bluuebunny/crossref_metadata_2025 huggingface dataset:
Spotlight on Calcipotriol/Betamethasone Fixed-Dose Combination in Topical Formulations: Is There Still Room for Innovation?
Authors: Selmin Francesca , Franzè Silvia , Casiraghi Antonella , Cilurzo Francesco
Published: September 2022

Psoriasis is a lifelong disease which requires treatment adherence for successful management. Considering the complexity of this pathology, the combination of active pharmaceutical ingredients with a synergistic mechanism of action can improve the safety and efficacy of the treatment with respect to the conventional monotherapy. Moreover, a fixed dose of therapeutic agents in a topical formulation offers the possibility to simplify administration, reduce the doses of each active ingredient, and improve patient’s compliance. Among the first-line treatments in mild to moderate psoriasis, the formulation of calcipotriol (Cal) and betamethasone dipropionate (BD) in a single vehicle is challenging due to their chemical incompatibility in an aqueous environment and the formation of degradation products. Based on these considerations, this review aims to provide an overview on the biopharmaceutical properties of Cal/BD fixed-dose combination products available on the market (namely ointment, oleogel, foam, and O/W cream), highlighting also the novel approaches under evaluation. The main differences among topical formulations are discussed considering the different features of the anatomic districts involved in psoriasis and the patient’s adherence. Moreover, since in vitro experiments are fundamental to evaluate the skin permeation profile during the development of an efficacious medicinal product, special emphasis is given to models proposed to mimic psoriatic lesions.
Development of a Combined Lipid-Based Nanoparticle Formulation for Enhanced siRNA Delivery to Vascular Endothelial Cells
Authors: He Yutong , Bi Dongdong , Plantinga Josée A. , Molema Grietje , Bussmann Jeroen , Kamps Jan A. A. M.
Published: September 2022

Low transfection efficiency in endothelial cells (EC) is still a bottleneck for the majority of siRNA-based vascular delivery approaches. In this work, we developed a lipid-based nanoparticle (LNP) formulation based on a combination of a permanently charged cationic lipid-DOTAP and a conditionally ionized cationic lipid-MC3 (DOTAP/MC3) for the enhanced delivery of siRNA into EC. Compared with a single DOTAP or MC3-based benchmark LNP, we demonstrated that the DOTAP/MC3 LNP formulation shows the best transfection efficiency both in primary EC in vitro and in endothelium in zebrafish. The high transfection activity of the DOTAP/MC3 LNP formulation is achieved by a combination of improved endothelial association mediated by DOTAP and MC3-triggered efficient siRNA intracellular release in EC. Furthermore, AbVCAM-1-coupled DOTAP/MC3 LNP-mediated siRNARelA transfection showed pronounced anti-inflammatory effects in inflammatory-activated primary EC by effectively blocking the NF-κB pathway. In conclusion, the combination of permanent and ionizable cationic lipids in LNP formulation provides an effective endothelial cell delivery of siRNA.
Resina Draconis Particles Encapsulated in a Hyaluronic-Acid-Based Hydrogel to Treat Complex Burn Wounds
Authors: Xu Lijun , Zhou Ziqiang , Chen Yuying , Lu Huangjie , Hu Ping
Published: September 2022

Severe burns require urgent new dressing treatments due to their irregular wounds and secondary injuries associated with dressing changes. In this study, a hyaluronic-acid-based hydrogel was developed to treat complex burn wounds. This hydrogel was prepared by mixing and cross-linking oxidized hyaluronic acid (OHA) and carboxymethyl chitosan (CMCS) through Schiff base reactions. Micronized Resina Draconis particles were encapsulated in this hydrogel to achieve sustained release of the active components when applied on wounds. The Resina-Draconis-loaded hydrogel (RD-Gel) demonstrated good mechanical properties and excellent self-healing. The results of in vitro experiments confirmed that RD-Gel had good biocompatibility, and was able to enhance cell migration and inhibit the production of inflammatory cytokines. It also induced rapid hemostasis in rats, downregulated the levels of inflammatory cytokines, and promoted collagen regeneration on model animals, eventually accelerating the rebuilding of skin structures and wound recovery.
Essential Oil and Hydrosol Composition of Immortelle (Helichrysumitalicum)
Authors: Kunc Nina , Frlan Antonela , Baričevič Dea , Kočevar Glavač Nina , Kokalj Ladan Meta
Published: September 2022

The chemical composition of essential oils and hydrosols of immortelle (Helichrysum italicum) stems with leaves obtained by hydrodistillation was identified using gas chromatography coupled with mass spectrometry (GC-MS). One-year-old and two-year-old plants of the same immortelle population, and plants from three Croatian populations, all grown in Slovenia, were included in the study. The main compounds of essential oils of one-year-old and two-year-old plants were α-pinene, α-eudesmol, and rosifoliol. Among essential oils of the Croatian populations, three dominant components were found: α-pinene, geranyl acetate, and 2-phenylethyl tiglate. Both the essential oils and hydrosols of one-year-old plants were more diverse in their compositions than two-year-old plants. The predominant compounds of hydrosols of one- and two-year-old plants were pentan-3-one, 3-octanone, 2,2-dimethylnon-5-en-3-one, and α-terpineol, and in the Croatian populations α-terpineol, 2,6-octadien-1-ol, 2,2-dimethylnon-5-en-3-one, and α-terpineol.
Effects of UV-B Radiation on the Performance, Antioxidant Response and Protective Compounds of Hazelnut Pollen
Authors: Çetinbaş-Genç Aslıhan , Toksöz Orçun , Piccini Chiara , Kilin Özkan , Sesal Nüzhet Cenk , Cai Giampiero
Published: September 2022

