bluuebunny/crossref_metadata_2025 huggingface dataset:
Studying the Complex Formation of Sulfonatocalix[4]naphthalene and Meloxicam towards Enhancing Its Solubility and Dissolution Performance
Authors: Al Hujran Tayel A. , Magharbeh Mousa K. , Al-Gharabli Samer , Haddadin Rula R. , Al Soub Manal N. , Tawfeek Hesham M.
Published: July 2021

The interaction between meloxicam and sulfonatocalix[4]naphthalene was investigated to improve the meloxicam solubility and its dissolution performance. Solubility behavior was investigated in distilled water (DW) and at different pH conditions. Besides, solid systems were prepared in a 1:1 molar ratio using coevaporate, kneading, and simple physical mixture techniques. Further, they were characterized by PXRD, FT-IR, DCS, and TGA. In vitro dissolution rate for coevaporate, kneaded, and physical mixture powders were also investigated. Solubility study revealed that meloxicam solubility significantly increased about 23.99 folds at phosphate buffer of pH 7.4 in the presence of sulfonatocalix[4]naphthalene. The solubility phase diagram was classified as AL type, indicating the formation of 1:1 stoichiometric inclusion complex. PXRD, FT-IR, DCS, and TGA pointed out the formation of an inclusion complex between meloxicam and sulfonatocalix[4]naphthalene solid powders prepared using coevaporate technique. In addition, in vitro meloxicam dissolution studies revealed an improvement of the drug dissolution rate. Furthermore, a significantly higher drug release (p ≤ 0.05) and a complete dissolution was achieved during the first 10 min compared with the other solid powders and commercial meloxicam product. The coevaporate product has the highest increasing dissolution fold and RDR10 in the investigated media, with average values ranging from 5.4–65.28 folds and 7.3–90.7, respectively. In conclusion, sulfonatocalix[4]naphthalene is a promising host carrier for enhancing the solubility and dissolution performance of meloxicam with an anticipated enhanced bioavailability and fast action for acute and chronic pain disorders.
Orodispersible Film Loaded with Enterococcus faecium CRL183 Presents Anti-Candida albicans Biofilm Activity In Vitro
Authors: Lordello Virgínia Barreto , Meneguin Andréia Bagliotti , de Annunzio Sarah Raquel , Taranto Maria Pía , Chorilli Marlus , Fontana Carla Raquel , Cavallini Daniela Cardoso Umbelino
Published: July 2021

Background: Probiotic bacteria have been emerging as a trustworthy choice for the prevention and treatment of Candida spp. infections. This study aimed to develop and characterize an orodispersible film (ODF) for delivering the potentially probiotic Enterococcus faecium CRL 183 into the oral cavity, evaluating its in vitro antifungal activity against Candida albicans. Methods and Results: The ODF was composed by carboxymethylcellulose, gelatin, and potato starch, and its physical, chemical, and mechanical properties were studied. The probiotic resistance and viability during processing and storage were evaluated as well as its in vitro antifungal activity against C. albicans. The ODFs were thin, resistant, and flexible, with neutral pH and microbiologically safe. The probiotic resisted the ODF obtaining process, demonstrating high viability (>9 log10 CFU·g−1), up to 90 days of storage at room temperature. The Probiotic Film promoted 68.9% of reduction in fungal early biofilm and 91.2% in its mature biofilm compared to the group stimulated with the control film. Those results were confirmed through SEM images. Conclusion: The probiotic ODF developed is a promising strategy to prevent oral candidiasis, since it permits the local probiotic delivery, which in turn was able to reduce C. albicans biofilm formation.
Recent Advances in Liposomal-Based Anti-Inflammatory Therapy
Authors: van Alem Carla M. A. , Metselaar Josbert M. , van Kooten Cees , Rotmans Joris I.
Published: July 2021

Liposomes can be seen as ideal carriers for anti-inflammatory drugs as their ability to (passively) target sites of inflammation and release their content to inflammatory target cells enables them to increase local efficacy with only limited systemic exposure and adverse effects. Nonetheless, few liposomal formulations seem to reach the clinic. The current review provides an overview of the more recent innovations in liposomal treatment of rheumatoid arthritis, psoriasis, vascular inflammation, and transplantation. Cutting edge developments include the liposomal delivery of gene and RNA therapeutics and the use of hybrid systems where several liposomal bilayer features, or several drugs, are combined in a single formulation. The majority of the articles reviewed here focus on preclinical animal studies where proof-of-principle of an improved efficacy–safety ratio is observed when using liposomal formulations. A few clinical studies are included as well, which brings us to a discussion about the challenges of clinical translation of liposomal nanomedicines in the field of inflammatory diseases.
Phenotypic Characterization of Quinoa (Chenopodium quinoa Willd.) for the Selection of Promising Materials for Breeding Programs
Authors: Manjarres-Hernández Elsa Helena , Arias-Moreno Diana Marcela , Morillo-Coronado Ana Cruz , Ojeda-Pérez Zaida Zarely , Cárdenas-Chaparro Agobardo
Published: July 2021

