bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of the Release Mechanism and Mould Resistance of Citral-Loaded Bamboo Strips
Authors: Peng Rui , Zhang Jingjing , Du Chungui , Li Qi , Hu Ailian , Liu Chunlin , Chen Shiqin , Shan Yingying , Yin Wenxiu
Published: September 2021

In the present study, the sustained-release system loading citral was synthesised by using PNIPAm nanohydrogel as a carrier and analysed its drug-release kinetics and mechanism. Four release models, namely zero-order, first-order, Higuchi, and Peppas, were employed to fit the experimental data, and the underlying action mechanism was analysed. The optimised system was applied to treat a bamboo mould, followed by assessment of the mould-proof performance. Our experimental results revealed that the release kinetics equation of the system conformed to the first order; the higher the external temperature, the better the match was. In the release process, PNIPAm demonstrated a good protection and sustained-release effect on citral. Under the pressure of 0.5 MPa, immersion time of 120 min, and the system concentration ratio of 1, the optimal drug-loading parameters were obtained using the slow-release system with the best release parameters. Compared to the other conditions, bamboos treated with pressure impregnation demonstrated a better control effect on bamboo mould, while the control effect on Penicillium citrinum, Trichoderma viride, Aspergillus niger, and mixed mould was 100% after 28 days. Moreover, the structure and colour of bamboo remained unchanged during the entire process of mould control.
Impact-Resistant and Tough 3D Helicoidally Architected Polymer Composites Enabling Next-Generation Lightweight Silicon Photovoltaics Module Design and Technology
Authors: Budiman Arief Suriadi , Sahay Rahul , Agarwal Komal , Illya Gregoria , Widjaja Ryo Geoffrey , Baji Avinash , Raghavan Nagarajan
Published: September 2021

Lightweight photovoltaics (PV) modules are important for certain segments of the renewable energy markets—such as exhibition halls, factories, supermarkets, farms, etc. However, lightweight silicon-based PV modules have their own set of technical challenges or concerns. One of them, which is the subject of this paper, is the lack of impact resistance, especially against hailstorms in deep winter in countries with four seasons. Even if the front sheet can be made sufficiently strong and impact-resistant, the silicon cells inside remain fragile and very prone to impact loading. This leads to cracks that significantly degrade performance (output power) over time. A 3D helicoidally architected fiber-based polymer composite has recently been found to exhibit excellent impact resistance, inspired by the multi-hierarchical internal structures of the mantis shrimp’s dactyl clubs. In previous work, our group demonstrated that via electrospinning-based additive manufacturing methodologies, weak polymer material constituents could be made to exhibit significantly improved toughness and impact properties. In this study, we demonstrate the use of 3D architected fiber-based polymer composites to protect the silicon solar cells by absorbing impact energy. The absorbed energy is equivalent to the energy that would impact the solar cells during hailstorms. We have shown that silicon cells placed under such 3D architected polymer layers break at substantially higher impact load/energy (compared to those placed under standard PV encapsulation polymer material). This could lead to the development of novel PV encapsulant materials for the next generation of lightweight PV modules and technology with excellent impact resistance.
Mussel-Inspired Catechol Functionalisation as a Strategy to Enhance Biomaterial Adhesion: A Systematic Review
Authors: Costa Pedro M. , Learmonth David A. , Gomes David B. , Cautela Mafalda P. , Oliveira Ana C. N. , Andrade Renato , Espregueira-Mendes João , Veloso Tiago R. , Cunha Cristiana B. , Sousa Rui A.
Published: September 2021

Biomaterials have long been explored in regenerative medicine strategies for the repair or replacement of damaged organs and tissues, due to their biocompatibility, versatile physicochemical properties and tuneable mechanical cues capable of matching those of native tissues. However, poor adhesion under wet conditions (such as those found in tissues) has thus far limited their wider application. Indeed, despite its favourable physicochemical properties, facile gelation and biocompatibility, gellan gum (GG)-based hydrogels lack the tissue adhesiveness required for effective clinical use. Aiming at assessing whether substitution of GG by dopamine (DA) could be a suitable approach to overcome this problem, database searches were conducted on PubMed® and Embase® up to 2 March 2021, for studies using biomaterials covalently modified with a catechol-containing substituent conferring improved adhesion properties. In this regard, a total of 47 reports (out of 700 manuscripts, ~6.7%) were found to comply with the search/selection criteria, the majority of which (34/47, ~72%) were describing the modification of natural polymers, such as chitosan (11/47, ~23%) and hyaluronic acid (6/47, ~13%); conjugation of dopamine (as catechol “donor”) via carbodiimide coupling chemistry was also predominant. Importantly, modification with DA did not impact the biocompatibility and mechanical properties of the biomaterials and resulting hydrogels. Overall, there is ample evidence in the literature that the bioinspired substitution of polymers of natural and synthetic origin by DA or other catechol moieties greatly improves adhesion to biological tissues (and other inorganic surfaces).
Major Factors Influencing the Size Distribution Analysis of Cellulose Nanocrystals Imaged in Transmission Electron Microscopy
Authors: Qian Hui
Published: September 2021