Increasing ultraviolet (UV) radiation is expected to become a problem in hazelnut cultivation. The aim of this study is to examine the effects of UV-B on hazelnut pollen. To this end, the pollens were exposed to UV-B for 1, 2, and 3 h at distances of 10, 20, 30, and 40 cm. Groups treated for 2 h at 20 cm and 3 h at 10 and 20 cm were identified as the most affected based on the results of viability, germination, and tube elongation. Further studies on these groups showed that UV-B does not change the DPPH radical scavenging activity for all groups. However, total phenolic compounds decreased after 3 h of treatment at 10 and 20 cm, while total flavonoid compounds decreased after all treatment groups. The UV-B absorbance of cytoplasmic and cell-wall-bound fractions decreased for all groups. The UV-B absorbance of the sporopollenin-derived fraction increased after 2 h of treatment at 20 cm but decreases after treatment for 3 h at 10 and 20 cm. In summary, exposure to UV-B for different times and distances adversely affected pollen grains in terms of pollen viability, germination rate, tube length, and the level of antioxidant molecules and UV-absorbing compounds.
Density Gradients, Cellular Structure and Thermal Conductivity of High-Density Polyethylene Foams by Different Amounts of Chemical Blowing Agent
Authors: Lobos Juan , Thirumuruganandham Saravana Prakash , Rodríguez-Pérez Miguel Angel
Published: September 2022

LDPE (low-density polyethylene) foams were prepared using the improved compression moulding technique (ICM) with relative densities ranging from 0.3 to 0.7 and with different levels of chemical blowing agents (from 1% to 20%). The density gradients, cellular structure and thermal conductivity of the foams were characterized. The density and amount of CBA used were found to have a significant effect on the cellular structure both at the mesoscale (density gradients) and at the microscale (different cell sizes and cell densities). In addition, the thermal conductivity of the samples is very sensitive to the local structure where the heat flux is located. The technique used to measure this property, the Transient Plane Source method (TPS), makes it possible to detect the presence of density gradients. A simple method for determining these gradients based on thermal conductivity data was developed.
Development of a New Type of Flame Retarded Biocomposite Reinforced with a Biocarbon/Basalt Fiber System: A Comparative Study between Poly(lactic Acid) and Polypropylene
Authors: Andrzejewski Jacek , Michałowski Sławomir
Published: September 2022

A new type of partially biobased reinforcing filler system was developed in order to be used as a flame retardant for polylactic acid (PLA) and polypropylene (PP)-based composites. The prepared materials intended for injection technique processing were melt blended using the novel system containing ammonium polyphosphate (EX), biocarbon (BC), and basalt fibers (BF). All of the prepared samples were subjected to a detailed analysis. The main criterion was the flammability of composites. For PLA-based composites, the flammability was significantly reduced, up to V-0 class. The properties of PLA/EX/BC and PLA/EX/(BC-BF) composites were characterized by their improved mechanical properties. The conducted analysis indicates that the key factor supporting the effectiveness of EX flame retardants is the addition of BC, while the use of BF alone increases the flammability of the samples to the reference level. The results indicate that the developed materials can be easily applied in industrial practice as effective and sustainable flame retardants.
Study of the Microscopic Mechanism of Natural Rubber (Cis-1, 4-Polyisoprene, NR)/Polyethylene (PE) Modified Asphalt from the Perspective of Simulation
Authors: Chen Yujing , Hu Kui , Yu Caihua , Yuan Dongdong , Ban Xiaoyi
Published: September 2022

This paper aims to study the interaction mechanism of waste tire/plastic modified asphalt from the microscopic perspective of molecules. Based on BIOVIA Materials Studio, a classic four-component asphalt model consisting of asphaltene (C149H177N3O2S2), resin (C59H85NOS), aromatic (C46H50S), and saturate (C22H46) was constructed. Waste tires are represented by natural rubber (NR), which uses cis-1, 4-polyisoprene as a repeating unit. In contrast, waste plastics are characterized by polyethylene (PE), whose optimum degree of polymerization is determined by the difference in solubility parameters. Then, the above molecular models are changed to a stable equilibrium state through the molecular dynamics process. Finally, the interaction process is analyzed and inferred using the indexes of radial distribution function, diffusion coefficient, and concentration distribution; further, the interaction mechanism is revealed. The results show that the optimal degree of polymerization of PE is 12, so the solubility parameter between PE and NR-modified asphalt is the lowest at 0.14 (J/cm3) 1/2. These models are in agreement with the characteristics of amorphous materials with the structures ordered in the short-range and long-range disordered. For NR-modified asphalt, the saturate moves fastest, and its diffusion coefficient reaches 0.0201, followed by that of the aromatic (0.0039). However, the molecule of NR ranks the slowest in the NR-modified asphalt. After the addition of PE, the diffusion coefficient of resin increased most significantly from 0.0020 to 0.0127. NR, PE, and asphaltene have a particular attraction with the lightweight components, thus changing to a more stable spatial structure. Therefore, using NR and PE-modified asphalt can change the interaction between asphalt molecules to form a more stable system. This method not only reduces the large waste disposal task but also provides a reference for the application of polymer materials in modified asphalt.
A Review of Composite Phase Change Materials Based on Biomass Materials
Authors: Zhang Qiang , Liu Jing , Zhang Jian , Lin Lin , Shi Junyou
Published: September 2022