Quinoa is an ancestral crop in the Andean region, characterized by its adaptability to different agroclimatic conditions, great nutritional value, and broad genetic variability. A preliminary approach for understanding the genetics of quinoa materials entails a morphologic characterization, which can provide the basis for the selection of materials that satisfy the needs of farmers and consumers. Therefore, this study aimed to evaluate the phenotypic characteristics of thirty genetic C. quinoa accessions for the selection of outstanding accessions in terms of yield and grain quality. A randomized complete block design was used, with nine replications for each accession under greenhouse conditions. Nine quantitative and twelve qualitative descriptors were evaluated with descriptive analysis, Spearman correlation variance, and multivariate and cluster analysis. The results showed that the accessions with heights greater than the average (>176.72 cm) and long panicles (>57.94 cm) presented lower yields and smaller seed sizes, thus decreasing the grain quality. The multivariate and cluster analyses established groups of accessions with good yields (>62.02 g of seeds per plant) and stable morphological characteristics. The proposed selection index, based on yield components and morphological descriptors, indicated four accessions as potential parents for quinoa breeding programs in Colombia.
Diversity and Relationships among Neglected Apricot (Prunus armeniaca L.) Landraces Using Morphological Traits and SSR Markers: Implications for Agro-Biodiversity Conservation
Authors: Corrado Giandomenico , Forlani Marcello , Rao Rosa , Basile Boris
Published: July 2021

Apricot (Prunus armeniaca L.) is an economically important tree species globally cultivated in temperate areas. Italy has an ample number of traditional varieties, but numerous landraces are abandoned and at risk of extinction because of increasing urbanization, agricultural intensification, and varietal renewal. In this work, we investigated the morphological and genetic diversity present in an ex-situ collection of 28 neglected varieties belonging to the so-called “Vesuvian apricot”. Our aim was to understand the level of diversity and the possible link between the promotion of specific fruit types (e.g., by public policies) and the intraspecific variation in apricot. The combination of five continuous and seven categorical traits allowed us to phenotypically distinguish the varieties; while fruit quality-related attributes displayed high variation, both apricot size and skin colour were more uniform. The twelve fluorescent-based Simple Sequence Repeats (SSRs) markers identified cultivar-specific molecular profiles and revealed a high molecular diversity, which poorly correlated with that described by the morphological analysis. Our results highlighted the complementary information provided by the two sets of descriptors and that DNA markers are necessary to separate morphologically related apricot landraces. The observed morphological and genetic differences suggest a loss of diversity influenced by maintenance breeding of specific pomological traits (e.g., skin colour and size). Finally, our study provided evidence to recommend complementary strategies to avoid the loss of diversity in apricot. Actions should pivot on both the promotion of easily identified premium products and more inclusive biodiversity-centred on-farm strategies.
The Different Faces of Arabidopsis arenosa—A Plant Species for a Special Purpose
Authors: Gieroń Żaneta , Sitko Krzysztof , Małkowski Eugeniusz
Published: July 2021

The following review article collects information on the plant species Arabidopsis arenosa. Thus far, A. arenosa has been known as a model species for autotetraploidy studies because, apart from diploid individuals, there are also tetraploid populations, which is a unique feature of this Arabidopsis species. In addition, A arenosa has often been reported in heavy metal-contaminated sites, where it occurs together with a closely related species A. halleri, a model plant hyperaccumulator of Cd and Zn. Recent studies have shown that several populations of A. arenosa also exhibit Cd and Zn hyperaccumulation. However, it is assumed that the mechanism of hyperaccumulation differs between these two Arabidopsis species. Nevertheless, this phenomenon is still not fully understood, and thorough research is needed. In this paper, we summarize the current state of knowledge regarding research on A. arenosa.
Fine Mapping of a Major Pleiotropic QTL Associated with Sesamin and Sesamolin Variation in Sesame (Sesamum indicum L.)
Authors: Xu Fangtao , Zhou Rong , Dossou Senouwa Segla Koffi , Song Shengnan , Wang Linhai
Published: July 2021

