bluuebunny/crossref_metadata_2025 huggingface dataset:
Formulation Strategies to Improve the Stability and Handling of Oral Solid Dosage Forms of Highly Hygroscopic Pharmaceuticals and Nutraceuticals
Authors: Ng Liu Han , Ling Jordy Kim Ung , Hadinoto Kunn
Published: September 2022

Highly hygroscopic pharmaceutical and nutraceutical solids are prone to significant changes in their physicochemical properties due to chemical degradation and/or solid-state transition, resulting in adverse effects on their therapeutic performances and shelf life. Moisture absorption also leads to excessive wetting of the solids, causing their difficult handling during manufacturing. In this review, four formulation strategies that have been employed to tackle hygroscopicity issues in oral solid dosage forms of pharmaceuticals/nutraceuticals were discussed. The four strategies are (1) film coating, (2) encapsulation by spray drying or coacervation, (3) co-processing with excipients, and (4) crystal engineering by co-crystallization. Film coating and encapsulation work by acting as barriers between the hygroscopic active ingredients in the core and the environment, whereas co-processing with excipients works mainly by adding excipients that deflect moisture away from the active ingredients. Co-crystallization works by altering the crystal packing arrangements by introducing stabilizing co-formers. For hygroscopic pharmaceuticals, coating and co-crystallization are the most commonly employed strategies, whereas coating and encapsulation are popular for hygroscopic nutraceuticals (e.g., medicinal herbs, protein hydrolysates). Encapsulation is rarely applied on hygroscopic pharmaceuticals, just as co-crystallization is rarely used for hygroscopic nutraceuticals. Therefore, there is potential for improved hygroscopicity reduction by exploring beyond the traditionally used strategy.
Numerical Modeling of Damage Caused by Seawater Exposure on Mechanical Strength in Fiber-Reinforced Polymer Composites
Authors: Vidinha Hugo , Branco Ricardo , Neto Maria Augusta , Amaro Ana M. , Reis Paulo
Published: September 2022

Fiber-reinforced polymer composites are frequently used in marine environments which may limit their durability. The development of accurate engineering tools capable of simulating the effect of seawater on material strength can improve design and reduce structural costs. This paper presents a numerical-based approach to predict the stress–strain response of fiber-reinforced polymer composites exposed to different seawater immersion times, ranging from 0 to 900 days. A three-dimensional numerical model has been implemented using a static implicit finite element analysis along with a user-defined material (UMAT) subroutine. Puck’s failure criterion was used for ultimate failure analysis of the laminates, while Fick’s first diffusion law was used to predict the seawater absorption rate. Overall, the simulated stress–strain curves were close to those obtained experimentally. Moreover, the model agreed well with the experimental data regarding the maximum stress and the strain at failure leading to maximum errors lower than 9% and 11%, respectively. Additionally, the simulated strain fields agreed well with the experimental results measured by digital image correlation. Finally, the proposed procedure was also used to identify the most critical surfaces to protect the mechanical components from marine environments.
Optimization of Antibacterial, Physical and Mechanical Properties of Novel Chitosan/Olibanum Gum Film for Food Packaging Application
Authors: Salavati Hamedani Maedeh , Rezaeigolestani Mohammadreza , Mohsenzadeh Mohammad
Published: September 2022

Chitosan-based films are promising active biodegradable materials with the ability to be enhanced by different materials, including gums. This study aims to optimize the physical (transmittance, water vapor permeability and water solubility), mechanical (tensile strength and elongation at break) and antibacterial (against Staphylococcus aureus and Salmonella Typhimurium) properties of newly fabricated chitosan/olibanum gum (CH/OG) films as a function of different levels of CH (0.5, 0.75, 1, 1.25 and 1.5% w/v) and OG (0.125, 0.25, 0.375, 0.5 and 0.625% w/v), using response surface methodology (RSM). Moreover, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and differential scanning calorimetry (DSC) were used to better characterize the fabricated films. RSM analysis results showed the significant fitting of all dependent variable responses to the quadratic polynomial model. To attain the desirable physical, mechanical and antibacterial responses, the optimal concentrations were 1.31% (w/v) CH and 0.3% (w/v) OG. The encouraging antibacterial, physical and mechanical properties of the developed composites support the application of chitosan/gum blends in active food packaging, particularly for perishable foodstuffs, such as meat and horticultural products.
Mechanical Performance and Constitutive Model Analysis of Concrete Using PE Fiber-Strengthened Recycled Coarse Aggregate
Authors: Zhou Qimi , Zhou Yingwu , Guan Zhipei , Xing Feng , Guo Menghuan , Hu Biao
Published: September 2022

