bluuebunny/crossref_metadata_2025 huggingface dataset:
Simple and Robust Detection of CYP2D6 Gene Deletions and Duplications Using CYP2D8P as Reference
Authors: Larsen Jens Borggaard , Jørgensen Steffen
Published: January 2022

Genotyping of the CYP2D6 gene is the most commonly applied pharmacogenetic test globally. Significant economic interests have led to the development of a plurality of assays, available for almost any genotyping platform or DNA detection chemistry. Of all the genetic variants, copy number variations are particular difficult to detect by polymerase chain reaction. Here, we present two simple novel approaches for the identification of samples carrying either deletions or duplications of the CYP2D6 gene; by relative quantification using a singleplex 5′nuclease real-time PCR assay, and by high-resolution melting of PCR products. These methods make use of universal primers, targeting both the CYP2D6 and the reference gene CYP2D8P, which is necessary for the analysis. The assays were validated against a reference method using a large set of samples. The singleplex nature of the 5′nuclease real-time PCR ensures that the primers anneal with equal affinity to both the sequence of the CYP2D6 and the reference gene. This facilitates robust identification of gene deletions and duplications based on the cycle threshold value. In contrast, the high-resolution melting assay is an end-point PCR, where the identification relies on variations between the amount of product generated from each of the two genes.
Topical Pirfenidone-Loaded Liposomes Ophthalmic Formulation Reduces Haze Development after Corneal Alkali Burn in Mice
Authors: Diaz-Palomera Carlos Daniel , Vidal-Paredes Isaac Alejandro , Navarro-Partida Jose , Cid-Hernandez Margarita , Rosales-Rivera Luis Carlos , De la Rosa-Bibiano Ricardo , Monroy-Ramirez Hugo Christian , Santos Arturo , Armendariz-Borunda Juan
Published: January 2022

Corneal chemical burns (CCBs) frequently result in corneal fibrosis or haze, an opacity of the cornea that obstructs vision and induces corneal blindness. Diverse strategies have been employed to prevent or reduce CCB-related corneal haze. In this study, we evaluated the physicochemical characteristics and biologic effects of a topical pirfenidone (PFD)-loaded liposomal formulation (PL) on a corneal alkali burn mice model. We found that PL was appropriate for ocular application due to its physiologic tear pH, osmolarity and viscosity suitable for topical ophthalmic use. Regarding its therapeutic activity, PL-treated mice had significantly reduced haze size and density, corneal edema, corneal thickness, and corneal inflammatory infiltration, in contrast to PFD in aqueous solution (p < 0.01). Importantly, the antifibrotic activity of PL (reduction of corneal haze) was associated with modulation of transforming growth factor (TGF)-β and Interleukin (IL)-1β genes. PL suppressed TGF-β expression and restored normal IL-1β expression in corneal tissue more efficiently in contrast to PFD in aqueous solution. In conclusion, PFD showed essential anti-inflammatory and anti-fibrotic effects in the treatment of alkali burns. Noteworthy, a new formulation of PFD-loaded liposomes remarkably improved these effects, standing out as a promising treatment for corneal haze.
Increased Activities against Biofilms of the Pathogenic Yeast Candida albicans of Optimized Pom-1 Derivatives
Authors: Amann Valerie , Kissmann Ann-Kathrin , Krämer Markus , Krebs Imke , Perez-Erviti Julio A. , Otero-Gonzalez Anselmo J. , Morales-Vicente Fidel , Rodríguez Armando , Ständker Ludger , Weil Tanja , Rosenau Frank
Published: January 2022

Antimicrobial peptides (AMPs) are an alternative group for the therapy of infectious diseases, with activity against a wide range of diverse pathogens. However, classical AMPs have significant side effects in human cells due to their unspecific pore formation in biomembranes. Nevertheless, AMPs are promising therapeutics and can be isolated from natural sources, which include sea and freshwater molluscs. The AMPs identified in these organisms show promising antimicrobial activities, as pathogens are mainly fought by innate defence mechanisms. An auspicious candidate among molluscs is the Cuban freshwater snail Pomacea poeyana, from which the peptides Pom-1 and Pom-2 have been isolated and studied. These studies revealed significant antimicrobial activities for both AMPs. Based on the activities determined, Pom-1 was used for further optimization. In order to meet the emerging requirements of improved anti-biofilm activity against naturally occurring Candida species, the six derivatives Pom-1A to F were developed and investigated. Analysis of the derivatives acting on the most abundant naturally occurring Candida yeast Candida albicans (C. albicans) revealed a strong anti-biofilm activity, especially induced by Pom-1 B, C, and D. Furthermore, a moderate decrease in the metabolic activity of planktonic yeast cells was observed.
Human Rights and Democracy—Obligations and Delusions
Authors: Kolstad Hans
Published: January 2022