Deciphering the genetic basis of quantitative agronomic traits is a prerequisite for their improvement. Herein, we identified loci governing the main sesame lignans, sesamin and sesamolin variation in a recombinant inbred lines (RILs, F8) population under two environments. The content of the two lignans in the seeds was investigated by HPLC. The sesamin and sesamolin contents ranged from 0.33 to 7.52 mg/g and 0.36 to 2.70 mg/g, respectively. In total, we revealed 26 QTLs on a linkage map comprising 424 SSR markers, including 16 and 10 loci associated with sesamin and sesamolin variation, respectively. Among them, qSmin_11.1 and qSmol_11.1 detected in both the two environments explained 67.69% and 46.05% of the phenotypic variation of sesamin and sesamolin, respectively. Notably, qSmin11-1 and qSmol11-1 were located in the same interval of 127–127.21 cM on LG11 between markers ZMM1776 and ZM918 and acted as a pleiotropic locus. Furthermore, two potential candidate genes (SIN_1005755 and SIN_1005756) at the same locus were identified based on comparative transcriptome analysis. Our results suggest the existence of a single gene of large effect that controls expression, both of sesamin and sesamolin, and provide genetic information for further investigation of the regulation of lignan biosynthesis in sesame.
Induced Genetic Variations in Fruit Trees Using New Breeding Tools: Food Security and Climate Resilience
Authors: Sattar Muhammad Naeem , Iqbal Zafar , Al-Khayri Jameel M. , Jain S. Mohan
Published: July 2021

Fruit trees provide essential nutrients to humans by contributing to major agricultural outputs and economic growth globally. However, major constraints to sustainable agricultural productivity are the uncontrolled proliferation of the population, and biotic and abiotic stresses. Tree mutation breeding has been substantially improved using different physical and chemical mutagens. Nonetheless, tree plant breeding has certain crucial bottlenecks including a long life cycle, ploidy level, occurrence of sequence polymorphisms, nature of parthenocarpic fruit development and linkage. Genetic engineering of trees has focused on boosting quality traits such as productivity, wood quality, and resistance to biotic and abiotic stresses. Recent technological advances in genome editing provide a unique opportunity for the genetic improvement of woody plants. This review examines application of the CRISPR-Cas system to reduce disease susceptibility, alter plant architecture, enhance fruit quality, and improve yields. Examples are discussed of the contemporary CRISPR-Cas system to engineer easily scorable PDS genes, modify lignin, and to alter the flowering onset, fertility, tree architecture and certain biotic stresses.
In-Situ Synthesis of TiO2@GO Nanosheets for Polymers Degradation in a Natural Environment
Authors: Shi Yueqin , Yu Zhanyang , Li Zhengjun , Zhao Xiaodong , Yuan Yongjun
Published: July 2021

Plastic photodegradation naturally takes 300–500 years, and their chemical degradation typically needs additional energy or causes secondary pollution. The main components of global plastic are polymers. Hence, new technologies are urgently required for the effective decomposition of the polymers in natural environments, which lays the foundation for this study on future plastic degradation. This study synthesizes the in-situ growth of TiO2 at graphene oxide (GO) matrix to form the TiO2@GO photocatalyst, and studies its application in conjugated polymers’ photodegradation. The photodegradation process could be probed by UV-vis absorption originating from the conjugated backbone of polymers. We have found that the complete decomposition of various polymers in a natural environment by employing the photocatalyst TiO2@GO within 12 days. It is obvious that the TiO2@GO shows a higher photocatalyst activity than the TiO2, due to the higher crystallinity morphology and smaller size of TiO2, and the faster transmission of photogenerated electrons from TiO2 to GO. The stronger fluorescence (FL) intensity of TiO2@GO compared to TiO2 at the terephthalic acid aqueous solution indicates that more hydroxyl radicals (•OH) are produced for TiO2@GO. This further confirms that the GO could effectively decrease the generation of recombination centers, enhance the separation efficiency of photoinduced electrons and holes, and increase the photocatalytic activity of TiO2@GO. This work establishes the underlying basic mechanism of polymers photodegradation, which might open new avenues for simultaneously addressing the white pollution crisis in a natural environment.
A Comparison of Miniature Lattice Structures Produced by Material Extrusion and Vat Photopolymerization Additive Manufacturing
Authors: Guerra Silva Rafael , Torres María Josefina , Zahr Viñuela Jorge
Published: July 2021