Phase change materials (PCMs) can store/release heat from/to the external environment through their own phase change, which can reduce the imbalance between energy supply and demand and improve the effective utilization of energy. Biomass materials are abundant in reserves, from a wide range of sources, and most of them have a natural pore structure, which is a good carrier of phase change materials. Biomass-based composite phase change materials and their derived ones are superior to traditional phase change materials due to their ability to overcome the leakage of phase change materials during solid–liquid change. This paper reviews the basic properties, phase change characteristics, and binding methods of several phase change materials (polyethylene glycols, paraffins, and fatty acids) that are commonly compounded with biomass materials. On this basis, it summarizes the preparation methods of biomass-based composite phase change materials, including porous adsorption, microencapsulation based on biomass shell, and grafting by copolymerization and also analyzes the characteristics of each method. Finally, the paper introduces the latest research progress of multifunctional biomass-based composite phase change materials capable of energy storage and outlines the challenges and future research and development priorities in this field.
Study on Flexural Behaviour of Ferrocement Composites Reinforced with Polypropylene Warp Knitted Fabric
Authors: Rameshkumar Manickam , Malathy Ramalingam , Chandiran Priyalatha , Paramasivam Sundararajan , Chung Ill-Min , Kim Seung-Hyun , Prabakaran Mayakrishnan
Published: September 2022

Ferrocement is a cost-effective construction material used in the low-cost constructions. It is produced with the combination of cement mortar with closely spaced wire mesh known as chicken wire mesh. Ferrocement process eliminates coarse aggregates when compared to reinforced concrete thus makes the process simple. This paper deals with the influence of various characteristics of warp knitted fabric on the flexural properties of ferrocement composites. Ferrocement composites have a wide range of applications in the construction industry and it has some limitations due to the durability issues. Among the various durability issues, corrosion is one of the main issues to be addressed to enhance the long-term service life of the ferrocement composites. The idea of using non-metallic mesh to eliminate the corrosion problem is discussed in this paper. In this experiment, warp knitted fabric reinforced ferrocement composites were produced using polypropylene warp knitted fabrics. This paper deals with the flexural properties of ferrocement composites made of warp knitted fabric coated with expoxy. This paper deals with the flexural properties of ferrocement composites made of warp knitted fabric coated with expoxy. These composites were analyzed for their flexural strength, energy absorption and ductile property. The variables in the experiment are filament thickness, warp knitted structure and number of layers in the composites. Experimental results proved that the replacement of chicken mesh wire by warp knitted fabrics has an impact in the flexural properties of the composites and the effect of variables in the experiment set up has been analyzed. There is an imporvement of 200% is observed in the first crack load and 120% improvement in the ultimate load of the warp knit fabric reinforced composite compared to control sample. Experimental results proved that there is an increase in flexural strength of ferrocement composites made up with warp knitted fabrics. Microstructure studies like SEM and EDX on ferrocement laminates confirmed good bonding between the mortar mix and warp knitted fabrics.
Sexuality beyond Chastity: Negotiating Gender Intimacy and Sexuality within Roman Catholic Religious Communities in Poland
Authors: Jewdokimow Marcin , Sadlon Wojciech
Published: September 2022

In late modernity both religion and sexuality are being elaborated in terms of reflexivity. In this article, we present findings from our research on the topic of constructions of gender, intimacy and sexuality by sisters and brothers in Catholic monasteries in Poland. The findings are based on the mixed-method transformative connection between qualitative (n = 92) and representative sample quantitative research (n = 1543) conducted in 2020. We studied reflexivity on gender, intimacy and sexuality within Catholic religious communities in Poland in order to understand how gender, intimacy and sexuality are presented in the institutionalized framework of religious life. Our study demonstrates that reflexivity on gender, intimacy and sexuality is highly institutionalised and deeply privatized within Catholic religious communities. The article shows that reflexivity of consecrated persons in Poland on gender, intimacy and sexuality is strongly shaped by religious norms (chastity) and subordinated to their religious roles.
Sport as a Meaning-Making System: Insights from the Study of Religion
Authors: Sosis Richard , Kiper Jordan
Published: September 2022