To promote the sustainable development of the construction industry, concrete incorporating polyethylene (PE) fiber-strengthened recycled coarse aggregate (SRCA) and seawater and sea sand (SWSS) is prepared. The usage of SRCA significantly improves the mechanical performance of concrete. The strength is improved, and the failure mode of concrete cylinders is also remarkably altered. The incorporation of SWSS that alleviates the shortage of freshwater and river sand slightly reduces the mechanical strength of concrete at 28 and 90 days, while the replacement of cement by 35% limestone calcined clay cement (LC3) overcomes this drawback. The compressive strength of concrete is further enhanced, and the pore structure is refined. The introduction of LC3 also promotes the formation of Friedel’s salt, which could improve the chloride binding capacity of concrete using SWSS. Furthermore, the stress-strain relationship of sustainable concrete is analyzed, and the experimental results are compared with the commonly used constitutive models. The predictive constitutive models are proposed to effectively describe the mechanical performance of sustainable concrete.
Ionic Push–Pull Polythiophenes: A Further Step towards Eco-Friendly BHJ Organic Solar Cells
Authors: Marinelli Martina , Lanzi Massimiliano , Pierini Filippo , Ziai Yasamin , Zanelli Alberto , Quadretti Debora , Di Maria Francesca , Salatelli Elisabetta
Published: September 2022

Four new conjugated polymers alternating benzothiadiazole units and thiophene moieties functionalized with ionic phosphonium or sulfonic acid salts in the side chains were synthesized by a postfunctionalization approach of polymeric precursors. The introduction of ionic groups makes the conjugated polymers soluble in water and/or polar solvents, allowing for the fabrication of bulk heterojunction (BHJ) solar cells using environmentally friendly conditions. All polymers were fully characterized by spectroscopic, thermal, electrochemical, X-ray diffraction, scanning electron, and atomic force techniques. BHJ solar cells were obtained from halogen-free solvents (i.e., ethanol and/or anisole) by blending the synthesized ionic push–pull polymers with a serinol-fullerene derivative or an ionic homopolymer acting as electron-acceptor (EA) or electron-donor (ED) counterparts, respectively. The device with the highest optical density and the smoothest surface of the active layer was the best-performing, showing a 4.76% photoconversion efficiency.
Nomad Thought: Using Gregory of Nyssa and Deleuze and Guattari to Deterritorialize Mysticism
Authors: Conty Arianne
Published: September 2022

This article compares the mysticism of 4th-century Church Father Gregory of Nyssa to the nomadology of 20th century philosophers Deleuze and Guattari. In their book A Thousand Plateaus, Deleuze and Guattari returned to the figure of the nomad in order to free multiplicities from the “despotic unity” of modern Enlightenment thought. Though Deleuze and Guattari compare this nomadology to spiritual journeys, they claim that their nomad, unlike the mystic, resists a center, a homecoming, a destination. Yet Gregory of Nyssa, writing before the Church itself became a hegemonic power that would confine truth to a single reified code, described the Christian as a wandering nomad, for whom the path itself is the goal. Contrary to the static vision that would be developed in the onto-theological tradition that would lead Western metaphysics to interpret mysticism as the private experience of union with the divine, Gregory of Nyssa proposes a communal movement “from beginning to beginning” with no end, and no union in sight. By placing the postmodern secular nomad alongside the premodern Christian nomad, this article will draw on similarities between the two in order to accentuate the contemporary relevance of Gregory of Nyssa’s vision of mysticism.
Shamanism and the Manchu Bannermen of the Qing Dynasty
Authors: Jiang Xiaoli
Published: September 2022

With the Qing armies marching into Beijing through the Shanhai Pass, the Manchu bannermen under the Eight Banners system saw great changes in their everyday lives. However, they continued with the practice of shamanic sacrifices. This paper analyzes the “Sacred Books”, genealogies, and other historical materials about shamanic rituals, to place shamanism in the lives of the bannermen of the Qing Dynasty. It explores the relationship between this religion and the lives of Manchu bannermen, their clan organizations, and their livelihoods. The paper helps strengthen our understanding of shamanism that continued to flourish during the Qing Dynasty by playing a vital role in the lives of Manchu bannermen.
Assessing Height Variations in Qinghai-Tibet Plateau from Time-Varying Gravity Data and Hydrological Model
Authors: Shi Tong , Guo Jinyun , Yan Haoming , Chang Xiaotao , Ji Bing , Liu Xin
Published: September 2022