In this paper, we study the capabilities of two additive manufacturing technologies for the production of lattice structures, namely material extrusion and vat photopolymerization additive manufacturing. A set of polymer lattice structures with diverse unit cell types were built using these additive manufacturing methods and tested under compression. Lattice structures built using material extrusion had lower accuracy and a lower relative density caused by the air gaps between layers, but had higher elastic moduli and larger energy absorption capacities, as a consequence of both the thicker struts and the relatively larger strength of the feedstock material. Additionally, the deformation process in lattices was analyzed using sequential photographs taken during the compression tests, evidencing larger differences according to the manufacturing process and unit-cell type. Both additive manufacturing methods produced miniature lattice structures with similar mechanical properties, but vat polymerization should be the preferred option when high geometrical accuracy is required. Nevertheless, as the solid material determines the compressive response of the lattice structure, the broader availability of feedstock materials gives an advantage to material extrusion in applications requiring stiffer structures or with higher energy absorption capabilities.
Enhancement of Flame Retardancy and Mechanical Properties of Polylactic Acid with a Biodegradable Fire-Retardant Filler System Based on Bamboo Charcoal
Authors: Li Wenzhu , Zhang Liang , Chai Weisheng , Yin Ningning , Semple Kate , Li Lu , Zhang Wenbiao , Dai Chunping
Published: July 2021

A cooperative flame-retardant system based on natural intumescent-grafted bamboo charcoal (BC) and chitosan (CS) was developed for polylactic acid (PLA) with improved flame retardancy and minimal decline in strength properties. Chitosan (CS) as an adhesion promoter improved the interfacial compatibility between graft-modified bamboo charcoal (BC-m) and PLA leading to enhanced tensile properties by 11.11% and 8.42%, respectively for tensile strength and modulus. At 3 wt.% CS and 30 wt.% BC-m, the crystallinity of the composite increased to 38.92%, or 43 times that of pure PLA (0.9%). CS promotes the reorganization of the internal crystal structure. Thermogravimetric analysis showed significantly improved material retention of PLA composites in nitrogen and air atmosphere. Residue rate for 5 wt.% CS and 30 wt.% BC-m was 29.42% which is 55.1% higher than the theoretical value of 18.97%. Flammability tests (limiting oxygen index-LOI and UL-94) indicated significantly improved flame retardancy and evidence of cooperation between CS and BC-m, with calculated cooperative effectiveness index(Ce) >1. From CONE tests, the peak heat release rate (pHRR) and total heat release (THR) were reduced by 26.9% and 30.5%, respectively, for 3% CS + 20% BC-m in PLA compared with adding 20% BC-m alone. Analysis of carbon residue morphology, chemical elements and structure suggest CS and BC-m form a more stable char containing pyrophosphate. This char provides heat insulation to inhibit complete polymer pyrolysis, resulting in improved flame retardancy of PLA composites. Optimal mix may be recommended at 20% BC-m + 3% CS to balance compatibility, composite strength properties and flame retardance.
Synthesis and Study of the Optical Properties of PMMA Microspheres and Opals
Authors: Matamoros-Ambrocio Mayra , Sánchez-Mora Enrique , Gómez-Barojas Estela , Luna-López José Alberto
Published: July 2021

Polymethylmethacrylate (PMMA) microspheres were synthesized by surfactant-free emulsion polymerization. These microspheres were used to obtain opals by the self-assembly method. Monomer and initiator quantities were varied systematically to monitor the size of PMMA microspheres. From SEM and DLS measurements, a trend was observed showing as the monomer and initiator amounts increased the average diameter of PMMA microspheres increased except when a minimum monomer amount was reached, for which the size of the microspheres remained practically constant. Diffuse reflectance spectra were processed by the Kubelka–Munk treatment to estimate the energy band gap (Eg) of the PMMA microspheres. It was found that PMMA microspheres present an indirect transition. From SEM micrographs, it is seen that PMMA opals photonic crystals are formed by microspheres in a uniform periodic face-centered cubic (fcc) array. Variable-angle specular reflectance spectra show that the opals possess a pseudo photonic band gap (PBG) in the visible and near-IR regions. Furthermore, it was found that PBGs shift towards larger wavelengths as the average diameter of the PMMA microspheres increases.
Evolution of Surface Morphology of Spin-Coated Poly(Methyl Methacrylate) Thin Films
Authors: Chapman Navid , Chapman Mingyu , Euler William B.
Published: July 2021