Meaning-making has been one of the primary domains of religion throughout history, and some have claimed that this is religion’s central function. Yet, the modern era has seen a proliferation of other social institutions that generate meaning for people. Here we reflect on what religious meaning-making can tell us about meaning-making in secular institutions, with a particular focus on sport. Sport as a meaning-making institution is puzzling since sports are generally considered leisure activities, not serious enough to provide meaningful structure and purpose to human lives. Nonetheless, people do derive meaning from sport and we argue that because sport shares many features with religion, it offers a unique opportunity to examine a secular meaning-making institution. We offer a theoretical framework for the study of meaning-making that derives from our conceptual approach to religion as an adaptive system. We use this approach, and other anthropological research, to delineate seven general characteristics of human meaning-making systems: collective, constructed, subjective, narrative, relational, transcendent, and growth-oriented. These features of meaning-making systems highlight why sport has been so successful at offering meaning to sport enthusiasts, both fans and athletes alike. We conclude with a brief speculative evolutionary scenario that may explain our proclivity for seeking meaning, and why secular institutions will continue to fill that role when religious worldviews are not compelling.
Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound
Authors: Khobotov Alexander Gennadievich , Kalinina Vera Igorevna , Khil’ko Alexander Ivanovich , Malekhanov Alexander Igorevich
Published: September 2022

The well-known method of detecting a useful signal in the presence of noise during underwater remote sensing, based on the matched filtering of the received signal with the test signal, provides the maximum signal-to-noise ratio (SNR) at the receiver output. To do this, a correlation-type criterion function (CF) is constructed for the received and test signals. In the case of large volumes of processed data, this method requires the use of large computing resources. The search for a data processing method with lower computational costs, as well as the effective application of artificial neural networks to array signal processing, motivates the authors to propose an alternative approach to the CF construction based on the McCulloch–Pitts neuron model. Such a neuron-like CF is based on a specific nonlinear transformation of the input and test signals and uses only logical operations, which require much less computational resources. The ratio of the output signal amplitude to the input noise level is indeed the maximum with matched filtering. Studies have shown that it is not this parameter that should be considered, but statistical characteristics, on the basis of which the thresholds for detecting a signal in the presence of noise are determined. Such characteristics include the probability density distributions of correlation and neuron-like CFs in the presence and absence of noise. In this case, the signal detection thresholds will be lower for the neuron-like CF than for the conventional correlation CF. The aim of this research is to increase the accuracy of the selection of a useful signal against the intense noise background when using a processor based on the neuron-like CF and to determine the conditions when the input SNR, at which signal detection is possible, is lower compared to the correlation CF. The comparative results of stochastic modeling show the effectiveness of using a new neuron-like approach to reduce the detection threshold when a chirp signal is received against a background of unsteady Gaussian noise. The advantages of the neuron-like method become significant when the statistical distribution of the additive noise does not change, but its variance increases or decreases. In order to confirm the presence of non-stationarity in real noises, experimental data obtained from the remote sounding of bottom sediments in the Black Sea are presented. The results obtained are considered to be applicable in a wide range of practical situations related to remote sensing in non-stationary environments, long-range sonar and sea bottom exploration.
A Simple Band Ratio Library (BRL) Algorithm for Retrieval of Hourly Aerosol Optical Depth Using FY-4A AGRI Geostationary Satellite Data
Authors: Jiang Xingxing , Xue Yong , Jin Chunlin , Bai Rui , Sun Yuxin , Wu Shuhui
Published: September 2022

The Advanced Geostationary Radiation Imager (AGRI) is one of the primary payloads aboard the FY-4A geostationary meteorological satellite, which can provide high-frequency, wide coverage, and multiple spectral channel observations for China and surrounding areas. There are currently few studies on aerosol optical depth (AOD) inversion from FY-4A AGRI data. Based on AGRI data, a new land AOD retrieval algorithm called the band ratio library (BRL) algorithm was proposed in this study. The monthly average surface reflectance band ratio library was established after obtaining the relationship of band surface reflectance ratio from the MODIS combined AOD dataset. In order to calculate the hourly AOD, look-up tables (LUT) for the various aerosol models were constructed using the 6SV model. We quantitatively compared AOD produced from AGRI data with AERONET ground observations to validate the BRL algorithm. AGRI-retrieved AOD is in good agreement with AOD measured by AERONET, which has a correlation coefficient of R is 0.84, the linear regression function is AODAGRI = 0.80 ∗ AODAERONET − 0.004, the root-mean-square error (RMSE) is 0.16, and approximately 60% of the AGRI AOD results fall within the uncertain range of AOD = ±(0.2 × AODAERONET + 0.05). A cross-comparison was made with the MODIS AOD product provided by NASA. The comparison and verification show the proposed algorithm has a good accuracy of land AOD estimation from AGRI data.
Evaluating the Effects of Climate Change and Human Activities on the Seasonal Trends and Spatial Heterogeneity of Soil Moisture
Authors: Zhang Ermei , Liu Yujie , Pan Tao , Tan Qinghua , Ma Zhiang
Published: September 2022

Soil moisture (SM), as a crucial variable in the soil–vegetation–atmosphere continuum, plays an important role in the terrestrial water cycle. Analyzing SM’s variation and driver factors is crucial to maintaining ecosystem diversity on the Tibetan Plateau (TP) and ensuring food security as well as water supply balance in developing countries. Gradual wetting of the soil has been detected and attributed to precipitation in this area. However, there is still a gap in understanding the potential mechanisms. It is unclear whether the greening, glacier melting, and different vegetation degradation caused by asymmetrical climate change and intensified human activities have significantly affected the balance of SM. Here, to test the hypothesis that heterogeneous SM caused by precipitation was subject to temperatures and anthropogenic constraints, GLDAS-2.1 (Global Land Data Assimilation System-2.1) SM products combined with the statistical downscaling and Geographic detectors were applied. The results revealed that: (1) Seasonal SM gradually increased (p < 0.05), while SM deficit frequently appeared with exposure to extreme climates, such as in the summer of 2010 and 2013, and changed into a pattern of precipitation transport to western dry lands in autumn. (2) There was a synergistic reaction between greening and local moisture in autumn. SM was dominated by low temperature (TMN) in winter, warming indirectly regulated SM by exacerbating the thawing of glaciers and permafrost. The spatial coupling between the faster rising rate of TMN and the frozen soil might further aggravate the imbalance of SM. (3) The land cover’s mutual transformation principally affected SM in spring and autumn, and degradation accelerated the loss of SM replenished by precipitation. (4) Land cover responses were different; SM in grassland was less affected by external disturbance, while degraded woodland and shrub performed adaptive feedback under dry environments, SM increased by 0.05 and 0.04 m3/(m3 10a), respectively. Our research provides a scientific basis for improving hydrological models and developing vegetation restoration strategies for long-term adaptation to TP-changing environments.
Hyperspectral Image Classification with IFormer Network Feature Extraction
Authors: Ren Qi , Tu Bing , Liao Sha , Chen Siyuan
Published: September 2022

Convolutional neural networks (CNNs) are widely used for hyperspectral image (HSI) classification due to their better ability to model the local details of HSI. However, CNNs tends to ignore the global information of HSI, and thus lack the ability to establish remote dependencies, which leads to computational cost consumption and remains challenging. To address this problem, we propose an end-to-end Inception Transformer network (IFormer) that can efficiently generate rich feature maps from HSI data and extract high- and low-frequency information from the feature maps. First, spectral features are extracted using batch normalization (BN) and 1D-CNN, while the Ghost Module generates more feature maps via low-cost operations to fully exploit the intrinsic information in HSI features, thus improving the computational speed. Second, the feature maps are transferred to Inception Transformer through a channel splitting mechanism, which effectively learns the combined features of high- and low-frequency information in the feature maps and allows for the flexible modeling of discriminative information scattered in different frequency ranges. Finally, the HSI features are classified via pooling and linear layers. The IFormer algorithm is compared with other mainstream algorithms in experiments on four publicly available hyperspectral datasets, and the results demonstrate that the proposed method algorithm is significantly competitive among the HSI classification algorithms.
MS-Pansharpening Algorithm Based on Dual Constraint Guided Filtering
Authors: Wang Xianghai , Mu Zhenhua , Bai Shifu , Feng Yining , Song Ruoxi
Published: September 2022

The difference and complementarity of spatial and spectral information between multispectral (MS) image and panchromatic (PAN) image have laid the foundation for the fusion of the two types of images. In recent years, MS and PAN image fusion (also known as MS-Pansharpening) has gained attention as an important research area in remote sensing (RS) image processing. This paper proposes an MS-Pansharpening algorithm based on dual constraint Guided Filtering in the nonsubsampled shearlet transform (NSST) domain. The innovation is threefold. First, the dual constraint guided image filtering (DCGIF) model, based on spatial region average gradient correlation and vector correlation formed by neighborhood elements is proposed. Further, the PAN image detail information extraction scheme, based on the model, is provided, which extracts more complete and accurate detail information, thus avoiding, to some extent, the spectral distortion caused by the injection of non-adaptive information. Second, the weighted information injection model, based on the preservation of the correlation between the band spectra, is proposed. The model determines the information injection weight of each band pixel based on the spectral proportion between bands of the original MS image, which ensures the spectral correlation between bands of the fused MS image. Finally, a new MS-Pansharpening algorithm in NSST domain is proposed. The MS and PAN high frequency sub-bands of NSST are used to extract more effective spatial details. Then the proposed DCGIF model is used to extract the effective spatial detail injection information through the weighted joint method based on the regional energy matrix. Finally, the weighted information injection model is used to inject it into each band of MS to complete information fusion. Experimental results show that the proposed approach has better fusion effect than some conventional MS-Pansharpening algorithms, which can effectively improve the spatial resolution of the fused MS image and maintain the spectral characteristics of MS.
A Radar Detection Method of Plasma-Sheath-Covered Target Based on the Improved Keystone Algorithm
Authors: Bai Bowen , Ding Yi , Li Xiaoping , Liu Yanming
Published: September 2022

The aerodynamic thermal ionization affects the re-entry target, and the surface will form a ‘plasma sheath (PSh).’ The PSh with fluid characteristics will produce relative motion with the re-entry target. In the radar detection of the re-entry target, the relative motion characteristics cause the echo signal to couple different intra-pulse Doppler frequency components, forming a ‘false target’ on the one-dimensional range profile. In addition, the flight velocity of the re-entry target is exceptionally high (usually greater than 10 Mach), and there will be a severe phenomenon of migration through range cells (MTRC) during the detection period, which will make the coherent integration of the multi-period radar echo signal invalid and further affect the reliable detection of the re-entry target. Aiming at the ‘false target phenomenon’ and MTRC phenomenon in the process of re-entry target detection, this paper proposes an improved keystone algorithm. Based on the traditional keystone algorithm, a reliable, coherent integration method for radar echo of the plasma-sheath-covered target is proposed by modifying the scale transformation factor and constructing the Doppler frequency compensation function. It can effectively compensate the intra-pulse Doppler frequency and inter-pulse Doppler frequency to improve the energy gain of the real target and lay a theoretical foundation for the reliable detection of the plasma-sheath-covered target.
Hydrological Drivers for the Spatial Distribution of Wetland Herbaceous Communities in Poyang Lake
Authors: Huang Wenqin , Hu Tengfei , Mao Jingqiao , Montzka Carsten , Bol Roland , Wan Songxian , Li Jianxin , Yue Jin , Dai Huichao
Published: September 2022

Hydrological processes are known as major driving forces in structuring wetland plant communities, but the specific relationships are not always well understood. The recent dry conditions of Poyang Lake (i.e., the largest freshwater lake in China) are having a profound impact on its wetland vegetation, leading to the degradation of the entire wetland ecosystem. We developed an integrated framework to quantitatively investigate the relationship between the spatial distribution of major wetland herbaceous communities and the hydrological regimes of Poyang Lake. First, the wetland herbaceous community classification was built using a support-vector machine and simultaneous parameter optimization, achieving an overall accuracy of over 98%. Secondly, based on the inundation conditions since 2000, four hydrological drivers of the spatial distribution of these communities were evaluated by canonical correspondence analysis. Finally, the hydrological niches of the communities were quantified by Gaussian regression and quantile methods. The results show that there were significant interspecific differences in terms of the hydrological niche. For example, Carex cinerascens Ass was the most adaptable to inundation, while Triarrhena lutarioriparia + Phragmites australis Ass was the least. Our integrated analytical framework can contribute to hydrological management to better maintain the wetland plant community structure in the Poyang Lake area.
Remote Sensing Scene Graph and Knowledge Graph Matching with Parallel Walking Algorithm
Authors: Cui Wei , Hao Yuanjie , Xu Xing , Feng Zhanyun , Zhao Huilin , Xia Cong , Wang Jin
Published: September 2022

In deep neural network model training and prediction, due to the limitation of GPU memory and computing resources, massive image data must be cropped into limited-sized samples. Moreover, in order to improve the generalization ability of the model, the samples need to be randomly distributed in the experimental area. Thus, the background information is often incomplete or even missing. On this condition, a knowledge graph must be applied to the semantic segmentation of remote sensing. However, although a single sample contains only a limited number of geographic categories, the combinations of geographic objects are diverse and complex in different samples. Additionally, the involved categories of geographic objects often span different classification system branches. Therefore, existing studies often directly regard all the categories involved in the knowledge graph as candidates for specific sample segmentation, which leads to high computation cost and low efficiency. To address the above problems, a parallel walking algorithm based on cross modality information is proposed for the scene graph—knowledge graph matching (PWGM). The algorithm uses a graph neural network to map the visual features of the scene graph into the semantic space of the knowledge graph through anchors and designs a parallel walking algorithm of the knowledge graph that takes into account the visual features of complex scenes. Based on the algorithm, we propose a semantic segmentation model for remote sensing. The experiments demonstrate that our model improves the overall accuracy by 3.7% compared with KGGAT (which is a semantic segmentation model using a knowledge graph and graph attention network (GAT)), by 5.1% compared with GAT and by 13.3% compared with U-Net. Our study not only effectively improves the recognition accuracy and efficiency of remote sensing objects, but also offers useful exploration for the development of deep learning from a data-driven to a data-knowledge dual drive.
MSPR-Net: A Multi-Scale Features Based Point Cloud Registration Network
Authors: Yu Jinjin , Zhang Fenghao , Chen Zhi , Liu Liman
Published: September 2022

Point-cloud registration is a fundamental task in computer vision. However, most point clouds are partially overlapping, corrupted by noise and comprised of indistinguishable surfaces, especially for complexly distributed outdoor LiDAR point clouds, which makes registration challenging. In this paper, we propose a multi-scale features-based point cloud registration network named MSPR-Net for large-scale outdoor LiDAR point cloud registration. The main motivation of the proposed MSPR-Net is that the features of two keypoints from a true correspondence must match in different scales. From this point of view, we first utilize a multi-scale backbone to extract the multi-scale features of the keypoints. Next, we propose a bilateral outlier removal strategy to remove the potential outliers in the keypoints based on the multi-scale features. Finally, a coarse-to-fine registration way is applied to exploit the information both in feature and spatial space. Extensive experiments conducted on two large-scale outdoor LiDAR point cloud datasets demonstrate that MSPR-Net achieves state-of-the-art performance.
Optimization of Remote Sensing Image Segmentation by a Customized Parallel Sine Cosine Algorithm Based on the Taguchi Method
Authors: Fan Fang , Liu Gaoyuan , Geng Jiarong , Zhao Huiqi , Liu Gang
Published: September 2022

Affected by solar radiation, atmospheric windows, radiation aberrations, and other air and sky environmental factors, remote sensing images usually contain a large amount of noise and suffer from problems such as non-uniform image feature density. These problems bring great difficulties to the segmentation of high-precision remote sensing image. To improve the segmentation effect of remote sensing images, this study adopted an improved metaheuristic algorithm to optimize the parameter settings of pulse-coupled neural networks (PCNNs). Using the Taguchi method, the optimal parallelism scheme of the algorithm was effectively tailored for a specific target problem. The blindness in the design of the algorithm parallel structure was effectively avoided. The superiority of the customized parallel SCA based on the Taguchi method (TPSCA) was demonstrated in tests with different types of benchmark functions. In this study, simulations were performed using IKONOS, GeoEye-1, and WorldView-2 satellite remote sensing images. The results showed that the accuracy of the proposed remote sensing image segmentation model was significantly improved.
Bird-Borne Samplers for Monitoring CO2 and Atmospheric Physical Parameters
Authors: Di Bernardino Annalisa , Jennings Valeria , Dell’Omo Giacomo
Published: September 2022

Air quality monitoring in cities is significant for both human health and environment. Here, an innovative miniaturized active air sampler wearable by free-flying birds is presented. The device integrates a GPS logger and atmospheric calibrated sensors allowing for high spatiotemporal resolution measurements of carbon dioxide (CO2) concentration, barometric pressure, air temperature, and relative humidity. A field campaign, carried out from January to June 2021, involved the repeated release of homing pigeons (Columba livia) from downtown Rome (Italy), to sample the air on their way back to the loft, located in a rural area out of the city. The measurements suggest the importance of green urban areas in decreasing CO2 levels. Moreover, a positive relation between CO2 levels, relative humidity, and air temperature was revealed. In contrast, a negative relation with distance from the point of release, month, and time of day was found. Flight speed and the altitude of flight were related to rising CO2 levels. The easy use of such devices paves the way for the application of miniaturized air samplers to other synanthropic species (i.e., gulls), making birds convenient biomonitors for the urban environment.
Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach
Authors: de Vries Wiebe H. K. , Amrein Sabrina , Arnet Ursina , Mayrhuber Laura , Ehrmann Cristina , Veeger H. E. J.
Published: September 2022

Shoulder problems (pain and pathology) are highly prevalent in manual wheelchair users with spinal cord injury. These problems lead to limitations in activities of daily life (ADL), labor- and leisure participation, and increase the health care costs. Shoulder problems are often associated with the long-term reliance on the upper limbs, and the accompanying “shoulder load”. To make an estimation of daily shoulder load, it is crucial to know which ADL are performed and how these are executed in the free-living environment (in terms of magnitude, frequency, and duration). The aim of this study was to develop and validate methodology for the classification of wheelchair related shoulder loading ADL (SL-ADL) from wearable sensor data. Ten able bodied participants equipped with five Shimmer sensors on a wheelchair and upper extremity performed eight relevant SL-ADL. Deep learning networks using bidirectional long short-term memory networks were trained on sensor data (acceleration, gyroscope signals and EMG), using video annotated activities as the target. Overall, the trained algorithm performed well, with an accuracy of 98% and specificity of 99%. When reducing the input for training the network to data from only one sensor, the overall performance decreased to around 80% for all performance measures. The use of only forearm sensor data led to a better performance than the use of the upper arm sensor data. It can be concluded that a generalizable algorithm could be trained by a deep learning network to classify wheelchair related SL-ADL from the wearable sensor data.
Design and Analysis of Quartz Crystal Microbalance with a New Ring-Shaped Interdigital Electrode
Authors: Wang Pengyi , Ling Mingxiang , Li Minghai
Published: September 2022

In this paper, a new type of ring-shaped interdigital electrode is proposed to improve the accuracy and repeatability of quartz crystal microbalance. The influence of different types of single finger, dot finger, dot double-finger electrodes on mass sensitivity distribution as well as the optimal proportion of finger and gap width are obtained through multi-physical coupling simulation. The results show that the design criteria of interdigital electrodes will not change with the increase in the number of fingers. The gap width should obey the decrease order from central to edge and be about twice the width of finger. The width of the outermost finger and the radius of the middle dot electrode should be maintained at about 0.4 and 0.2 times of the total electrode radius. An experiment was carried out to verify that the quartz wafer with a dot double-finger electrode has high quality factors and less modal coupling, which can satisfy the engineering application well. As a conclusion, this study provides a design idea for the electrode to maintain a uniform distribution of quartz crystal microbalance mass sensitivity.
Robust Lidar-Inertial Odometry with Ground Condition Perception and Optimization Algorithm for UGV
Authors: Zhao Zixu , Zhang Yucheng , Shi Jinglin , Long Long , Lu Zaiwang
Published: September 2022

Unmanned ground vehicles (UGVs) are making more and more progress in many application scenarios in recent years, such as exploring unknown wild terrain, working in precision agriculture and serving in emergency rescue. Due to the complex ground conditions and changeable surroundings of these unstructured environments, it is challenging for these UGVs to obtain robust and accurate state estimations by using sensor fusion odometry without prior perception and optimization for specific scenarios. In this paper, based on an error-state Kalman filter (ESKF) fusion model, we propose a robust lidar-inertial odometry with a novel ground condition perception and optimization algorithm specifically designed for UGVs. The probability distribution gained from the raw inertial measurement unit (IMU) measurements during a certain time period and the state estimation of ESKF were both utilized to evaluate the flatness of ground conditions in real-time; then, by analyzing the relationship between the current ground condition and the accuracy of the state estimation, the tightly coupled lidar-inertial odometry was dynamically optimized further by adjusting the related parameters of the processing algorithm of the lidar points to obtain robust and accurate ego-motion state estimations of UGVs. The method was validated in various types of environments with changeable ground conditions, and the robustness and accuracy are shown through the consistent accurate state estimation in different ground conditions compared with the state-of-art lidar-inertial odometry systems.
Droplet Deposition Distribution Prediction Method for a Six-Rotor Plant Protection UAV Based on Inverse Distance Weighting
Authors: Wang Bin , Zhang Yan , Wang Chunshan , Teng Guifa
Published: September 2022

The aim of this work is to establish a method for real-time calculating droplet deposition distribution of a six-rotor plant protection unmanned aerial vehicle (UAV). The numerical simulation of the airflow field was carried out using computational fluid dynamics (CFD). The airflow field distribution was obtained under seven flight speeds, six flight heights, and seven crosswind speeds. The relative error verified the accuracy of the numerical model within 12% between the spatial point wind speed test and the simulated value. The numerical simulation results showed that with the improvement of the UAV flight speed and the crosswind, the relative airflow produces a vortex in the downwash wind field below the UAV and reduces the stability of the downwash wind field. The discrete droplet phase was introduced in the flow field. The ground regions were divided using a small grid of 0.5 m × 0.5 m, and statistical calculations of droplet deposition rates within each grid yielded the distribution of droplets under 294 different parameter combinations. The statistical results show that the relative airflow and crosswind caused droplet convolution, and droplet drift was increased. In the actual operation of the UAV, the flight speed should be well controlled under the condition of low environmental wind to reduce the droplet drift rate and improve the utilization rate of pesticides. Based on the distribution under 294 different parameter combinations, one droplet deposition prediction method was established using inverse distance weighting (IDW). The proposed method lays a foundation for the cumulative calculation of droplet deposition distribution during continuous operation of plant protection UAV. It provides a basis for objectively evaluating the operational quality of plant protection UAVs and optimizing the setting of operation parameters.
Towards a New Way of Understanding the Resilience of Socio-Technical Systems: The Safety Fractal Analysis Method Evaluated
Authors: Accou Bart , Reniers Genserik
Published: September 2022

Despite the systems approach to accident analysis being the dominant research paradigm and the concept of SMS being introduced in high-risk industries already for several years, accident investigation practice is still poor in analysing the basic elements that compose a safety management system (SMS) and in embracing system theory. In search of a systemic method for accident analysis that is easily applicable and less resource demanding than the actual methods, Accou and Reniers (2019) developed the SAfety FRactal ANalysis (SAFRAN) method. The method, which is based on the principles of an SMS with resilience as the explicit safety strategy, aims at finding a good balance between examining the complexity of a socio-technical system and making optimal use of limited resources and people; factors that often restrict the possibility for in-depth analysis of accidents. A series of practical tests, often involving active accident investigators, made it possible to examine and validate the SAFRAN method against the criteria Underwood (2013) developed to evaluate systemic accident analysis methods. Based on the performed evaluation, which includes elements related to the development of the method as well as system approach and usability characteristics, the study concludes that, when it comes to applying a systems approach to accident analysis and with the aim of creating more sustainable and resilient performance, the current investigation practice could gain from having the SAFRAN method as part of the investigation toolkit.
BPM2DDD: A Systematic Process for Identifying Domains from Business Processes Models
Authors: da Silva Carlos Eduardo , Gomes Eduardo Luiz , Basu Soumya Sankar
Published: September 2022

Domain-driven design is one of the most used approaches for identifying microservice architectures, which should be built around business capabilities. There are a number of documentation with principles and patterns for its application. However, despite its increasing use there is still a lack of systematic approaches for creating the context maps that will be used to design the microservices. This article presents BPM2DDD, a systematic approach for identification of bounded contexts and their relationships based on the analysis of business processes models, which provide a business view of an organisation. We present an example of its application in a real business process, which has also be used to perform a comparative application with external analysts. The technique has been applied to a real project in the department of transport of a Brazilian state capital, and has been incorporated into the software development process employed by them to develop their new system.