Height variations caused by mass change make an important contribution to the tectonic uplift of the Qinghai-Tibet Plateau (QTP). To study the deformation attributable to hydrological loading and real potential tectonic vertical motion, satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with data from the Global Land Data Assimilation System (GLDAS) and Global Positioning System (GPS) are adopted to estimate height variations in QTP. Based on spherical harmonic function (SHF) and Green’s function (GF), the results show the trend of height variations is unevenly distributed in the spatial domain. The SHF indicated that the rate in the southwest of the QTP is ~1 mm/year, while the northern and eastern show a subtle decreasing trend, which indicates hydrological loading is not the main cause of the uplift observed with GRACE. The maximum annual amplitude of height variations is ~12 mm, reaching the annual maximum around February to March. The average correlation coefficients of SHF, and GF height variations with GPS heights are 0.70 and 0.82, respectively. Based on cross wavelet transform, it is concluded that there are annual signals between the height variations derived from GPS with GRACE (-FO) and GLDAS. Finally, the tectonic vertical motion in the QTP is given by removing the effect of hydrological loading, which shows most GPS stations are uplifted at a rate of 0.06 mm/year–1.97 mm/year.
Water Surface Mapping from Sentinel-1 Imagery Based on Attention-UNet3+: A Case Study of Poyang Lake Region
Authors: Jiang Chaowei , Zhang Hong , Wang Chao , Ge Ji , Wu Fan
Published: September 2022

The mapping of water surfaces is important for water resource and flood monitoring. Synthetic Aperture Radar (SAR) images can be used to monitor water bodies and detect floods over large areas. To address the problem of low identification accuracy in different time phases and different scales of water area, a water surface mapping method based on Attention-UNet3+ with SAR images is proposed in this paper. In the model, full-scale skip connections are introduced for combining feature maps from different scales and improving the accuracy of narrow water identification; the spatial attention module is used to obtain the importance of each connected feature, which can reduce the number of false alarms caused by speckle noise and water shadows in SAR data; the deep supervision module is used to learn hierarchical representative features from comprehensive aggregated feature maps to provide the periodic output capability of the model and meet the needs of rapid and large-scale water identification. The effectiveness of Attention-UNet3+ is verified by experiments in the Poyang Lake region with Sentinel-1 SAR images. The results show that the proposed Attention-UNet3+ outperforms the conventional threshold segmentation and deep learning models such as UNet, Deepvlabv3+, and SegNet, with an average IOU/Kappa value of 0.9502/0.9698. Multitemporal Sentinel-1 images in 2021 covering Poyang Lake are used for time series water surface mapping with the proposed method, and it is found that the detected water area of Poyang Lake has a good correlation with the corresponding water level values at observation stations. The Pearson coefficients are about 0.96. The above results indicate that the proposed method achieves good water surface mapping performance.
Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
Authors: Li Jiahao , Xu Ming , Xie Yizhuang , Chen He
Published: September 2022

With the development of spaceborne processing technologies, the demand for on-board processing has risen sharply. Against this background, spaceborne Interferometric Synthetic Aperture Radar (InSAR) processing has become an important research area. In many cases, high processing capacity is required during on-board InSAR processing, yet Field-Programmable Gate Array (FPGA) resources on the satellites are limited. To improve the performance of spaceborne remote sensing processing, this paper designs a high-performing FPGA system for the coarse registration and interferogram generation process of InSAR. Moreover, to address this dual-constraint problem of resource and processing capacity, the paper proposes an FPGA design method based on the gradient descent theory, which can identify the optimum trade-off scheme between two such constraints. Finally, the proposed system design and method are implemented in FPGA. Experiments showed that the FPGA system outperformed the NVIDIA (Santa Clara, CA, USA) GTX Titan Black Graphics Processing Unit (GPU), and the optimum trade-off scheme only increases the entire time by 1.1% but reduces the FPGA BRAM usage by 8.7%. The experimental results proved the effectiveness and validity of the proposed system and method.
Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
Authors: Kurbanov Eldar , Vorobev Oleg , Lezhnin Sergey , Sha Jinming , Wang Jinliang , Li Xiaomei , Cole Janine , Dergunov Denis , Wang Yibo
Published: September 2022

Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.
Virtual Laser Scanning Approach to Assessing Impact of Geometric Inaccuracy on 3D Plant Traits
Authors: Henke Michael , Gladilin Evgeny
Published: September 2022

In recent years, 3D imaging became an increasingly popular screening modality for high-throughput plant phenotyping. The 3D scans provide a rich source of information about architectural plant organization which cannot always be derived from multi-view projection 2D images. On the other hand, 3D scanning is associated with a principle inaccuracy by assessment of geometrically complex plant structures, for example, due the loss of geometrical information on reflective, shadowed, inclined and/or curved leaf surfaces. Here, we aim to quantitatively assess the impact of geometrical inaccuracies in 3D plant data on phenotypic descriptors of four different shoot architectures, including tomato, maize, cucumber, and arabidopsis. For this purpose, virtual laser scanning of synthetic models of these four plant species was used. This approach was applied to simulate different scenarios of 3D model perturbation, as well as the principle loss of geometrical information in shadowed plant regions. Our experimental results show that different plant traits exhibit different and, in general, plant type specific dependency on the level of geometrical perturbations. However, some phenotypic traits are tendentially more or less correlated with the degree of geometrical inaccuracies in assessing 3D plant architecture. In particular, integrative traits, such as plant area, volume, and physiologically important light absorption show stronger correlation with the effectively visible plant area than linear shoot traits, such as total plant height and width crossover different scenarios of geometrical perturbation. Our study addresses an important question of reliability and accuracy of 3D plant measurements and provides solution suggestions for consistent quantitative analysis and interpretation of imperfect data by combining measurement results with computational simulation of synthetic plant models.
Estimating Regional Snow Line Elevation Using Public Webcam Images
Authors: Portenier Céline , Hasler Martina , Wunderle Stefan
Published: September 2022

Snow cover is of high relevance for the Earth’s climate system, and its variability plays a key role in alpine hydrology, ecology, and socioeconomic systems. Measurements obtained by optical satellite remote sensing are an essential source for quantifying snow cover variability from a local to global scale. However, the temporal resolution of such measurements is often affected by persistent cloud coverage, limiting the application of high resolution snow cover mapping. In this study, we derive the regional snow line elevation in an alpine catchment area using public webcams. We compare our results to the snow line information derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 snow cover products and find our results to be in good agreement therewith. Between October 2017 and the end of June 2018, snow lines derived from webcams lie on average 55.8 m below and 33.7 m above MODIS snow lines using a normalized-difference snow index (NDSI) of 0.4 and 0.1, respectively, and are on average 53.1 m below snow lines derived from Sentinel-2. We further analyze the superior temporal resolution of webcam-based snow cover information and demonstrate its effectiveness in filling temporal gaps in satellite-based measurements caused by cloud cover. Our findings show the ability of webcam-based snow line elevation retrieval to complement and improve satellite-based measurements.
Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer
Authors: Ma Chen , Jiang Junjun , Li Huayi , Mei Xiaoguang , Bai Chengchao
Published: September 2022

Hyperspectral images (HSIs) contain spatially structured information and pixel-level sequential spectral attributes. The continuous spectral features contain hundreds of wavelength bands and the differences between spectra are essential for achieving fine-grained classification. Due to the limited receptive field of backbone networks, convolutional neural networks (CNNs)-based HSI classification methods show limitations in modeling spectral-wise long-range dependencies with fixed kernel size and a limited number of layers. Recently, the self-attention mechanism of transformer framework is introduced to compensate for the limitations of CNNs and to mine the long-term dependencies of spectral signatures. Therefore, many joint CNN and Transformer architectures for HSI classification have been proposed to obtain the merits of both networks. However, these architectures make it difficult to capture spatial–spectral correlation and CNNs distort the continuous nature of the spectral signature because of the over-focus on spatial information, which means that the transformer can easily encounter bottlenecks in modeling spectral-wise similarity and long-range dependencies. To address this problem, we propose a neighborhood enhancement hybrid transformer (NEHT) network. In particular, a simple 2D convolution module is adopted to achieve dimensionality reduction while minimizing the distortion of the original spectral distribution by stacked CNNs. Then, we extract group-wise spatial–spectral features in a parallel design to enhance the representation capability of each token. Furthermore, a feature fusion strategy is introduced to increase subtle discrepancies of spectra. Finally, the self-attention of transformer is employed to mine the long-term dependencies between the enhanced feature sequences. Extensive experiments are performed on three well-known datasets and the proposed NEHT network shows superiority over state-of-the-art (SOTA) methods. Specifically, our proposed method outperforms the SOTA method by 0.46%, 1.05% and 0.75% on average in overall accuracy, average accuracy and kappa coefficient metrics.
Estimation of Vegetation Leaf-Area-Index Dynamics from Multiple Satellite Products through Deep-Learning Method
Authors: Liu Tian , Jin Huaan , Li Ainong , Fang Hongliang , Wei Dandan , Xie Xinyao , Nan Xi
Published: September 2022