The morphology of sub-micron poly(methyl methacrylate) films coated to glass supports by spin coating from toluene is examined using surface profilometry. Wrinkled surfaces with local quasi-sinusoidal periodicity were seen on the surfaces of films with thicknesses of larger than 75 nm. The surface wrinkles had large aspect ratios with wavelengths in the tens of microns and amplitudes in the tens of nanometers. Wrinkles that formed during spin-coating are attributed to surface perturbations caused by Rayleigh–Bénard–Marangoni convective instabilities. The effects of film thickness, coating solution concentration, and drying rate on the thin film surface morphology are investigated. The results can be used to prepare surfaces with controlled morphology, either smooth or with periodic wrinkles.
Viscoelastic Effects on the Response of Electroelastic Materials
Authors: Diaz-Calleja Ricardo , Ginestar Damián , Compañ Moreno Vícente , Llovera-Segovia Pedro , Burgos-Simón Clara , Cortés Juan Carlos , Quijano Alfredo , Díaz-Boils Joaquín
Published: July 2021

Electroelastic materials, as for example, 3M VHB 4910, are attracting attention as actuators or generators in some developments and applications. This is due to their capacity of being deformed when submitted to an electric field. Some models of their actuation are available, but recently, viscoelastic models have been proposed to give an account of the dissipative behaviour of these materials. Their response to an external mechanical or electrical force field implies a relaxation process towards a new state of thermodynamic equilibrium, which can be described by a relaxation time. However, it is well known that viscoelastic and dielectric materials, as for example, polymers, exhibit a distribution of relaxation times instead of a single relaxation time. In the present approach, a continuous distribution of relaxation times is proposed via the introduction of fractional derivatives of the stress and strain, which gives a better account of the material behaviour. The application of fractional derivatives is described and a comparison with former results is made. Then, a double generalisation is carried out: the first one is referred to the viscoelastic or dielectric models and is addressed to obtain a nonsymmetric spectrum of relaxation times, and the second one is the adoption of the more realistic Mooney–Rivlin equation for the stress–strain relationship of the elastomeric material. A modified Mooney–Rivlin model for the free energy density of a hyperelastic material, VHB 4910 has been used based on experimental results of previous authors. This last proposal ensures the appearance of the bifurcation phenomena which is analysed for equibiaxial dead loads; time-dependent bifurcation phenomena are predicted by the extended Mooney–Rivlin equations.
Synergic Effect of Adsorption and Biodegradation by Microsphere Immobilizing Bacillus velezensis for Enhanced Removal Organics in Slaughter Wastewater
Authors: Deng Jing , Chen Qijue , Hu Boyong , Li Wen , Jia Mingxi , Shi Yi , Xiong Shouyao , Bai Jie , Yin Huaqun
Published: July 2021

Bacterial cell immobilization offers considerable advantages over traditional biotreatment methods using free bacteria. Bacillus velezensis was underwented isolation and genetic identification as COD-degrading bacteria in slaughter wastewaterand immobilized on the surface of polyvinyl alcohol (PVA) microsphere with the adhesion to bio-carrier through direct physical adsorption. The removal CODMn rates of microsphere (PVA) immobilized cells were 16.99%, increased 9.38% from a 50% concentration of slaughter wastewater within 24 h at 37 °C, pH 7.0, and 120 rpm, which was about 2.2 times that of the free bacteria. A significant difference was found in two groups (p < 0.01 p value less than 0.01 means statistical significance), and the COD degradation rate of the microsphere immobilized Bacillus velezensis strain was higher than the control group (PVA: control vs 20.08: 10.81), with the processing time reaching 36 h (p < 0.05). Additionally, similar results were obtained from a 20% concentration of slaughter wastewater within 24 h and 36 h. Moreover, the starch and protein digestibility of the immobilized Bacillus velezensis strain was higher than that of the free bacteria (20.1%: 42.2% vs. 17.5%: 37.2%). These findings revealed that the PVA-bacteria system was a simple, green, and inexpensive process, as well as a promising method. The research goal is aimed to synergize the effects of adsorption and biodegradation, as it can enhance organic removal by immobilized Bacillus velezensis in slaughter wastewater. Moreover, it may be possible that more potential materials can be used as biological carriers for the immobilization of bacterial cells later, which is beneficial for the recycling of resources.
A Polysomnographic and Cluster Analysis of Periodic Limb Movements in Sleep of Restless Legs Syndrome Patients with Psychiatric Conditions
Authors: Walters Arthur S. , Brunelin Jérôme , Catoire Sébastien , Suaud-Chagny Marie-Françoise , Spruyt Karen
Published: July 2021