Based on today’s compromises with human rights and the numerous violations of them, which for several countries seems to be the rule rather than an exception, this article discusses the cause of the delusions that in today’s politics are attached to human rights. An analysis is made of the nature of human rights understood as something common and universal for all people. On this basis, a division of human rights is proposed, which at the same time means limiting them to perfect, imperfect and adventitious rights. Central to the discussion is the question of how the normative element of human rights should be understood. This article distinguishes between two approaches to the question, where one is identified as a source of current misconceptions about human rights, while the other is highlighted as a possible answer to key challenges facing democracy.
Local and Remote Conformational Switching in 2-Fluoro-4-Hydroxy Benzoic Acid
Authors: Góbi Sándor , Balbisi Mirjam , Tarczay György
Published: January 2022

In this work, 2-F-4-OH benzoic acid was isolated in Ar matrices and conformational changes were induced by near-IR irradiating the sample. Upon deposition, three conformers could be observed in the matrix, denoted as A1, A2, and D1, respectively. A1 and A2 are trans carboxylic acids, i.e., there is an intramolecular H bond between the H and the carbonyl O atoms in the COOH group, whereas D1 is a cis carboxylic acid with an intramolecular H bond between the F atom and the H atom in the COOH group, which otherwise has the same structure as A1. The difference between A1 and A2 is in the orientation of the carbonyl O atom with regard to the F atom, i.e., whether they are on the opposite or on the same side of the molecule, respectively. All three conformers have their H atom in their 4-OH group, facing the opposite direction with regard to the F atom. The stretching overtones of the 4-OH and the carboxylic OH groups were selectively excited in the case of each conformer. Unlike A2, which did not show any response to irradiation, A1 could be converted to the higher energy form D1. The D1 conformer spontaneously converts back to A1 via tunneling; however, the conversion rate could be significantly increased by selectively exciting the OH vibrational overtones of D1. Quantum efficiencies have been determined for the ‘local’ or ‘remote’ excitations, i.e., when the carboxylic OH or the 4-OH group is excited in order to induce the rotamerization of the carboxylic OH group. Both ‘local’ and ‘remote’ conformational switching are induced by the same type of vibration, which allows for a direct comparison of how much energy is lost by energy dissipation during the two processes. The experimental findings indicate that the ‘local’ excitation is only marginally more efficient than the ‘remote’ one.
Machine Learning Based Low-Cost Optical Performance Monitoring in Mode Division Multiplexed Optical Networks
Authors: Saif Waddah S. , Ragheb Amr M. , Esmail Maged A. , Marey Mohamed , Alshebeili Saleh A.
Published: January 2022

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature.
Identification of Model Particle Mixtures Using Machine-Learning-Assisted Laser Diffraction
Authors: Villegas Arturo , Quiroz-Juárez Mario A. , U’Ren Alfred B. , Torres Juan P. , León-Montiel Roberto de J.
Published: January 2022

We put forward and demonstrate with model particles a smart laser-diffraction analysis technique aimed at particle mixture identification. We retrieve information about the size, shape, and ratio concentration of two-component heterogeneous model particle mixtures with an accuracy above 92%. We verify the method by detecting arrays of randomly located model particles with different shapes generated with a Digital Micromirror Device (DMD). In contrast to commonly-used laser diffraction schemes—In which a large number of detectors are needed—Our machine-learning-assisted protocol makes use of a single far-field diffraction pattern contained within a small angle (∼0.26°) around the light propagation axis. Therefore, it does not need to analyze particles of the array individually to obtain relevant information about the ensemble, it retrieves all information from the diffraction pattern generated by the whole array of particles, which simplifies considerably its implementation in comparison with alternative schemes. The method does not make use of any physical model of scattering to help in the particle characterization, which usually adds computational complexity to the identification process. Because of its reliability and ease of implementation, this work paves the way towards the development of novel smart identification technologies for sample classification and particle contamination monitoring in industrial manufacturing processes.
Induction of Metabolic Changes in Amino Acid, Fatty Acid, Tocopherol, and Phytosterol Profiles by Exogenous Methyl Jasmonate Application in Tomato Fruits
Authors: Meza Silvia Leticia Rivero , de Castro Tobaruela Eric , Pascoal Grazieli Benedetti , Magalhães Hilton César Rodrigues , Massaretto Isabel Louro , Purgatto Eduardo
Published: January 2022