Size distributions of cellulose nanocrystals (CNCs), extracted from softwood pulp via strong sulfuric acid hydrolysis, exhibit large variability when analyzed from transmission electron microscopy (TEM) images. In this article, the causes of this variability are studied and discussed. In order to obtain results comparable with those reported, a reference material of CNCs (CNCD-1) was used to evaluate size distribution. CNC TEM specimens were prepared as-stained and dried with a rapid-flushing staining method or hydrated and embedded in vitreous ice with the plunge-freezing method. Several sets of bright-field TEM (BF-TEM), annular dark-field scanning TEM (ADF-STEM) and cryogenic-TEM (cryo-TEM) images were acquired for size distribution analysis to study the contributing factors. The rapid-flushing staining method was found to be the most effective for contrast enhancement of CNCs, not only revealing the helical structure of single CNCs but also resolving the laterally jointed CNCs. During TEM specimen preparation, CNCs were fractionated on TEM grids driven by the coffee-ring effect, as observed from contrast variation of CNCs with a stain-depth gradient. From the edge to the center of the TEM grids, the width of CNCs increases, while the aspect ratio (length to width) decreases. This fractionated dispersion of CNCs suggests that images taken near the center of a droplet would give a larger mean width. In addition to particle fractionation driven by the coffee-ring effect, the arrangement and orientation of CNC particles on the substrate significantly affect the size measurement when CNC aggregation cannot be resolved in images. The coexistence of asymmetric cross-section CNC particles introduces a large variation in size measurement, as TEM images of CNCs are mixed projections of the width and height of particles. As a demonstration of how this contributes to inflated size measurement, twisted CNC particles, rectangular cross-section particles and end-to-end jointed CNCs were revealed in reconstructed three-dimensional (3D) micrographs by electron tomography (ET).
Experimental Study of Bentonite-Free Water Based Mud Reinforced with Carboxymethylated Tapioca Starch: Rheological Modeling and Optimization Using Response Surface Methodology (RSM)
Authors: Ali Imtiaz , Ahmad Maqsood , Ganat Tarek
Published: September 2021

Drilling mud’s rheological characteristics, such as plastic viscosity and yield point, are adversely affected with an inappropriate mud formulation. Native starch is one of the most important components in water-based mud because it improves the rheological and filtration characteristics of the mud. The native starch stability under various temperature and exposure time regimes is an important concern for utilizing starch in oil and gas drilling operations. In this work, tapioca starch was modified using carboxymethylation for the first time in order to improve its performance in non-damaging water-based muds. The modified starch was characterized by Fourier-transform infrared spectroscopy and X-ray diffraction. The thermal stability was tested using thermal gravimetric analysis. Various mud blends were formulated based on the experimental design using response surface methodology (RSM) to investigate their performance at various temperature conditions. Thirty experimental runs were carried out based on the selected factors and responses considering the optimal (custom) design, and the results were analyzed through ANOVA. The Fourier-transform infrared spectroscopy and X-ray diffraction results confirmed the carboxymethylation of starch. The TGA analysis revealed strong thermal stability after modification. Additionally, the Power law model (PLM) described the obtained rheological data for the selected formulations, resulting in determination coefficients of more than 0.95. Furthermore, the examined samples showed a reduction in the flow behavior index from 0.30 to 0.21 and an increase in the consistency index from 5.6 to 15.1. Optimization and confirmation results revealed the adequacy of the generated empirical models for both plastic viscosity and yield point. The obtained consistency index values provided a direct relationship with the modified starch concentration, indicating an improvement in the cutting carrying capacity of mud. Based on the current literature survey, the studied formulation has not been reported in the literature.
Nature-Based Biomaterials and Their Application in Biomedicine
Authors: Troy Eoin , Tilbury Maura A. , Power Anne Marie , Wall J. Gerard
Published: September 2021