Only survey studies have linked specific individual psychiatric disorders such as anxiety, depression and schizophrenia to Restless Legs Syndrome (RLS), Periodic Limb Movements in Sleep (PLMS) or both. We therefore aim to polysomnographically characterize sleep in a sample of physician-based, newly diagnosed cases of RLS with various ICD-10 psychiatric diagnoses. Retrospective analysis of data from a convenience sample of psychiatric patients (n = 43) per standard clinical sleep disorder cut-offs was conducted. Next, a cluster analysis was performed on the sleep data, taking into account the psychiatric diagnosis, comorbid non-psychiatric somatic problems and medication. We found that 37.2% of our sample showed clinically significant PLMS ≥ 15 and 76.5% exhibited an apnea hypopnea index (AHI) ≥ 5. Sleep structure was unaltered apart from the PLMS-related parameters. Two clusters were statistically identified: Cluster 1 primarily representing recurrent major depressive issues and Cluster 2 representing present but not predominant mood symptomatology as well as mixed disorders with personality problems. The known confounders were controlled. A PLMS index ≥ 15 was differentially distributed among the two clusters with Cluster 1: 10 out of 17 with PLMS index ≥ 15; Cluster 2: 1 out of 16 with PLMS index ≥15; whilst AHI was not different. Patients in Cluster 1 have a higher rate of periodic leg movements than patients in Cluster 2. This suggests that the high association with PLMS is primarily driven by affective disorders. Our findings warrant questioning of RLS symptomatology in patients with psychiatric conditions.
The Lulav: Early Modern Polemical Ethnographies and the Art of Fencing
Authors: Goren Ahuvia
Published: July 2021

In recent years, scholars have devoted a great deal of attention to the history of scholarship in general and, more specifically, to the emergence of critical historical and anthropological literature from and within ecclesiastical scholarship. However, few studies have discussed the Jewish figures who took part in this process. This paper analyzes the role played by historiographical and ethnographical writing in seventeenth- and eighteenth-century Italian Jewish–Christian polemics. Tracing various Christian polemical ethnographical depictions of the Jewish rite of shaking the lulav (sacramental palm leaves used by Jews during the festival of Sukkot), it discusses the variety of ways in which Jewish scholars responded to these depictions or circumvented them. These responses reflect the Jewish scholars’ familiarity with prevailing contemporary scholarship and the key role of translation and cultural transfers in their own attempts to create parallel works. Furthermore, this paper presents new Jewish polemical manuscript material within the relevant contexts, examines Jewish attempts to compose polemical and apologetic ethnographies, and argues that Jewish engagement with critical scholarship began earlier than scholars of this period usually suggest
Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy
Authors: Elaamery Badr , Pesavento Massimo , Aldovini Teresa , Lissandrini Nicola , Michieletto Giulia , Cenedese Angelo
Published: July 2021

The transportation of large payloads can be made possible with Multi-Robot Systems (MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the acting robots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.
Use of A MODIS Satellite-Based Aridity Index to Monitor Drought Conditions in Mongolia from 2001 to 2013
Authors: Kimura Reiji , Moriyama Masao
Published: July 2021

The 4D disasters (desertification, drought, dust, and dzud, a Mongolian term for severe winter weather) have recently been increasing in Mongolia, and their impacts on the livelihoods of humans has likewise increased. The combination of drought and dzud has caused the loss of livestock on which nomadic herdsmen depend for their well-being. Understanding the spatiotemporal patterns of drought and predicting drought conditions are important goals of scientific research in Mongolia. This study involved examining the trends of the normalized difference vegetation index (NDVI) and satellite-based aridity index (SbAI) to determine why the land surface of Mongolia has recently (2001–2013) become drier across a range of aridity indices (AIs). The main reasons were that the maximum NDVI (NDVImax) was lower than the NDVImax typically found in other arid regions of the world, and the SbAI throughout the year was large (dry), although the SbAI in summer was comparatively small (wet). Under the current conditions, the capacity of the land surface to retain water throughout the year caused a large SbAI because rainfall in Mongolia is concentrated in the summer, and the conditions of grasslands reflect summer rainfall in addition to grazing pressure. We then proposed a method to monitor the land-surface dryness or drought using only satellite data. The correct identification of drought was higher for the SbAI. Drought is more strongly correlated with soil moisture anomalies, and thus the annual averaged SbAI might be appropriate for monitoring drought during seasons. Degraded land area, defined as annual NDVImax < 0.2 and annual averaged SbAI > 0.025, has decreased. Degraded land area was large in the major drought years of Mongolia.
Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery
Authors: Martini Mauro , Mazzia Vittorio , Khaliq Aleem , Chiaberge Marcello
Published: July 2021

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.
Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine
Authors: Ghorbanian Arsalan , Zaghian Soheil , Asiyabi Reza Mohammadi , Amani Meisam , Mohammadzadeh Ali , Jamali Sadegh
Published: July 2021