Methyl jasmonate hormone can stimulate the production of several metabolites responsible for improving fruit quality and nutritional attributes related to human health. In this context, efforts to manipulate tomatoes, such as using hormonal treatment to increase metabolite levels essential to plant growth and human nutrition, have received considerable attention. The aim of this study was to show the impact of metabolic profile on fruit quality and nutritional properties under exogenous methyl jasmonate during fruit ripening. The treatments were performed using 100 ppm of methyl jasmonate and 100 ppm of gaseous ethylene over 24 h. Ethylene emission, fruit surface color and metabolomics analysis were measured at 4, 10, and 21 days after harvest, considering the untreated fruits as control group. Methyl jasmonate induced the production of amino acids—mainly glutamine, glutamic acid and γ-aminobutyric acid (at least 14-fold higher)—and fatty acids—mainly oleic, linoleic, and linolenic acids (at least three-fold higher than untreated fruits); while exogenous ethylene predominantly affected sugar metabolism, increasing the levels of fructose, mannose and glucose to at least two-fold that levels in the untreated fruits. Additionally, methyl jasmonate significantly affected secondary metabolites, inducing by at least 80% the accumulation of α-tocopherol and β-sitosterol in fully ripe fruits. Our results suggest that the postharvest application of the hormone methyl jasmonate can contribute to the sensory characteristics and increase the nutritional value of the fruits since important changes related to the tomato metabolome were associated with compounds responsible for the fruit quality and health benefits.
Enantiomeric Composition, Antioxidant Capacity and Anticholinesterase Activity of Essential Oil from Leaves of Chirimoya (Annona cherimola Mill.)
Authors: Valarezo Eduardo , Ludeña Jeannette , Echeverria-Coronel Estefanía , Cartuche Luis , Meneses Miguel Angel , Calva James , Morocho Vladimir
Published: January 2022

Annona cherimola Mill. is a native species of Ecuador cultivated worldwide for the flavor and properties of its fruit. In this study, hydrodistillation was used to isolate essential oil (EO) of fresh Annona cherimola leaves collected in Ecuadorian Sierra. The EO chemical composition was determined using a non-polar and a polar chromatographic column and enantiomeric distribution with an enantioselective column. The qualitative analysis was carried out by gas chromatography coupled to a mass spectrometer and quantitative analysis using gas chromatography equipped with a flame ionization detector. The antibacterial potency was assessed against seven Gram-negative bacteria and one Gram-positive bacterium. ABTS and DPPH assays were used to evaluate the radical scavenging properties of the EO. Spectrophotometric method was used to measure acetylcholinesterase inhibitory activity. GC-MS analysis allowed us to identify more than 99% of the EO chemical composition. Out of the fifty-three compounds identified, the main were germacrene D (28.77 ± 3.80%), sabinene (3, 9.05 ± 1.69%), β-pinene (4, 7.93 ± 0.685), (E)-caryophyllene (10.52 ± 1.64%) and bicyclogermacrene (11.12 ± 1.39%). Enantioselective analysis showed the existence of four pairs of enantiomers, the (−)-β-Pinene (1S, 5S) was found pure (100%). Chirimoya essential oil exhibited a strong antioxidant activity and a very strong anticholinesterase potential with an IC50 value of 41.51 ± 1.02 µg/mL. Additionally, EO presented a moderate activity against Campylobacter jejuni and Klebsiella pneumoniae with a MIC value of 500 μg/mL.
Effect of Light Curing Distance on Microhardness Profiles of Bulk-Fill Resin Composites
Authors: Hasanain Fatin A. , Nassar Hani M. , Ajaj Reem A.
Published: January 2022

Bulk-fill (BF) dental resin composites are made to be polymerized in increments of up to 5 mm rather than the 2 mm increment recommended for conventional composites. This project aimed to determine microhardness (MH) profiles of BF resin composites at different depths and varying light cure (LC) distances from the light source in an attempt to mimic varying clinical situations. Forty-eight cylindrical specimens (4 mm diameter and 6 mm height) were prepared from 3 BF composites: Tetric N-Ceram Bulk-Fill (TBF), Filtek One Bulk-Fill (FBF), and Sonic-Fill 2 (SF2). Four different distances (0, 2, 4, and 6 mm) from the LC unit were investigated. Vickers MH was measured at the top and bottom of the samples and at every 1 mm, by creating 3 indentations at each depth. The bottom-top microhardness ratio (MHR) and percentage reduction in MHR were also measured. Data was analyzed using mixed-model repeated-measure ANOVA at 0.05 significance level. The main variables effects “material, LC distance, and depth” were significant (p < 0.001). Increasing LC distance and the depth of the tested BF significantly affected Vickers MH and MHR. None of the tested BF materials had sufficient MHR at the depths of 4–6 mm. SF2 showed the least MHR reduction.
A Refined Prediction Parameter for Molecular Alignability in Stretched Polymers and a New Light-Harvesting Material for AlGaAs Photovoltaics
Authors: Hohgardt Manuel , Gädeke Franka Elisabeth , Wegener Lucas , Walla Peter Jomo
Published: January 2022