Natural polymers, based on proteins or polysaccharides, have attracted increasing interest in recent years due to their broad potential uses in biomedicine. The chemical stability, structural versatility, biocompatibility and high availability of these materials lend them to diverse applications in areas such as tissue engineering, drug delivery and wound healing. Biomaterials purified from animal or plant sources have also been engineered to improve their structural properties or promote interactions with surrounding cells and tissues for improved in vivo performance, leading to novel applications as implantable devices, in controlled drug release and as surface coatings. This review describes biomaterials derived from and inspired by natural proteins and polysaccharides and highlights their promise across diverse biomedical fields. We outline current therapeutic applications of these nature-based materials and consider expected future developments in identifying and utilising innovative biomaterials in new biomedical applications.
Hyaluronic Acid-Based Gold Nanoparticles for the Topical Delivery of Therapeutics to the Retina and the Retinal Pigment Epithelium
Authors: Laradji Amine , Karakocak Bedia B. , Kolesnikov Alexander V. , Kefalov Vladimir J. , Ravi Nathan
Published: September 2021

The ocular immune privilege is a phenomenon brought about by anatomical and physiological barriers to shield the eye from immune and inflammation responses. While this phenomenon is beneficial for eyes protection, it is, at the same time, a hindrance for drug delivery to the posterior segment of the eye to treat retinal diseases. Some ocular barriers can be bypassed by intravitreal injections, but these are associated with several side effects and patient noncompliance, especially when frequent injections are required. As an alternative, applying drugs as an eye drop is preferred due to the safety and ease. This study investigated the possible use of topically-applied hyaluronic acid-coated gold nanoparticles as drug delivery vehicles to the back of the eye. The coated gold nanoparticles were topically applied to mouse eyes, and results were compared to topically applied uncoated gold nanoparticles and phosphate-buffered saline (PBS) solution. Retina sections from these mice were then analyzed using fluorescence microscopy, inductively coupled plasma mass spectrometry (ICP-MS), and transmission electron microscopy (TEM). All characterization techniques used in this study suggest that hyaluronic acid-coated gold nanoparticles have higher distribution in the posterior segment of the eye than uncoated gold nanoparticles. Electroretinogram (ERG) analysis revealed that the visual function of mice receiving the coated gold nanoparticles was not affected, and these nanoparticles can, therefore, be applied safely. Together, our results suggest that hyaluronic acid-coated gold nanoparticles constitute potential drug delivery vehicles to the retina when applied noninvasively as an eye drop.
Determination of Heavy Metal Contamination and Pollution Indices of Roadside Dust in Dhaka City, Bangladesh
Authors: Kabir Md Humayun , Rashid Md Harun , Wang Qingyue , Wang Weiqian , Lu Senlin , Yonemochi Shinichi
Published: September 2021

Urban roadside dust samples from Dhaka City in Bangladesh were collected from a planned residential area (PRA), spontaneous residential area (SRA), commercial area (CA), and urban green area (UGA) in winter and summer to study how season and different urban land-use categories influence the concentrations of heavy metals (Cr, Mn, Co, Ni, Cu, Zn, As, and Pb) and different pollution indices. The dust samples were fractionated into <32 μm particles, extracted by acid digestion followed by estimation of heavy metals, using ICP-MS. Pollution indices were calculated from the metal concentrations, using standard protocols. The concentrations of heavy metals in roadside dust varied significantly (all p < 0.05), due to sampling seasons and the land-use category. Higher concentrations of heavy metals (Cr, Mn, Ni, Cu, Zn, and Pb) were found in the dust sampled during the winter season than in the summer season, except for As and Co. The geo-accumulation index (Igeo) indicated that the commercial area was heavily contaminated with Cu and Zn during the winter season. The contamination factor (CF) was higher for Cu and Zn in the CA, PRA, and SRA of Dhaka City in winter than in the summer season. The enrichment factor (EF) suggested that Mn and Co were the least enriched metals, and significant enrichment was seen for Cu and Zn for all land-use categories, both in summer and winter. A moderate potential ecological risk for Cu was estimated in CA and PRA in the winter season.
An Optimization Framework for Low-Carbon Oriented Integrated Energy System Management in Commercial Building under Electric Vehicle Demand Response
Authors: Wang Zesen , Li Xin , Li Yu , Zhao Tianqi , Xia Xue , Zhang Hanzhi
Published: September 2021