Mangroves are among the most productive ecosystems in existence, with many ecological benefits. Therefore, generating accurate thematic maps from mangrove ecosystems is crucial for protecting, conserving, and reforestation planning for these valuable natural resources. In this paper, Sentinel-1 and Sentinel-2 satellite images were used in synergy to produce a detailed mangrove ecosystem map of the Hara protected area, Qeshm, Iran, at 10 m spatial resolution within the Google Earth Engine (GEE) cloud computing platform. In this regard, 86 Sentinel-1 and 41 Sentinel-2 data, acquired in 2019, were employed to generate seasonal optical and synthetic aperture radar (SAR) features. Afterward, seasonal features were inserted into a pixel-based random forest (RF) classifier, resulting in an accurate mangrove ecosystem map with average overall accuracy (OA) and Kappa coefficient (KC) of 93.23% and 0.92, respectively, wherein all classes (except aerial roots) achieved high producer and user accuracies of over 90%. Furthermore, comprehensive quantitative and qualitative assessments were performed to investigate the robustness of the proposed approach, and the accurate and stable results achieved through cross-validation and consistency checks confirmed its robustness and applicability. It was revealed that seasonal features and the integration of multi-source remote sensing data contributed towards obtaining a more reliable mangrove ecosystem map. The proposed approach relies on a straightforward yet effective workflow for mangrove ecosystem mapping, with a high rate of automation that can be easily implemented for frequent and precise mapping in other parts of the world. Overall, the proposed workflow can further improve the conservation and sustainable management of these valuable natural resources.
Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint
Authors: Xie Hao , Chen Yushi , Ghamisi Pedram
Published: July 2021

In recent years, many convolutional neural network (CNN)-based methods have been proposed to address the scene classification tasks of remote sensing images. Since the number of training samples in RS datasets is generally small, data augmentation is often used to expand the training set. It is, however, not appropriate when original data augmentation methods keep the label and change the content of the image at the same time. In this study, label augmentation (LA) is presented to fully utilize the training set by assigning a joint label to each generated image, which considers the label and data augmentation at the same time. Moreover, the output of images obtained by different data augmentation is aggregated in the test process. However, the augmented samples increase the intra-class diversity of the training set, which is a challenge to complete the following classification process. To address the above issue and further improve classification accuracy, Kullback–Leibler divergence (KL) is used to constrain the output distribution of two training samples with the same scene category to generate a consistent output distribution. Extensive experiments were conducted on widely-used UCM, AID and NWPU datasets. The proposed method can surpass the other state-of-the-art methods in terms of classification accuracy. For example, on the challenging NWPU dataset, competitive overall accuracy (i.e., 91.05%) is obtained with a 10% training ratio.
Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations
Authors: Qi Wenyan , Chen Jie , Xu Chongyu , Wan Yongjing
Published: July 2021

Global gridded precipitations have been extensively considered as the input of hydrological models for runoff simulations around the world. However, the limitations of hydrologic models and the inaccuracies of the precipitation datasets could result in large uncertainty in hydrological forecasts and water resource estimations. Therefore, it is of great importance to investigate the hydrological value of a weighted combination of hydrological models driven by different precipitation datasets. In addition, due to the diversities of combination members and climate conditions, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the weighted combinations. This study undertakes a comprehensive analysis of various multimodel averaging methods and schemes (i.e., the combination of the members in averaging) to identify the most skillful and reliable multimodel averaging application. To achieve this, four hydrological models driven by six precipitation datasets were used as averaging members. The behaviors of 9 averaging methods and 11 averaging schemes in hydrological simulations were tested over 2277 watersheds distributed in different climate regions in the world. The results show the following: (1) The multi-input averaging schemes (i.e., members consist of one model driven by multiple precipitation datasets) generally perform better than the multimodel averaging schemes (i.e., members consist of multiple models driven by the same precipitation dataset) for each averaging method; (2) The use of multiple members can improve the averaging performances. Six averaging members are found to be necessary and advisable, since using more than six members only imrpoves the estimation results slightly, as compared with using all 24 members; (3) The advantage of using averaging methods for hydrological modeling is region dependent. The averaging methods, in general, produced the best results in the warm temperate region, followed by the snow and equatorial regions, while a large difference among various averaging methods is found in arid and arctic regions. This is mainly due to the different averaging methods being affected to a different extent by the poorly performed members in the arid and arctic regions; (4) the multimodel superensemble method (MMSE) is recommended for its robust and outstanding performance among various climatic regions.
A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images
Authors: Wu Zitong , Hou Biao , Ren Bo , Ren Zhongle , Wang Shuang , Jiao Licheng
Published: July 2021