Light-harvesting concentrators have a high potential to make highly efficient but precious energy converters, such as multijunction photovoltaics, more affordable for everyday applications. They collect sunlight, including diffusively scattered light, on large areas and redirect it to much smaller areas of the highly efficiency solar cells. Among the best current concepts are pools of randomly oriented light-collecting donor molecules that transfer all excitons to few aligned acceptors reemitting the light in the direction of the photovoltaics. So far, this system has only been realized for the 350–550 nm wavelength range, suitable for AlGaInP photovoltaics. This was achieved by using acceptor molecules that aligned during mechanical stretching of polymers together with donors, that stay random in that very same material and procedure. However, until recently, very little was known about the factors that are responsible for the alignability of molecules in stretched polymers and therefore it was difficult to find suitable donors and acceptors, as well as for other spectral ranges. Recently, a structural parameter was introduced with a high predictivity for the alignability of molecules that contain rigid band-like structures or linear aromatic π-systems. However, for light concentrators in more red spectral ranges, molecular systems often contain larger and extended, planar-like π-systems for which the previously reported parameter is not directly applicable. Here, we present a refined prediction parameter also suitable for larger plane-like structures. The new parameter depends on the number of in-plane atoms divided by out-of-plane atoms as determined by computational geometry optimization and additionally the planar aspect ratio for molecules that contain only in-plane atoms. With the help of this parameter, we found a new system that can efficiently collect and redirect light for the second 500–700 nm AlGaAs layer of current world-record multijunction photovoltaics. Similarly, as the previously reported system for the blue-green layer, it has also overall absorption and re-directioning quantum efficiencies close to 80–100%. Both layers, together, already cover about 75% of the energy in the solar spectrum.
Coarse Grained Modeling of Multiphase Flows with Surfactants
Authors: Nguyen Thao X. D. , Vu Tuan V. , Razavi Sepideh , Papavassiliou Dimitrios V.
Published: January 2022

Coarse-grained modeling methods allow simulations at larger scales than molecular dynamics, making it feasible to simulate multifluid systems. It is, however, critical to use model parameters that represent the fluid properties with fidelity under both equilibrium and dynamic conditions. In this work, dissipative particle dynamics (DPD) methods were used to simulate the flow of oil and water in a narrow slit under Poiseuille and Couette flow conditions. Large surfactant molecules were also included in the computations. A systematic methodology is presented to determine the DPD parameters necessary for ensuring that the boundary conditions were obeyed, that the oil and water viscosities were represented correctly, and that the velocity profile for the multifluid system agreed with the theoretical expectations. Surfactant molecules were introduced at the oil–water interface (sodium dodecylsulfate and octaethylene glycol monododecyl ether) to determine the effects of surface-active molecules on the two-phase flow. A critical shear rate was found for Poiseuille flow, beyond which the surfactants desorbed to form the interface forming micelles and destabilize the interface, and the surfactant-covered interface remained stable under Couette flow even at high shear rates.
Advanced Composites Based on Sea Buckthorn Carotenoids for Mayonnaise Enrichment
Authors: Roman Diana , Condurache (Lazăr) Nina Nicoleta , Stănciuc Nicoleta , Andronoiu Doina Georgeta , Aprodu Iuliana , Enachi Elena , Barbu Vasilica , Bahrim Gabriela Elena , Stanciu Silvius , Râpeanu Gabriela
Published: January 2022

This study aimed at the extraction and encapsulation of the carotenoids from sea buckthorn fruits and obtaining value-added mayonnaise. First, the carotenoids from sea buckthorn fruits were extracted using ultrasound-assisted extraction. Then, they were microencapsulated through complex coacervation and freeze-drying techniques using different wall material combinations. Two powders were obtained and analyzed in terms of encapsulation efficiency, total carotenoid content, antioxidant activity, stability of phytochemicals and color, morphological structure, and in vitro digestibility. All results pointed out that the carotenoid molecules were successfully encapsulated within the mixture of alginate, agar, and chitosan, with a 61.17 ± 0.89% encapsulation efficiency. To probe the functionality, the powder was added into mayonnaise in 2.5% and 5% amounts. The obtained mayonnaise samples were characterized in terms of phytochemical and antioxidant activity properties with their storage stability and texture, color, and sensory characteristics. A significant increase of total carotenoid content and antioxidant activity compared to the control sample was observed. The addition of powder also led to improved texture by increasing the firmness and adhesion. In addition, the sensory evaluation indicated an improved color and overall acceptability of the value-added mayonnaise. Thus, sea buckthorn extracts may be considered as valuable ingredients for the development of added-value food products.
Temperature-Dependent Conformation Behavior of Isolated Poly(3-hexylthiopene) Chains
Authors: Pantawane Sanwardhini , Gekle Stephan
Published: January 2022

We use atomistic as well as coarse-grained molecular dynamics simulations to study the conformation of a single poly(3-hexylthiopene) chain as a function of temperature. We find that mainly bundle and toroid structures appear with bundles becoming more abundant for decreasing temperatures. We compare an atomistic and a Martini-based coarse-grained model which we find in very good agreement. We further illustrate how the temperature dependence of P3HT can be connected to that of simple Lennard–Jones model polymers in a vacuum. Upon adding solvent (THF) we observe the occurrence of a prominent swelling of the molecular size at a temperature of about 220 K. This swelling is in close agreement with the interpretation of recent spectroscopic experiments which allows us to explain the experimental observations by an increased frequency of bundle structures.
Improving Ductility for Composite Beams Reinforced with GFRP Tubes by Using Rebars/Steel Angles
Authors: Yuan Jiansong , Gao Danying , Zhang Yin , Zhu Haitang
Published: January 2022

The ductile behaviour of composite beams reinforced with glass fibre-reinforced polymer (GFRP) pultruded rectangular tubes was investigated in this paper. The composite beams were reinforced with GFRP tubes and different steel products, aiming to improve their ductility by using steel products. The main parameters were the types of the steel reinforcement, namely rebars and steel angles. The flexural behaviour of four beam specimens was tested by using a four-point bending test. The experimental results show that the yield load of the specimens was determined by the steel products and the ultimate load was controlled by the GFRP tubes. Two ductility methods (displacement ductility and energy ductility) were used to evaluate the change of the ductility. Both the methods confirmed that the ductility of the composite beam was improved in varying degrees by using rebars and steel angles. Moreover, the analysis shows that improving the yield load or decreasing the ultimate load of the composite beams contributed to the improvement of the ductility.
Physical and Chemical Macroscopic Processes in Wooden Construction Materials of Buildings during WUI Fires: Recent and Advanced Developments
Authors: Baranovskiy Nikolay Viktorovich , Malinin Aleksey Olegovich
Published: January 2022

Forest fires are one of the strongest natural phenomena, occurring both for natural and man-made reasons. Forest fires entail not only economic losses, but also affect the ecological, biological and demographic picture of the region of its origin and far beyond its borders. Around the world, work is underway to develop effective methods for predicting the impact of a forest fire on the environment, the speed and direction of propagation and impact on various infrastructure facilities near the wildland–urban interface (WUI). The purpose of the work is to review recent and significant research works on the physical and chemical processes in wooden construction materials during WUI fires. As a result of the analysis of literary sources, works devoted to the modeling and experimental study of various physical and chemical processes, namely, the impact of forest fires on residential and industrial facilities, heat and mass transfer in structural materials, drying processes, pyrolysis, and ignition of structural materials, are highlighted. The results of the analysis of literature sources and promising directions are presented in the Discussion section. The formulated conclusions are presented in the Conclusion section. The main conclusion is that the existing experimental and theoretical work can be integrated into the developed deterministic–probabilistic approach for predicting the impact of a forest fire on buildings.
Selection of Agricultural Machinery Based on Improved CRITIC-Entropy Weight and GRA-TOPSIS Method
Authors: Lu Haonan , Zhao Yongman , Zhou Xue , Wei Zikai
Published: January 2022

In view of the problems existing in the process of agricultural machinery selection, such as inadequate decision-making information, strong subjectivity and quantification difficulty of the index weight assignment, and accuracy deficiency of the selection results, a model on the selection of agricultural machinery based on the improved CRITIC-entropy weight and GRA-TOPSIS method was established in this study. Through analysis, based on the construction of a comprehensive evaluation index system for the selection of agricultural machinery, the combined weight value was determined by combining the weights obtained using the improved CRITIC method and the weights obtained using the entropy weight method. The grey relational analysis method was also combined with the TOPSIS method. The power machinery combination with 88.2 and 73.5 kW of the 68th Regiment of the Fourth Division of Xinjiang Production and Construction Corps was used as an example for verification to determine the optimal power machinery combination. Results indicated that the ranking results were consistent, and the GRA-TOPSIS method was of the greatest degree of discrimination, which was conducive to the selection and evaluation of agricultural machinery equipment. Moreover, the equipment selection results were determined after the comprehensive ranking of machinery types under the different subjective preferences of decision makers was performed.
Bio-Oil: The Next-Generation Source of Chemicals
Authors: Machado Henrique , Cristino Ana F. , Orišková Sofia , Galhano dos Santos Rui
Published: January 2022

Bio-oil, although rich in chemical species, is primarily used as fuel oil, due to its greater calorific power when compared to the biomass from which it is made. The incomplete understanding of how to explore its chemical potential as a source of value-added chemicals and, therefore, a supply of intermediary chemical species is due to the diverse composition of bio-oil. Being biomass-based, making it subject to composition changes, bio-oil is obtained via different processes, the two most common being fast pyrolysis and hydrothermal liquefaction. Different methods result in different bio-oil compositions even from the same original biomass. Understanding which biomass source and process results in a particular chemical makeup is of interest to those concerned with the refinement or direct application in chemical reactions of bio-oil. This paper presents a summary of published bio-oil production methods, origin biomass, and the resulting composition.
From Secular Religiosity to Cultural Disjunctions: Visions of Post-Traditional Jewishness in the Thought of Paul Mendes-Flohr
Authors: Shonkoff Sam S. B.
Published: January 2022

“Post-traditional” Jewishness—a distinctively modern condition wherein past sources of theological authority and religious normativity are no longer self-evident—has been one of the most abiding interests in Paul Mendes-Flohr’s writings for more than four decades. The present article traces the contours of this concern over time. In a number of publications between 1978 and 1987, Mendes-Flohr highlights “secular religiosity” as a manifestation of post-traditional Jewishness, exemplified by figures such as Martin Buber and Franz Rosenzweig. These early writings intimate the possibility of a critical and yet nonetheless integrated Jewish religious subject, grounded hermeneutically in Jewish sources and sociologically in the Jewish community of destiny (Schicksalsgemeinschaft). Starting in the late 1980s, however, Mendes-Flohr’s representations of post-traditional Jewishness begin to emphasize greater degrees of complexity and, indeed, fragmentation. These later writings gesture less to visions of secular religiosity than toward postures of “undogmatic, pluralistic, and open” self-reflectivity before the ever-changing faces of reality. Throughout this rich trajectory in Mendes-Flohr’s thought, though, we see that he returns continually—and ever more trenchantly—to dialogical life as a grounding principle.
Siamese Detail Difference and Self-Inverse Network for Forest Cover Change Extraction Based on Landsat 8 OLI Satellite Images
Authors: Guo Yantao , Long Tengfei , Jiao Weili , Zhang Xiaomei , He Guojin , Wang Wei , Peng Yan , Xiao Han
Published: January 2022

In the context of carbon neutrality, forest cover change detection has become a key topic of global environmental monitoring. As a large-scale monitoring technique, remote sensing has received obvious attention in various land cover observation applications. With the rapid development of deep learning, remote sensing change detection combined with deep neural network has achieved high accuracy. In this paper, the deep neural network is used to study forest cover change with Landsat images. The main research ideas are as follows. (1) A Siamese detail difference neural network is proposed, which uses a combination of concatenate weight sharing mode and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and improves the utilization rate of the sample data set. The experimental results on Landsat 8 images show that the proposed method outperforms several Siamese neural network methods in forest cover change extraction.
Evaluating the Potential of Different Evapotranspiration Datasets for Distributed Hydrological Model Calibration
Authors: Guo Xiao , Wu Zhiyong , He Hai , Xu Zhengguang
Published: January 2022

Evapotranspiration (ET), a key component of the hydrological cycle, has a direct impact on runoff and water balance. Various global satellite-based and numerical datasets provide continuous and high spatiotemporal resolution data, which makes it possible to calibrate hydrological parameters against ET. However, the accuracy of ET datasets varies with region and algorithm, introducing uncertainties in hydrological parameter calibration. This study focused on evaluating the potential of different ET datasets in the calibration of distributed hydrological model parameters. Five different ET datasets (PML, SEBAL, EB-ET, GLASS, REA-ET) were evaluated using the water balance method to explore the effect of intrinsic dataset accuracy on applications. The benchmark calibration scheme calibrated parameters by using observed streamflow data from the outlet. Two calibration schemes were proposed to take advantage of the temporal dynamics and spatial patterns of the raw ET datasets. The results show that the model parameters calibrated by all selected ET datasets produced satisfactory results in streamflow simulations. These results were dependent on the calibration schemes and accuracy of ET datasets. Overall, the scheme calibrated by using temporal dynamics of ET at the grid scale provided better streamflow simulations at the basin outlet than the scheme calibrated by using spatial patterns of ET at the basin scale. Three metrics (bias, root mean square error [RMSE], and correlation coefficient [R]) showed that there is a high potential for selected ET datasets to improve soil moisture simulations, as compared to the benchmark scheme. Parameters calibrated by EB-ET and PML datasets provided the best performance in the simulation of streamflow at the outlet and the sub-basin scale. The calibration case with the SEBAL dataset showed the highest potential to improve soil moisture simulation. The annual average ET estimates of these three datasets were closest to the water balance-based ET values.
A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration
Authors: Li Shuo , Lv Xiaolei , Ren Jian , Li Jian
Published: January 2022

The co-registration between SAR and optical images is a challenging task because of the speckle noise of SAR and the nonlinear radiation distortions (NRD), particularly in the one-look situation. In this paper, we propose a novel density descriptor based on the histogram of oriented primary edge structure (HOPES) for the co-registration of SAR and optical images, aiming to describe the shape structure of patches more firm. In order to extract the primary edge structure, we develop the novel multi-scale sigmoid Gabor (MSG) detector and a primary edge fusion algorithm. Based on the HOPES, we propose the co-registration method. To obtain stable and uniform keypoints, the non-maximum suppressed SAR-Harris (NMS-SAR-Harris) and deviding grids methods are used. NMS-SSD fast template matching and fast sample consensus (FSC) algorithm are used to further complete and optimize matching. We use two one-look simulated SAR images to demonstrate that the signal-to-noise ratio (SNR) of MSG is more than 10 dB higher than other state-of-the-stage detectors; the binary edge maps and F-score show that MSG has more accurate positioning performance. Compared with the other state-of-the-stage co-registration methods, the image co-registration results obtained on seven pairs of test images show that, the correct match rate (CMR) and the root mean squared error (RMSE) improve by more than 25% and 15% on average, respectively. It is experimentally demonstrated that the HOPES is robust against speckle noise and NRD, which can effectively improve the matching success rate and accuracy.
Detailed Cartography of Cotopaxi’s 1877 Primary Lahar Deposits Obtained by Drone-Imagery and Field Surveys in the Proximal Northern Drainage
Authors: Andrade S. Daniel , Saltos Emilia , Nogales Valeria , Cruz Sebastián , Lee Gareth , Barclay Jenni
Published: January 2022

Cotopaxi is an active volcano in Ecuador, whose eruptions are characterized by producing destructive primary lahars which represent a major risk for the country. The hazard assessment related to such lahars relies largely on the knowledge of the latest event, which occurred on 26 June 1877, for either scenario definition or simulation calibration. A detailed (1:5000 scale) cartography of the deposits belonging to that eruption has been obtained in the proximal northern drainage of Cotopaxi. The cartography was performed through a combination of geological fieldwork, as well as the analysis and interpretation of high-definition imagery obtained by drone surveys combined with the Structure from Motion technology for image processing. Such imagery included red and green visible bands, and a near-infrared band, which allowed the obtention of NDVI imagery where the primary lahar deposits were identified and cartographed with support of fieldwork data. Both data sources are mutually complementary, and the final cartography would be impossible if any of them were not available. The results obtained represent a significant advance for the level of detail with respect to previous cartographic works. Moreover, they should allow an improved calibration of the new generation of numerical models that simulate lahar flow for hazard assessment at Cotopaxi.
Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean
Authors: Chen Jianqiang , Gong Xun , Guo Xinyu , Xing Xiaogang , Lu Keyu , Gao Huiwang , Gong Xiang
Published: January 2022

Subsurface chlorophyll maxima (SCMs), commonly occurring beneath the surface mixed layer in coastal seas and open oceans, account for main changes in depth-integrated primary production and hence significantly contribute to the global carbon cycle. To fill the gap of previous methods (in situ measurement, remote sensing, and the extrapolating function based on surface-ocean data) for obtaining SCM characteristics (intensity, depth, and thickness), we developed an improved deep neural network (IDNN) model using a Gaussian radial basis activation function to retrieve the vertical profile of chlorophyll a concentration (Chl a) and associated SCM characteristics from surface-ocean data. The annually averaged SCM depth was further incorporated into the bias term and the Gaussian activation function to improve the estimation accuracy of the IDNN model. Based on the Biogeochemical-Argo (BGC-Argo) data acquired for three regions in the northwestern Pacific Ocean, vertical Chl a profiles produced by our improved DNN model using sea surface Chl a and sea surface temperature (SST) were in good agreement with the observations, especially in regions with low surface Chl a. Compared to other neural-network-based models with one hidden layer and a sigmoid activation function, the IDNN model retrieved vertical Chl a profiles well in more eutrophic subpolar regions. Furthermore, the application of the IDNN model to infer vertical Chl a profiles from remote-sensing information was validated in the northwestern Pacific Ocean.
Flow Velocity Variations and Surface Change of the Destabilised Plator Rock Glacier (Central Italian Alps) from Aerial Surveys
Authors: Bearzot Francesca , Garzonio Roberto , Colombo Roberto , Crosta Giovanni Battista , Di Mauro Biagio , Fioletti Matteo , Morra Di Cella Umberto , Rossini Micol
Published: January 2022

Flow velocities were measured on the Plator rock glacier in the Central Italian Alps using a correlation image analysis algorithm on orthophotos acquired by drones between the years 2016 and 2020. The spatial patterns of surface creep were then compared to the Bulk Creep Factor (BCF) spatial variability to interpret the rock glacier dynamics as a function of material properties and geometry. The rock glacier showed different creep rates in the rooting zone (0.40–0.90 m/y) and in the frontal zone (>4.0 m/y). Close to the rock glacier front, the BCF assumed the highest values, reaching values typical of rock glaciers experiencing destabilisation. Conversely, in the rooting zone the small rates corresponded to lowest BCFs, about five times smaller than in the frontal zone. The Plator rock glacier revealed a substantial advancement from 1981 to 2020 and distinct geomorphological features typical of rock glaciers exhibiting destabilising processes. Given the fast-moving phase, the advancement of both the front line and the front toe of the rock glacier, and the contrasting spatial distribution in the BCFs, the Plator could be considered a destabilised rock glacier.
A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities
Authors: Alibabaei Khadijeh , Gaspar Pedro D. , Lima Tânia M. , Campos Rebeca M. , Girão Inês , Monteiro Jorge , Lopes Carlos M.
Published: January 2022

Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.
Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images
Authors: Liang Shuang , Gong Zhaoning , Wang Yingcong , Zhao Jiafu , Zhao Wenji
Published: January 2022

Submerged aquatic vegetation (SAV) is one of the most important biological groups in shallow lakes ecosystems, and it plays a vital role in stabilizing the structure and function of water ecosystems. The study area of this research is Baiyangdian, which is a typical macrophytic lake with complex land cover types. This research aims to solve the low accuracy problem of the remote sensing extraction of SAV, which is mainly caused by water level fluctuations, differences in life-history characteristics, and mixed-pixel phenomena. Here, we developed a phenology–pixel method to determine the spatial distribution of SAV and the start and end dates of its growing season by using all Sentinel-2 images collected over a year on the Google Earth Engine platform. The experimental results show the following: (1) The phenology–pixel algorithm can effectively identify the maximum spatial distribution and growth period of submerged aquatic vegetation in Baiyangdian Lake throughout the year. The unique normalized difference vegetation index (NDVI) peak characteristics of Potamogeton crispus from March to May were used to effectively distinguish it from the low Phragmites australis population. Textural features based on the modified normalized difference water index (MNDWI) index effectively removed the mixed-pixel phenomenon of macrophytic lakes (such as dikes and sparse reeds). (2) A complete five-day interval NDVI time-series dataset was obtained, which removes potential noise on the temporal scale and fills in noisy observations by the harmonic analysis of time series (HANTS) method. We determined the two phenological periods of typical SAV by analyzing the intrayear variation characteristics of NDVI and MNDWI. (3) Using field-survey data for accuracy verification, the overall accuracy of our method was determined to be 94.8%, and the user’s accuracy and producer’s accuracy were 93.3% and 87.3%, respectively. Determining the temporal and spatial distribution of different SAV populations provides important technical support for actively promoting the maintenance and reconstruction of lake and reservoir ecosystems.
3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms
Authors: Dehghan Firoozabadi Ali , Irarrazaval Pablo , Adasme Pablo , Zabala-Blanco David , Játiva Pablo Palacios , Azurdia-Meza Cesar
Published: January 2022

Multiple simultaneous sound source localization (SSL) is one of the most important applications in the speech signal processing. The one-step algorithms with the advantage of low computational complexity (and low accuracy), and the two-step methods with high accuracy (and high computational complexity) are proposed for multiple SSL. In this article, a combination of one-step-based method based on the generalized eigenvalue decomposition (GEVD), and a two-step-based method based on the adaptive generalized cross-correlation (GCC) by using the phase transform/maximum likelihood (PHAT/ML) filters along with a novel T-shaped circular distributed microphone array (TCDMA) is proposed for 3D multiple simultaneous SSL. In addition, the low computational complexity advantage of the GCC algorithm is considered in combination with the high accuracy of the GEVD method by using the distributed microphone array to eliminate spatial aliasing and thus obtain more appropriate information. The proposed T-shaped circular distributed microphone array-based adaptive GEVD and GCC-PHAT/ML algorithms (TCDMA-AGGPM) is compared with hierarchical grid refinement (HiGRID), temporal extension of multiple response model of sparse Bayesian learning with spherical harmonic (SH) extension (SH-TMSBL), sound field morphological component analysis (SF-MCA), and time-frequency mixture weight Bayesian nonparametric acoustical holography beamforming (TF-MW-BNP-AHB) methods based on the mean absolute estimation error (MAEE) criteria in noisy and reverberant environments on simulated and real data. The superiority of the proposed method is presented by showing the high accuracy and low computational complexity for 3D multiple simultaneous SSL.
Vacuum Based Gas Sensing Material Characterization System for Precise and Simultaneous Measurement of Optical and Electrical Responses
Authors: Wei Jie , Zhao Meng , Wang Cong , Wang Jun , Ye Jian-Min , Wei Yu-Chen , Li Zhe-Yi , Zhao Run , Liu Guo-Zhen , Geng Yan-Hong , Wang Rui , Xiao Hui-Dong , Li Ying , Li Chao-Ya , Gao Zhi-Qiang , Gao Ju
Published: January 2022

Gas sensing performance characterization systems are essential for the research and development of gas sensing materials and devices. Although existing systems are almost completely automatically operated, the accuracies of gas concentration control and of pressure control and the ability to simultaneously detect different sensor signals still require improvement. In this study, a high-precision gas sensing material characterization system is developed based on vacuum technology, with the objective of enabling the precise and simultaneous measurement of electrical responses. Because of the implementation of vacuum technology, the gas concentration control accuracy is improved more than 1600 times, whereas the pressure of the test ambient condition can be precisely adjusted between vacuum and 1.2 bar. The vacuum-assisted gas-exchanging mechanism also enables the sensor response time to be determined more accurately. The system is capable of performing sensitivity, selectivity, and stability tests and can control the ambient relative humidity in a precise manner. More importantly, the levels of performance of three different optical signal measurement set-ups were investigated and compared in terms of detection range, linearity, noise, and response time, based on which of their scopes of application were proposed. Finally, single-period and cyclical tests were performed to examine the ability of the system to detect optical and electrical responses simultaneously, both at a single wavelength and in a spectral region.