As the carbon emissions of commercial buildings are attracting considerable attention, the integrated energy system (IES) has become a promising low-carbon method in response. In this paper, an optimization framework for low-carbon oriented integrated energy system management under electric vehicles (EV) demand response is proposed. After analyzing the charging behavior, EV charging demand is simulated. Then, the low-carbon integrated energy system model is proposed with the optimization framework considering carbon reduction. Subsequently the objective function containing carbon emission is obtained for the whole operation optimization. The results of the studied case show that the optimization framework proposed can reduce the carbon emission greatly as well as moderate economic cost, which declined because of the revenue from charging demand response. In general, the optimization of low-carbon oriented IES in commercial buildings is feasible.
Drying Characteristics and Quality Attributes Affected by a Fluidized-Bed Drying Assisted with Swirling Compressed-Air for Preparing Instant Red Jasmine Rice
Authors: Chupawa Prarin , Gaewsondee Tiwanat , Duangkhamchan Wasan
Published: September 2021

A new process for the production of instant red jasmine rice was investigated using fluidized bed drying with the aid of swirling compressed air. Drying characteristics were evaluated using the operating parameters of fluidizing air temperature (90–120 °C) and pressure of swirling compressed air (4–6 bar). Appropriate air pressure was determined based on the highest value of model parameters from the semi-empirical Page equation and effective diffusivity. Influences of supply time of swirling compressed air (2–10 min) and drying temperature of 90–120 °C were investigated and optimized based on the quality attributes using response surface methodology. Drying at 120 °C and compressed air pressure of 6 bar gave the highest rate constant and effective diffusion coefficient. Drying at 120 °C combined with injecting swirling air for 2 min was the most suitable approach, while drying at 90 °C and supplying compressed air for 10 min was the best choice to preserve antioxidant properties. Air temperature of 98.5 °C with 2 min supply of swirling compressed air suitably provided high physical and rehydration properties and retained high health benefits of antioxidant compounds. Finally, after rehydration in warm water at 70 °C for 10 min, the textural properties of the rehydrated rice sample were comparable to conventionally cooked rice.
The Invention of Perspective and the Transition from the Sacred to the Religious
Authors: García Sánchez Rafael
Published: September 2021

Many historical events and scientific and artistic discoveries took place at the twilight of the Late Middle Ages and the beginning of the Renaissance. The great majority of them enabled the birth of individualism and the objectification of reality. This was decisively influenced by the devaluation of the oral and the prioritization of the visual. The priority of sight as a sovereign form of knowledge had in the invention of perspective one of its most unquestionable foundations. All of this caused a change in mentality that, in the field of aesthetics, gave rise to a new conception of the artist and his authorship and, undoubtedly, to the prevalence of the religious and devotional over the sacred.
A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net
Authors: Lu Zhiying , Wang Zehan , Li Xin , Zhang Jianfeng
Published: September 2021

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.
Optical and Microphysical Properties of Aged Biomass Burning Aerosols and Mixtures, Based on 9-Year Multiwavelength Raman Lidar Observations in Athens, Greece
Authors: Mylonaki Maria , Papayannis Alexandros , Anagnou Dimitra , Veselovskii Igor , Papanikolaou Christina-Anna , Kokkalis Panagiotis , Soupiona Ourania , Foskinis Romanos , Gidarakou Marilena , Kralli Eleni
Published: September 2021

Mean optical and microphysical aerosol properties of long-range transported biomass burning (BB) particles and mixtures are presented from a 9-year (2011–2019) data set of multiwavelength Raman lidar data, obtained by the EOLE lidar over the city of Athens (37.58° N, 23.47° E), Greece. We studied 34 aerosol layers characterized as: (1) smoke; (2) smoke + continental polluted, and (3) smoke + mixed dust. We found, mainly, small-sized aerosols with mean backscatter-related (355 nm/532 nm, 532 nm/1064 nm) values and Ångström exponent (AE) values in the range 1.4–1.7. The lidar ratio (LR) value at 355 nm was found to be 57 ± 10 sr, 51 ± 5 sr, and 38 ± 9 sr for the aerosol categories (1), (2), and (3), respectively; while at 532 nm, we observed LR values of 73 ± 11 sr, 59 ± 10 sr, and 62 ± 12 for the same categories. Regarding the retrieved microphysical properties, the effective radius (reff) ranged from 0.24 ± 0.11 to 0.24 ± 0.14 μm for all aerosol categories, while the volume density (vd) ranged from 8.6 ± 3.2 to 20.7 ± 14.1 μm−3cm−3 with the higher values linked to aerosol categories (1) and (2); the real part of the refractive index (mR) ranged between 1.49 and 1.53, while for the imaginary part (mI), we found values within 0.0108 i and 0.0126 i. Finally, the single scattering albedo (SSA) of the propped particles varied from 0.915 to 0.936 at all three wavelengths (355–532–1064 nm). The novelty of this study is the provision of typical values of BB aerosol properties from the UV to the near IR, which can be used in forecasting the aerosol climatic effects in the European region.
Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic
Authors: Zhao Jiechen , Yu Yining , Cheng Jingjing , Guo Honglin , Li Chunhua , Shu Qi
Published: September 2021

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.
A Framework for Generating High Spatiotemporal Resolution Land Surface Temperature in Heterogeneous Areas
Authors: Zhu Xinming , Song Xiaoning , Leng Pei , Li Xiaotao , Gao Liang , Guo Da , Cai Shuohao
Published: September 2021

Land surface temperature (LST) is a crucial biophysical parameter related closely to the land–atmosphere interface. Satellite thermal infrared measurement provides an effective method to derive LST on regional and global scales, but it is very hard to acquire simultaneously high spatiotemporal resolution LST due to its limitation in the sensor design. Recently, many LST downscaling and spatiotemporal image fusion methods have been widely proposed to solve this problem. However, most methods ignored the spatial heterogeneity of LST distribution, and there are inconsistent image textures and LST values over heterogeneous regions. Thus, this study aims to propose one framework to derive high spatiotemporal resolution LSTs in heterogeneous areas by considering the optimal selection of LST predictors, the downscaling of MODIS LST, and the spatiotemporal fusion of Landsat 8 LST. A total of eight periods of MODIS and Landsat 8 data were used to predict the 100-m resolution LST at prediction time tp in Zhangye and Beijing of China. Further, the predicted LST at tp was quantitatively contrasted with the LSTs predicted by the regression-then-fusion strategy, STARFM-based fusion, and random forest-based regression, and was validated with the actual Landsat 8 LST product at tp. Results indicated that the proposed framework performed better in characterizing LST texture than the referenced three methods, and the root mean square error (RMSE) varied from 0.85 K to 2.29 K, and relative RMSE varied from 0.18 K to 0.69 K, where the correlation coefficients were all greater than 0.84. Furthermore, the distribution error analysis indicated the proposed new framework generated the most area proportion at 0~1 K in some heterogeneous regions, especially in artificial impermeable surfaces and bare lands. This means that this framework can provide a set of LST dataset with reasonable accuracy and a high spatiotemporal resolution over heterogeneous areas.
Subsoil Recognition for Road Investment Supported by the Integration of Geodetic and GPR Data in the Form of a Point Cloud
Authors: Ortyl Łukasz , Gabryś Marta
Published: September 2021

During road construction investments, the key issue affecting the structure’s safety is accurate subsoil recognition. Identifying subsoil variability zones or natural voids can be performed using geophysical methods, and ground-penetrating radar (GPR) is recommended for this task as it identifies the location and spatial range karst formations. This paper describes the methodology of acquisition and processing of GPR data for ground recognition for road investment. Additional subsoil research was performed after karst phenomena were identified in the investment area, formations not revealed by geological recognition from earlier studies during the pre-design stage. Mala Ramac CU II radar with a 250 MHz antenna and a Leica DS2000 with 250 and 700 MHz antennas with real-time geopositioning were used to obtain the data. Regarding GPR data postprocessing, we present a method of converting spatial visualization into a point cloud that allows for GPR and geodetic data integration and confrontation. This approach enabled us to determine the locations of control trenches, the results of which were used for material validation, which is necessary to improve the reliability of subsoil recognition. The results showed a high correlation between the recorded GPR signals and the subsoil structure. Additionally, differences in the quality of results for measurements conducted before laying supporting layers with slag and on the completed road structure surface are illustrated.
Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields
Authors: Ma Chunfeng , Wang Shuguo , Zhao Zebin , Ma Hanqing
Published: September 2021

The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.
Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery
Authors: Zhang Tianxiang , Xu Zhiyong , Su Jinya , Yang Zhifang , Liu Cunjia , Chen Wen-Hua , Li Jiangyun
Published: September 2021

Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales.
Design and Optimization of a Linear Wavenumber Spectrometer with Cylindrical Optics for Line Scanning Optical Coherence Tomography
Authors: Samadi Sevin , Dargahi Javad , Narayanswamy Sivakumar
Published: September 2021

We report the design of a high-efficiency spectral-domain spectrometer with cylindrical optics for line scanning optical coherence tomography (OCT). The spectral nonlinearity in k space (wavenumber) lowers the depth-dependent signal sensitivity of the spectrometers. For linearizing, in this design, grating and prism have been introduced. For line scanning, a cylindrical mirror is utilized in the scanning part. Line scanning improves the speed of imaging compared to fly-spot scanning. Line scanning OCT requires a spectrometer that utilizes cylindrical optics. In this work, an optical design of a linear wavenumber spectrometer with cylindrical optics is introduced. While there are many works using grating and prism to linearize the K space spectrometer design, there is no work on linearizing the k-space spectrometer with cylindrical optics for line scanning that provides high sensitivity and high-speed imaging without the need for resampling. The design of the spectrometer was achieved through MATLAB and ZEMAX simulations. The spectrometer design is optimized for the broadband light source with a center wavelength of 830 ± 100 nm (8.607 μm−1− 6.756 μm−1 in k-space). The variation in the output angle with respect to the wavenumber can be mentioned as a nonlinearity error. From our design results, it is observed that the nonlinearity error reduced from 147.0115 to 0.0149 Δθ*μm within the wavenumber range considered. The use of the proposed reflective optics for focusing reduces the chromatic aberration and increases image quality (measured by the Strehl ratio (SR)). The complete system will provide clinicians a powerful tool for real-time diagnosis, treatment, and guidance in surgery with high image quality for in-vivo applications.
Terahertz Imaging for Breast Cancer Detection
Authors: Wang Lulu
Published: September 2021

Terahertz (THz) imaging has the potential to detect breast tumors during breast-conserving surgery accurately. Over the past decade, many research groups have extensively studied THz imaging and spectroscopy techniques for identifying breast tumors. This manuscript presents the recent development of THz imaging techniques for breast cancer detection. The dielectric properties of breast tissues in the THz range, THz imaging and spectroscopy systems, THz radiation sources, and THz breast imaging studies are discussed. In addition, numerous chemometrics methods applied to improve THz image resolution and data collection processing are summarized. Finally, challenges and future research directions of THz breast imaging are presented.
The Optical Coherence Tomography and Raman Spectroscopy for Sensing of the Bone Demineralization Process
Authors: Głowacki Maciej J. , Kamińska Aleksandra M. , Gnyba Marcin , Pluciński Jerzy , Strąkowski Marcin R.
Published: September 2021

The presented research was intended to seek new optical methods to investigate the demineralization process of bones. Optical examination of the bone condition could facilitate clinical trials and improve the safety of patients. The authors used a set of complementary methods: polarization-sensitive optical coherence tomography (PS-OCT) and Raman spectroscopy. Chicken bone samples were used in this research. To stimulate in laboratory conditions the process of demineralization and gradual removal of the hydroxyapatite, the test samples of bones were placed into 10% acetic acid. Measurements were carried out in two series. The first one took two weeks with data acquired every day. In the second series, the measurements were made during one day at an hourly interval (after 1, 2, 3, 5, 7, 10, and 24 h). The relation between the content of hydroxyapatite and images recorded using OCT was analyzed and discussed. Moreover, the polarization properties of the bones, including retardation angles of the bones, were evaluated. Raman measurement confirmed the disappearance of the hydroxyapatite and the speed of this process. This work presents the results of the preliminary study on the possibility of measuring changes in bone mineralization by means of the proposed methods and confirms their potential for practical use in the future.
Seeking Inspiration: Examining the Validity and Reliability of a New Smartphone Respiratory Therapy Exergame App
Authors: Baxter Clarence , Carroll Julie-Anne , Keogh Brendan , Vandelanotte Corneel
Published: September 2021

Background: Clinically valid and reliable simulated inspiratory sounds were required for the development and evaluation of a new therapeutic respiratory exergame application (i.e., QUT Inspire). This smartphone application virtualises incentive spirometry, a longstanding respiratory therapy technique. Objectives: Inspiratory flows were simulated using a 3 litre calibration syringe and validated using clinical reference devices. Syringe flow nozzles of decreasing diameter were applied to model the influence of mouth shape on audible sound levels generated. Methods: A library of calibrated audio inspiratory sounds was created to determine the reliability and range of inspiratory sound detection at increasing distances separating the sound source and smartphones running the app. Results: Simulated inspiratory sounds were reliably detected by the new application at higher air inflows (high, medium), using smaller mouth diameters (<25 mm) and where smartphones were held proximal (≤5 cm) to the mouth (or at distances up to 50 cm for higher airflows). Performance was comparable for popular smartphone types and using different phone orientations (i.e., held horizontally, at 45° or 90°). Conclusions: These observations inform future application refinements, including prompts to reduce mouth diameter, increase inspiratory flow and maintain proximity to the phone to optimise sound detection. This library of calibrated inspiratory sounds offers reproducible non-human reference data suitable for development, evaluation and regression testing of a therapeutic respiratory exergame application for smartphones.
Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data
Authors: Phillips Tyson , D’Adamo Tim , McAree Peter
Published: September 2021

The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration.
Phase Error Analysis and Correction for Crossed-Grating Phase-Shifting Profilometry
Authors: Li Fuqian , Chen Wenjing
Published: September 2021

Crossed-grating phase-shifting profilometry (CGPSP) has great utility in three-dimensional shape measurement due to its ability to acquire horizontal and vertical phase maps in a single measurement. However, CGPSP is extremely sensitive to the non-linearity effect of a digital fringe projection system, which is not studied in depth yet. In this paper, a mathematical model is established to analyze the phase error caused by the non-linearity effect. Subsequently, two methods used to eliminate the non-linearity error are discussed in detail. To be specific, a double five-step algorithm based on the mathematical model is proposed to passively suppress the second non-linearity. Furthermore, a precoding gamma correction method based on probability distribution function is introduced to actively attenuate the non-linearity of the captured crossed fringe. The comparison results show that the active gamma correction method requires less fringe patterns and can more effectively reduce the non-linearity error compared with the passive method. Finally, employing CGPSP with gamma correction, a faster and reliable inverse pattern projection is realized with less fringe patterns.
The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
Authors: Casals Lluís , Gomez Carles , Vidal Rafael
Published: September 2021

LoRaWAN has become a popular technology for the Internet of Things (IoT) device connectivity. One of the expected properties of LoRaWAN is high network scalability. However, LoRaWAN network performance may be compromised when even a relatively small number of devices use link-layer reliability. After failed frame delivery, such devices typically tend to reduce their physical layer bit rate by increasing their spreading factor (SF). This reaction increases channel utilization, which may further degrade network performance, even into congestion collapse. When this problem arises, all the devices performing reliable frame transmission end up using SF12 (i.e., the highest SF in LoRaWAN). In this paper, we identify and characterize the described network condition, which we call the SF12 Well, in a range of scenarios and by means of extensive simulations. The results show that by using alternative SF-management techniques it is possible to avoid the problem, while achieving a packet delivery ratio increase of up to a factor of 4.7.
An Efficient Closed Form Solution to the Absolute Orientation Problem for Camera with Unknown Focal Length
Authors: Guo Kai , Ye Hu , Zhao Zinian , Gu Junhao
Published: September 2021

In this paper we propose an efficient closed form solution to the absolute orientation problem for cameras with an unknown focal length, from two 2D–3D point correspondences and the camera position. The problem can be decomposed into two simple sub-problems and can be solved with angle constraints. A polynomial equation of one variable is solved to determine the focal length, and then a geometric approach is used to determine the absolute orientation. The geometric derivations are easy to understand and significantly improve performance. Rewriting the camera model with the known camera position leads to a simpler and more efficient closed form solution, and this gives a single solution, without the multi-solution phenomena of perspective-three-point (P3P) solvers. Experimental results demonstrated that our proposed method has a better performance in terms of numerical stability, noise sensitivity, and computational speed, with synthetic data and real images.
Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation
Authors: Fournelle Marc , Grün Tobias , Speicher Daniel , Weber Steffen , Yilmaz Mehmet , Schoeb Dominik , Miernik Arkadiusz , Reis Gerd , Tretbar Steffen , Hewener Holger
Published: September 2021

We developed a new mobile ultrasound device for long-term and automated bladder monitoring without user interaction consisting of 32 transmit and receive electronics as well as a 32-element phased array 3 MHz transducer. The device architecture is based on data digitization and rapid transfer to a consumer electronics device (e.g., a tablet) for signal reconstruction (e.g., by means of plane wave compounding algorithms) and further image processing. All reconstruction algorithms are implemented in the GPU, allowing real-time reconstruction and imaging. The system and the beamforming algorithms were evaluated with respect to the imaging performance on standard sonographical phantoms (CIRS multipurpose ultrasound phantom) by analyzing the resolution, the SNR and the CNR. Furthermore, ML-based segmentation algorithms were developed and assessed with respect to their ability to reliably segment human bladders with different filling levels. A corresponding CNN was trained with 253 B-mode data sets and 20 B-mode images were evaluated. The quantitative and qualitative results of the bladder segmentation are presented and compared to the ground truth obtained by manual segmentation.
Informed Attentive Predictors: A Generalisable Architecture for Prior Knowledge-Based Assisted Diagnosis of Cancers
Authors: Li Han , Qiu Linling , Wang Meihong
Published: September 2021

Due to the high mortality of many cancers and their related diseases, the prediction and prognosis techniques of cancers are being extensively studied to assist doctors in making diagnoses. Many machine-learning-based cancer predictors have been put forward, but many of them have failed to become widely utilised due to some crucial problems. For example, most methods require too much training data, which is not always applicable to institutes, and the complicated genetic mutual effects of cancers are generally ignored in many proposed methods. Moreover, a majority of these assist models are actually not safe to use, as they are generally built on black-box machine learners that lack references from related field knowledge. We observe that few machine-learning-based cancer predictors are capable of employing prior knowledge (PrK) to mitigate these issues. Therefore, in this paper, we propose a generalisable informed machine learning architecture named the Informed Attentive Predictor (IAP) to make PrK available to the predictor’s decision-making phases and apply it to the field of cancer prediction. Specifically, we make several implementations of the IAP and evaluate its performance on six TCGA datasets to demonstrate the effectiveness of our architecture as an assist system framework for actual clinical usage. The experimental results show a noticeable improvement in IAP models on accuracies, f1-scores and recall rates compared to their non-IAP counterparts (i.e., basic predictors).
Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System
Authors: Mohamed Nur Ayuni , Zulkifley Mohd Asyraf , Ibrahim Ahmad Asrul , Aouache Mustapha
Published: September 2021

In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a person unintentionally drops down onto a lower surface. It is crucial to detect the occurrence of fall events as early as possible so that any severe fall consequences can be minimized. Nonetheless, a fall event is a sporadic incidence that occurs seldomly that is falsely detected due to a wide range of fall conditions and situations. Therefore, an automated fall frame detection system, which is referred to as the SmartConvFall is proposed to detect the exact fall frame in a video sequence. It is crucial to know the exact fall frame as it dictates the response time of the system to administer an early treatment to reduce the fall’s negative consequences and related injuries. Henceforth, searching for the optimal training configurations is imperative to ensure the main goal of the SmartConvFall is achieved. The proposed SmartConvFall consists of two parts, which are object tracking and instantaneous fall frame detection modules that rely on deep learning representations. The first stage will track the object of interest using a fully convolutional neural network (CNN) tracker. Various training configurations such as optimizer, learning rate, mini-batch size, number of training samples, and region of interest are individually evaluated to determine the best configuration to produce the best tracker model. Meanwhile, the second module goal is to determine the exact instantaneous fall frame by modeling the continuous object trajectories using the Long Short-Term Memory (LSTM) network. Similarly, the LSTM model will undergo various training configurations that cover different types of features selection and the number of stacked layers. The exact instantaneous fall frame is determined using an assumption that a large movement difference with respect to the ground level along the vertical axis can be observed if a fall incident happened. The proposed SmartConvFall is a novel technique as most of the existing methods still relying on detection rather than the tracking module. The SmartConvFall outperforms the state-of-the-art trackers, namely TCNN and MDNET-N trackers, with the highest expected average overlap, robustness, and reliability metrics of 0.1619, 0.6323, and 0.7958, respectively. The SmartConvFall also managed to produce the lowest number of tracking failures with only 43 occasions. Moreover, a three-stack LSTM delivers the lowest mean error with approximately one second delay time in locating the exact instantaneous fall frame. Therefore, the proposed SmartConvFall has demonstrated its potential and suitability to be implemented for a real-time application that could help to avoid any crucial fall consequences such as death and internal bleeding if the early treatment can be administered.