Ship detection is a challenging task for synthetic aperture radar (SAR) images. Ships have arbitrary directionality and multiple scales in SAR images. Furthermore, there is a lot of clutter near the ships. Traditional detection algorithms are not robust to these situations and easily cause redundancy in the detection area. With the continuous improvement in resolution, the traditional algorithms cannot achieve high-precision ship detection in SAR images. An increasing number of deep learning algorithms have been applied to SAR ship detection. In this study, a new ship detection network, known as the instance segmentation assisted ship detection network (ISASDNet), is presented. ISASDNet is a two-stage detection network with two branches. A branch is called an object branch and can extract object-level information to obtain positioning bounding boxes and classification results. Another branch called the pixel branch can be utilized for instance segmentation. In the pixel branch, the designed global relational inference layer maps the features to interaction space to learn the relationship between ship and background. The global reasoning module (GRM) based on global relational inference layers can better extract the instance segmentation results of ships. A mask assisted ship detection module (MASDM) is behind the two branches. The MASDM can improve detection results by interacting with the outputs of the two branches. In addition, a strategy is designed to extract the mask of SAR ships, which enables ISASDNet to perform object detection training and instance segmentation training at the same time. Experiments carried out two different datasets demonstrated the superiority of ISASDNet over other networks.
Statistical Modelling of SPADs for Time-of-Flight LiDAR
Authors: Incoronato Alfonso , Locatelli Mauro , Zappa Franco
Published: July 2021

Time-of-Flight (TOF) based Light Detection and Ranging (LiDAR) is a widespread technique for distance measurements in both single-spot depth ranging and 3D mapping. Single Photon Avalanche Diode (SPAD) detectors provide single-photon sensitivity and allow in-pixel integration of a Time-to-Digital Converter (TDC) to measure the TOF of single-photons. From the repetitive acquisition of photons returning from multiple laser shots, it is possible to accumulate a TOF histogram, so as to identify the laser pulse return from unwelcome ambient light and compute the desired distance information. In order to properly predict the TOF histogram distribution and design each component of the LiDAR system, from SPAD to TDC and histogram processing, we present a detailed statistical modelling of the acquisition chain and we show the perfect matching with Monte Carlo simulations in very different operating conditions and very high background levels. We take into consideration SPAD non-idealities such as hold-off time, afterpulsing, and crosstalk, and we show the heavy pile-up distortion in case of high background. Moreover, we also model non-idealities of timing electronics chain, namely, TDC dead-time, limited number of storage cells for TOF data, and TDC sharing. Eventually, we show how the exploit the modelling to reversely extract the original LiDAR return signal from the distorted measured TOF data in different operating conditions.
NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks
Authors: Contreras Rodrigo Colnago , Parnandi Avinash , Coelho Bruno Gomes , Silva Claudio , Schambra Heidi , Nonato Luis Gustavo
Published: July 2021

A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.
Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)
Authors: Matin Sahar S. , Pradhan Biswajeet
Published: July 2021

Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)—a machine learning model—and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model’s decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.
Internal Consistency of Sway Measures via Embedded Head-Mounted Accelerometers: Implications for Neuromotor Investigations
Authors: Lapointe Andrew P. , Ritchie Jessica N. , Vitali Rachel V. , Burma Joel S. , Soroush Ateyeh , Oni Ibukunoluwa , Dunn Jeff F.
Published: July 2021

Accelerometers are being increasingly incorporated into neuroimaging devices to enable real-time filtering of movement artifacts. In this study, we evaluate the reliability of sway metrics derived from these accelerometers in a standard eyes-open balance assessment to determine their utility in multimodal study designs. Ten participants equipped with a head-mounted accelerometer performed an eyes-open standing condition on 7 consecutive days. Sway performance was quantified with 4 standard metrics: root-mean-square (RMS) acceleration, peak-to-peak (P2P) acceleration, jerk, and ellipse area. Intraclass correlation coefficients (ICC) quantified reliability. P2P in both the mediolateral (ICC = 0.65) and anteroposterior (ICC = 0.67) planes yielded the poorest reliability. Both ellipse area and RMS exhibited good reliability, ranging from 0.76 to 0.84 depending on the plane. Finally, jerk displayed the highest reliability with an ICC value of 0.95. Moderate to excellent reliability was observed in all sway metrics. These findings demonstrate that head-mounted accelerometers, commonly found in neuroimaging devices, can be used to reliably assess sway. These data validate the use of head-mounted accelerometers in the assessment of motor control alongside other measures of brain activity such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS).