CCC and R2 = 0.84, RMSE = 1.10 g m−2 for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R2 = 0.88 and RMSE = 0.21 g m−2 for CCC; R2 = 0.93 and RMSE = 0.71 g m−2 for CNC), providing good results also at leaf level (best metrics: R2 = 0.72 and RMSE = 3.31 μg cm−2 for LCC; R2 = 0.56 and RMSE = 0.02 mg cm−2 for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts.
The Impact of Climate Changes on Slope Stability and Landslide Conditioning Factors: An Example from Kravarsko, Croatia
Authors: Miklin Luka , Podolszki Laszlo , Gulam Vlatko , Markotić Ivan
Published: April 2022

The Gajevo landslide in the Kravarsko area (Vukomeričke Gorice hilly area, northern Croatia) was chosen for investigation due to the existing landslide risk for the households at the landslide crown. Available data are limited, but a new landslide map and cross-section was developed within the presented research, mostly based on detailed light detection and ranging (LiDAR) data and field mapping. By comparing available orthophotos of the landslide, resident testimonies, precipitation data, and media releases, it was concluded that the landslide was activated in February 2014. The landslide was primarily triggered by increased precipitation (its measured variations could be in direct connection with ongoing global climate changes), but natural terrain features and anthropogenic factors also affected slope stability. New findings have led to the conclusion that the existing landslide area is large and complex and the crown and head scarp area should be stabilized by urgent remediation measures.
Spatiotemporal Reconstruction of MODIS Normalized Difference Snow Index Products Using U-Net with Partial Convolutions
Authors: Xing De , Hou Jinliang , Huang Chunlin , Zhang Weimin
Published: April 2022

Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product is one of the prevailing datasets for global snow monitoring, but cloud obscuration leads to the discontinuity of ground coverage information in spatial and temporal. To solve this problem, a novel spatial-temporal missing information reconstruction model based on U-Net with partial convolutions (PU-Net) is proposed to recover the cloud gaps in the MODIS Normalized Difference Snow Index (NDSI) products. Taking the Yellow River Source Region as a study case, in which the snow cover is characterized by shallow, fast-changing and complex heterogeneity, the MODIS NDSI product in the 2018–2019 snow season is reconstructed, and the reconstruction accuracy is validated with simulated cloud mask and in situ snow depth (SD) observations. The results show that under the simulated cloud mask scenario, the mean absolute error (MAE) of the reconstructed missing pixels is from 4.22% to 18.81% under different scenarios of the mean NDSI of the patch and the mask ratio of the applied mask, and the coefficient of determination (R2) ranges from 0.76 to 0.94. The validation based on in situ SD observations at 10 sites shows good consistency, the overall accuracy is increased by 25.66% to 49.25% compared with the Aqua-Terra combined MODIS NDSI product, and its value exceeds 90% at 60% of observation stations.
Learning Spatio-Temporal Attention Based Siamese Network for Tracking UAVs in the Wild
Authors: Chen Junjie , Huang Bo , Li Jianan , Wang Ying , Ren Moxuan , Xu Tingfa
Published: April 2022

The popularity of unmanned aerial vehicles (UAVs) has made anti-UAV technology increasingly urgent. Object tracking, especially in thermal infrared videos, offers a promising solution to counter UAV intrusion. However, troublesome issues such as fast motion and tiny size make tracking infrared drone targets difficult and challenging. This work proposes a simple and effective spatio-temporal attention based Siamese method called SiamSTA, which performs reliable local searching and wide-range re-detection alternatively for robustly tracking drones in the wild. Concretely, SiamSTA builds a two-stage re-detection network to predict the target state using the template of first frame and the prediction results of previous frames. To tackle the challenge of small-scale UAV targets for long-range acquisition, SiamSTA imposes spatial and temporal constraints on generating candidate proposals within local neighborhoods to eliminate interference from background distractors. Complementarily, in case of target lost from local regions due to fast movement, a third stage re-detection module is introduced, which exploits valuable motion cues through a correlation filter based on change detection to re-capture targets from a global view. Finally, a state-aware switching mechanism is adopted to adaptively integrate local searching and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on three anti-UAV datasets nicely demonstrate SiamSTA’s advantage over other competitors. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.
Soil Salinity Inversion Model of Oasis in Arid Area Based on UAV Multispectral Remote Sensing
Authors: Zhao Wenju , Zhou Chun , Zhou Changquan , Ma Hong , Wang Zhijun
Published: April 2022

Soil salinization severely restricts the development of global industry and agriculture and affects human beings. In the arid area of Northwest China, oasis saline-alkali land threatens the development of agriculture and food security. This paper develops and optimizes an inversion monitoring model for monitoring the soil salt content using unmanned aerial vehicle (UAV) multispectral remote sensing data. Using the multispectral remote sensing data in three research areas, the soil salt inversion models based on the support vector machine regression (SVR), random forest (RF), backpropagation neural network (BPNN), and extreme learning machine (ELM) were constructed. The results show that the four constructed models based on the spectral index can achieve good inversion accuracy, and the red edge band can effectively improve the soil salt inversion accuracy in saline-alkali land with vegetation cover. Based on the obtained results, for bare land, the best model for soil salt inversion is the ELM model, which reaches the determination coefficient (Rv2) of 0.707, the root mean square error RMSEv of 0.290, and the performance deviation ratio (RPD) of 1.852 on the test dataset. However, for agricultural land with vegetation cover, the best model for soil salinity inversion using the vegetation index is the BPNN model, which achieves Rv2 of 0.836, RMSEv of 0.027, and RPD of 2.100 on the test dataset. This study provides technical support for rapid monitoring and inversion of soil salinization and salinization control in irrigation areas.
Geophysical Study of the Diendorf-Boskovice Fault System (Austria)
Authors: Paoletti Valeria , Hintersberger Esther , Schattauer Ingrid , Milano Maurizio , Deidda Gian Piero , Supper Robert
Published: April 2022

We describe here the results of the characterization of subsurface structures in an area of the south-eastern edge of the Bohemian Massif, in Austria by high-resolution geophysical survey techniques and advanced analysis methods of potential fields. The employed methods included potential field multiscale techniques for source-edge location and characterization of sources at depth. Our results confirmed the presence of already known structures: the location of the Diendorf Fault and the Moldanubian Shearzone are clearly recognized in the data at the same location as on the geological maps, even where the Diendorf fault is covered with sediments of the Molasse Basin. In addition, we detected several geological contacts between different rock types in the Bohemian Massif west of the Diendorf Fault. From our results, we were also able to quickly identify and image, without a priori information, previously unknown structures, such as faults with-depth-to-the top of about 500 m and magmatic intrusions about 400 m deep.
Analysis of Multispectral Drought Indices in Central Tunisia
Authors: Farhani Nesrine , Carreau Julie , Kassouk Zeineb , Le Page Michel , Lili Chabaane Zohra , Boulet Gilles
Published: April 2022

Surface water stress remote sensing indices can be very helpful to monitor the impact of drought on agro-ecosystems, and serve as early warning indicators to avoid further damages to the crop productivity. In this study, we compare indices from three different spectral domains: the plant water use derived from evapotranspiration retrieved using data from the thermal infrared domain, the root zone soil moisture at low resolution derived from the microwave domain using the Soil Water Index (SWI), and the active vegetation fraction cover deduced from the Normalized Difference Vegetation Index (NDVI) time series. The thermal stress index is computed from a dual-source model Soil Plant Atmosphere and Remote Evapotranspiration (SPARSE) that relies on meteorological variables and remote sensing data. In order to extend in time the available meteorological series, we compare the use of a statistical downscaling method applied to reanalysis data with the use of the unprocessed reanalysis data. Our study shows that thermal indices show comparable performance overall compared to the SWI at better resolution. However, thermal indices are more sensitive for a drought period and tend to react quickly to water stress.
Optimized Spatial Gradient Transfer for Hyperspectral-LiDAR Data Classification
Authors: Tu Bing , Zhu Yu , Zhou Chengle , Chen Siyuan , Plaza Antonio
Published: April 2022

The classification accuracy of ground objects is improved due to the combined use of the same scene data collected by different sensors. We propose to fuse the spatial planar distribution and spectral information of the hyperspectral images (HSIs) with the spatial 3D information of the objects captured by light detection and ranging (LiDAR). In this paper, we use the optimized spatial gradient transfer method for data fusion, which can effectively solve the strong heterogeneity of heterogeneous data fusion. The entropy rate superpixel segmentation algorithm over-segments HSI and LiDAR to extract local spatial and elevation information, and a Gaussian density-based regularization strategy normalizes the local spatial and elevation information. Then, the spatial gradient transfer model and l1-total variation minimization are introduced to realize the fusion of local multi-attribute features of different sources, and fully exploit the complementary information of different features for the description of ground objects. Finally, the fused local spatial features are reconstructed into a guided image, and the guided filtering acts on each dimension of the original HSI, so that the output maintains the complete spectral information and detailed changes of the spatial fusion features. It is worth mentioning that we have carried out two versions of expansion on the basis of the proposed method to improve the joint utilization of multi-source data. Experimental results on two real datasets indicated that the fused features of the proposed method have a better effect on ground object classification than the mainstream stacking or cascade fusion methods.
Scorepochs: A Computer-Aided Scoring Tool for Resting-State M/EEG Epochs
Authors: Fraschini Matteo , La Cava Simone Maurizio , Rodriguez Giuseppe , Vitale Andrea , Demuru Matteo
Published: April 2022

M/EEG resting-state analysis often requires the definition of the epoch length and the criteria in order to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thereby hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during the epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64 channel system.
Semantic Image Inpainting with Multi-Stage Feature Reasoning Generative Adversarial Network
Authors: Li Guangyao , Li Liangfu , Pu Yingdan , Wang Nan , Zhang Xi
Published: April 2022

Most existing image inpainting methods have achieved remarkable progress in small image defects. However, repairing large missing regions with insufficient context information is still an intractable problem. In this paper, a Multi-stage Feature Reasoning Generative Adversarial Network to gradually restore irregular holes is proposed. Specifically, dynamic partial convolution is used to adaptively adjust the restoration proportion during inpainting progress, which strengthens the correlation between valid and invalid pixels. In the decoding phase, the statistical natures of features in the masked areas differentiate from those of unmasked areas. To this end, a novel decoder is designed which not only dynamically assigns a scaling factor and bias on per feature point basis using point-wise normalization, but also utilizes skip connections to solve the problem of information loss between the codec network layers. Moreover, in order to eliminate gradient vanishing and increase the reasoning times, a hybrid weighted merging method consisting of a hard weight map and a soft weight map is proposed to ensemble the feature maps generated during the whole reconstruction process. Experiments on CelebA, Places2, and Paris StreetView show that the proposed model generates results with a PSNR improvement of 0.3 dB to 1.2 dB compared to other methods.
An Adaptive Topology Management Scheme to Maintain Network Connectivity in Wireless Sensor Networks
Authors: Haq Muhammad Zia Ul , Khan Muhammad Zahid , Rehman Haseeb Ur , Mehmood Gulzar , Binmahfoudh Ahmed , Krichen Moez , Alroobaea Roobaea
Published: April 2022

The roots of Wireless Sensor Networks (WSNs) are tracked back to US military developments, and, currently, WSNs have paved their way into a vast domain of civil applications, especially environmental, critical infrastructure, habitat monitoring, etc. In the majority of these applications, WSNs have been deployed to monitor critical and inaccessible terrains; however, due to their unique and resource-constrained nature, WSNs face many design and deployment challenges in these difficult-to-access working environments, including connectivity maintenance, topology management, reliability, etc. However, for WSNs, topology management and connectivity still remain a major concern in WSNs that hampers their operations, with a direct impact on the overall application performance of WSNs. To address this issue, in this paper, we propose a new topology management and connectivity maintenance scheme called a Tolerating Fault and Maintaining Network Connectivity using Array Antenna (ToMaCAA) for WSNs. ToMaCAA is a system designed to adapt to dynamic structures and maintain network connectivity while consuming fewer network resources. Thereafter, we incorporated a Phase Array Antenna into the existing topology management technologies, proving ToMaCAA to be a novel contribution. This new approach allows a node to connect to the farthest node in the network while conserving resources and energy. Moreover, data transmission is restricted to one route, reducing overheads and conserving energy in various other nodes’ idle listening state. For the implementation of ToMaCAA, the MATLAB network simulation platform has been used to test and analyse its performance. The output results were compared with the benchmark schemes, i.e., Disjoint Path Vector (DPV), Adaptive Disjoint Path Vector (ADPV), and Pickup Non-Critical Node Based k-Connectivity (PINC). The performance of ToMaCAA was evaluated based on different performance metrics, i.e., the network lifetime, total number of transmitted messages, and node failure in WSNs. The output results revealed that the ToMaCAA outperformed the DPV, ADPV, and PINC schemes in terms of maintaining network connectivity during link failures and made the network more fault-tolerant and reliable.