A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTMfusion LAI provided the best performance (R2 = 0.83, RMSE = 0.82) when compared to LSTMGLASS (R2 = 0.79, RMSE = 0.93), LSTMMODIS (R2 = 0.78, RMSE = 1.25), LSTMVIIRS (R2 = 0.70, RMSE = 0.94), GLASS (R2 = 0.68, RMSE = 1.05), MODIS (R2 = 0.26, RMSE = 1.75), VIIRS (R2 = 0.44, RMSE = 1.37) and DLF LAI (R2 = 0.67, RMSE = 0.98). A temporal comparison among LSTMfusion and three LAI products demonstrated that the LSTMfusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTMfusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI.
Comparison of Satellite Precipitation Products: IMERG and GSMaP with Rain Gauge Observations in Northern China
Authors: Zhu Huiqin , Chen Sheng , Li Zhi , Gao Liang , Li Xiaoyu
Published: September 2022

Extreme precipitation events have increasingly happened at global and regional scales as the global climate has changed in recent decades. Accurate quantitative precipitation estimation (QPE) plays an important role in the warning of extreme precipitation events. With hourly rain gauge observations as a reference, this study compares the performance of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) quantitative precipitation estimation (QPE) products over Northern China in 2021. The Probability of Detection (POD), Relative Bias (RB), Root-Mean-Squared Error (RMSE), and Fractional Standard Error (FSE) are among the assessment metrics, as are the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). We examined the spatial distribution of cumulative precipitation and the temporal distribution of hourly average precipitation for three severe precipitation occurrences using these assessment metrics. The IMERG products capture strong precipitation centers that are compatible with the gauge observations, especially in extreme precipitation events in areas with relatively flat terrain and low-altitude (≤1000 m). Both IMERG (National Aeronautics and Space Administration, NASA) and GSMaP (Japan Aerospace Exploration Agency, JAXA) satellite-based QPE products have precipitation peaks in advance (2–4 h) and generally underestimate (overestimate) precipitation when the actual precipitation is heavy (light). The satellite-based QPE products generally overestimate the heavy rainfall caused by non-typhoons and underestimate the heavy rainfall caused by typhoons. The GSMaP products may have the capacity to detect short-term rainstorm events. The accuracy of satellite-based QPE products may be influenced by precipitation intensity, sensors, terrain, and other variables. Therefore, in accordance with our recommendations, more ground rainfall stations should be used to collect actual precipitation data in regions with high levels of spatial heterogeneity and complex topography. The data programmers should strengthen the weights computation retrieval technique and fully utilize infrared (IR)-based data. Furthermore, this study is expected to give helpful feedback to the algorithm developers of IMERG and GSMaP products, as well as those researchers into the use of IMERG and GSMaP satellite-based QPE products in applications.
Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
Authors: La Salvia Marco , Torti Emanuele , Leon Raquel , Fabelo Himar , Ortega Samuel , Balea-Fernandez Francisco , Martinez-Vega Beatriz , Castaño Irene , Almeida Pablo , Carretero Gregorio , Hernandez Javier A. , Callico Gustavo M. , Leporati Francesco
Published: September 2022

Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints.
Coaxial Mach–Zehnder Digital Strain Sensor Made from a Tapered Depressed Cladding Fiber
Authors: Celaschi Sergio , Grégoire Nicolas , Messaddeq Younès , Biazoli Claudecir R. , Malheiros-Silveira Gilliard N.
Published: September 2022

An in-line digital optical sensor was proposed. It was built from a tapered depressed-cladding single-mode fiber and modeled as a coaxial Mach–Zehnder interferometer. The principle of operation of the optical digital sensor is based on the computation of the number of optical power transfer turning points (PTTP) from the transmission data of the component. Biconic tapers with high values of PTTP, high spectral resolution, high extinction ratio, and low insertion loss were modeled, fabricated, and characterized. As a proof of concept, an in-line digital strain sensor was fabricated and characterized. It presents a free spectral range of 1.3 nm, and produced 96 PTTP, at λ0 = 1.55 μm, under stretch of ΔL = 707 µm, therefore producing a digital resolution of 7.4 µm/PTTP. The sensor also produced a quasi-symmetric response to stretch and compression.
Benchmarking of Various LiDAR Sensors for Use in Self-Driving Vehicles in Real-World Environments
Authors: Schulte-Tigges Joschua , Förster Marco , Nikolovski Gjorgji , Reke Michael , Ferrein Alexander , Kaszner Daniel , Matheis Dominik , Walter Thomas
Published: September 2022

In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.
Intrinsic Performance of Monte Carlo Calibration-Free Algorithm for Laser-Induced Breakdown Spectroscopy
Authors: Gornushkin Igor B. , Völker Tobias
Published: September 2022

The performance of the Monte Carlo (MC) algorithm for calibration-free LIBS was studied on the example of a simulated spectrum that mimics a metallurgical slag sample. The underlying model is that of a uniform, isothermal, and stationary plasma in local thermodynamical equilibrium. Based on the model, the algorithm generates from hundreds of thousands to several millions of simultaneous configurations of plasma parameters and the corresponding number of spectra. The parameters are temperature, plasma size, and concentrations of species. They are iterated until a cost function, which indicates a difference between synthetic and simulated slag spectra, reaches its minimum. After finding the minimum, the concentrations of species are read from the model and compared to the certified values. The algorithm is parallelized on a graphical processing unit (GPU) to reduce computational time. The minimization of the cost function takes several minutes on the GPU NVIDIA Tesla K40 card and depends on the number of elements to be iterated. The intrinsic accuracy of the MC calibration-free method is found to be around 1% for the eight elements tested. For a real experimental spectrum, however, the efficiency may turn out to be worse due to the idealistic nature of the model, as well as incorrectly chosen experimental conditions. Factors influencing the performance of the method are discussed.
Acoustic Source Tracking Based on Probabilistic Data Association and Distributed Cubature Kalman Filtering in Acoustic Sensor Networks
Authors: Chen Yang , Cao Yideng , Wang Rui
Published: September 2022

A probabilistic data association-based distributed cubature Kalman filter (PDA-DCKF) method is proposed in this paper, whose performance on tracking single moving sound sources in the distributed acoustic sensor network was verified. In this method, the PDA algorithm is first used to sift the observations from neighboring nodes. Then, the sifted observations are fused to update the state vectors in the CKF. Since nodes in a sensor network have different reliabilities, the final tracking result integrates the estimations from the local nodes, which are weighted with the parameters depending on the mean square error of the estimation and the energy of the received signal. The experimental results illustrated that the proposed PDA-DCKF method is superior to the other DCKF methods in tracking sound sources even under severe noise and reverberant conditions.
A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism
Authors: Ashfaq Tehreem , Khalid Rabiya , Yahaya Adamu Sani , Aslam Sheraz , Azar Ahmad Taher , Alsafari Safa , Hameed Ibrahim A.
Published: September 2022

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.
Probe Contact Force Monitoring during Conductivity Measurements of the Left Atrial Appendage to Support the Design of Novel Diagnostic and Therapeutic Procedures
Authors: Benchakroun Hamza , Ištuk Niko , Dunne Eoghan , Elahi Muhammad Adnan , O’Halloran Tony , O’Halloran Martin , O’Loughlin Declan
Published: September 2022

The electrical properties of many biological tissues are freely available from the INRC and the IT’IS databases. However, particularly in lower frequency ranges, few studies have investigated the optimal measurement protocol or the key confounders that need to be controlled, monitored, and reported. However, preliminary work suggests that the contact force of the measurement probe on the tissue sample can affect the measurements. The aim of this paper is to investigate the conductivity change due to the probe contact force in detail. Twenty ex vivo bovine heart samples are used, and conductivity measurements are taken in the Left Atrial Appendage, a common target for medical device developments. The conductivity measurements reported in this work (between 0.14 S/m and 0.24 S/m) align with the literature. The average conductivity is observed to change by −21% as the contact force increases from 2 N to 10 N. In contrast, in conditions where the fluid concentration in the measurement area is expected to be lower, very small changes are observed (less than 2.5%). These results suggest that the LAA conductivity is affected by the contact force due to the fluid concentration in the tissue. This work suggests that contact force should be controlled for in all future experiments.
Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study
Authors: Choi Jae Won , Kim Dong Hyun , Koo Dae Lim , Park Yangmi , Nam Hyunwoo , Lee Ji Hyun , Kim Hyo Jin , Hong Seung-No , Jang Gwangsoo , Lim Sungmook , Kim Baekhyun
Published: September 2022

Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave radar using convolutional recurrent neural networks. The dataset comprised 44 participants from an ongoing OSA cohort, recruited from July 2021 to April 2022, who underwent overnight PSG with a radar sensor. All PSG recordings, including sleep and wakefulness, were included in the dataset. Model development and evaluation were based on a five-fold cross-validation. The area under the receiver operating characteristic curve for the classification of 1-min segments ranged from 0.796 to 0.859. Depending on OSA severity, the sensitivities for apnea-hypopnea events were 49.0–67.6%, and the number of false-positive detections per participant was 23.4–52.8. The estimated apnea-hypopnea index showed strong correlations (Pearson correlation coefficient = 0.805–0.949) and good to excellent agreement (intraclass correlation coefficient = 0.776–0.929) with the ground truth. There was substantial agreement between the estimated and ground truth OSA severity (kappa statistics = 0.648–0.736). The results demonstrate the potential of radar as a standalone screening tool for OSA.
The Automatization of the Gait Analysis by the Vicon Video System: A Pilot Study
Authors: Smirnova Victoriya , Khamatnurova Regina , Kharin Nikita , Yaikova Elena , Baltina Tatiana , Sachenkov Oskar
Published: September 2022

The quality of modern measuring instruments has a strong influence on the speed of diagnosing diseases of the human musculoskeletal system. The research is focused on automatization of the method of gait analysis. The study involved six healthy subjects. The subjects walk straight. Each subject made several gait types: casual walking and imitation of a non-standard gait, including shuffling, lameness, clubfoot, walking from the heel, rolling from heel to toe, walking with hands in pockets, and catwalk. Each type of gait was recorded three times. For video fixation, the Vicon Nexus system was used. A total of 27 reflective markers were placed on the special anatomical regions. The goniometry methods were used. The walk data were divided by steps and by step phases. Kinematic parameters for estimation were formulated and calculated. An approach for data clusterization is presented. For this purpose, angle data were interpolated and the interpolation coefficients were used for clustering the data. The data were processed and four cluster groups were found. Typical angulograms for cluster groups were presented. For each group, average angles were calculated. A statistically significant difference was found between received cluster groups.
Physics-Informed Data-Driven Prediction of 2D Normal Strain Field in Concrete Structures
Authors: Pereira Mauricio , Glisic Branko
Published: September 2022

Concrete exhibits time-dependent long-term behavior driven by creep and shrinkage. These rheological effects are difficult to predict due to their stochastic nature and dependence on loading history. Existing empirical models used to predict rheological effects are fitted to databases composed largely of laboratory tests of limited time span and that do not capture differential rheological effects. A numerical model is typically required for application of empirical constitutive models to real structures. Notwithstanding this, the optimal parameters for the laboratory databases are not necessarily ideal for a specific structure. Data-driven approaches using structural health monitoring data have shown promise towards accurate prediction of long-term time-dependent behavior in concrete structures, but current approaches require different model parameters for each sensor and do not leverage geometry and loading. In this work, a physics-informed data-driven approach for long-term prediction of 2D normal strain field in prestressed concrete structures is introduced. The method employs a simplified analytical model of the structure, a data-driven model for prediction of the temperature field, and embedding of neural networks into rheological time-functions. In contrast to previous approaches, the model is trained on multiple sensors at once and enables the estimation of the strain evolution at any point of interest in the longitudinal section of the structure, capturing differential rheological effects.
Lightweight Anonymous Authentication and Key Agreement Protocol Based on CoAP of Internet of Things
Authors: Gong Xiang , Feng Tao
Published: September 2022

To solve the problem regarding the lack of a lightweight and secure authentication and key agreement protocol in the Constrained Application Protocol of the Internet of Things environment, we explore the security flaws and applicability problems in the current related research. Then, we propose a new lightweight authentication and key agreement protocol based on the CoAP framework. The scheme adopts shared secret and elliptic curve public key technology, which ensures the anonymity of the communicators and provides strong security and anti-attack capacity. In terms of security analysis, the Dolev–Yao Adversary model and a security model checking analysis method based on CPN Tools are improved, in order to verify the correctness and security of the proposed scheme. Compared with other schemes, regarding communication overhead, computational cost, and security, the proposed scheme provides a robust and comprehensive security guarantee, although it is not the lightest.
Data Analytics, Self-Organization, and Security Provisioning for Smart Monitoring Systems
Authors: Anwar Raja Waseem , Qureshi Kashif Naseer , Nagmeldin Wamda , Abdelmaboud Abdelzahir , Ghafoor Kayhan Zrar , Javed Ibrahim Tariq , Crespi Noel
Published: September 2022

Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new area has suffered from big data handling and security issues. There is a need to design a data analytics model by using new 5G technologies, architecture, and a security model. Reliable data communication in the presence of legitimate nodes is always one of the challenges in these networks. Malicious nodes are generating inaccurate information and breach the user’s security. In this paper, a data analytics model and self-organizing architecture for IoT networks are proposed to understand the different layers of technologies and processes. The proposed model is designed for smart environmental monitoring systems. This paper also proposes a security model based on an authentication, detection, and prediction mechanism for IoT networks. The proposed model enhances security and protects the network from DoS and DDoS attacks. The proposed model evaluates in terms of accuracy, sensitivity, and specificity by using machine learning algorithms.
Effect of Various Edge Configurations on the Accuracy of the Modelling Shape of Shell Structures Using Spline Functions
Authors: Lenda Grzegorz , Marmol Urszula
Published: September 2022

Spline functions are a useful tool for modelling the shape of shell structures. They have curvature continuity that allows good approximation accuracy for various objects, including hyperboloid cooling towers, spherical domes, paraboloid bowls of radio telescopes, or many other types of smooth free surfaces. Spline models can be used to determine the displacement of structures based on point clouds from laser scanning or photogrammetry. The curvature continuity of splines may, however, cause local distortions in models that have edges. Edges may appear in point clouds where surface patches are joined, on surfaces equipped with additional technical infrastructure or with cracks and shifts in the structure. Taking the properties of spline functions into account, several characteristic types of edge configurations can be distinguished, which may, to a different extent, affect the values of modelling errors. The research conducted below was aimed at identifying such configurations based on theoretical considerations and then assessing their effect on the accuracy of modelling shell structures measured by laser scanning. It turned out to be possible to distinguish between edge configurations, based on the deviation values.
Calcium Biofortification of Rocha Pear Fruits: Implications on Mineral Elements, Sugars and Fatty Acids Accumulation in Tissues
Authors: Pessoa Cláudia Campos , Lidon Fernando C. , Daccak Diana , Luís Inês Carmo , Marques Ana Coelho , Coelho Ana Rita F. , Legoinha Paulo , Ramalho José Cochicho , Leitão António E. , Guerra Mauro , Leitão Roberta G. , Campos Paula Scotti , Pais Isabel P. , Silva Maria Manuela , Reboredo Fernando H. , Pessoa Maria Fernanda , Simões Manuela
Published: September 2022

Following an agronomic approach for the Ca enrichment of Rocha pears, this study aimed to assess the interactions between mineral nutrients in fruit tissues at harvest and after storage for 5 months and to characterize the implications on the profile of sugars and fatty acids (FA). A total of seven foliar sprays (with concentrations of 0.1–0.6 kg·ha−1 Ca(NO3)2 and 0.8–8 kg·ha−1 CaCl2) were applied to pear trees. After harvest, the fruits were stored for 5 months, in environmentally controlled chambers, and the mineral contents in five regions (on the equatorial section) of the fruits were assessed, while the sugar and FA content were quantified. For both dates, all foliar sprayed treatments, at different extends, increased Ca content in the center and near the epidermis of Rocha pear fruits and the levels of K, Mn, Fe, Zn and Cu also varied. At harvest, the Ca treatments did not affect the levels of sucrose, glucose, fructose and sorbitol and, after storage, their concentrations remained higher in Ca-treated fruits. Additionally, the tendency of the relative proportions of FA was C18:2 > C18:1 > C16:0 > C18:3 > C18:0 > chains inferior to 16 C (<16:0), but after storage it was C18:2 > C16:0 > C18:3 > C18:0 > C18:1 > chains inferior to 16 C (<16:0). It is concluded that the heterogeneous distribution of Ca in the tissues of Rocha pear fruits results from its absorption in the peel after Ca(NO3)2 and CaCl2 sprays and from the xylemic flux in the core prior to maturity. Additionally, the hydrolysis of complex polysaccharides affects the contents of simpler sugars during maturation, ripening and senescence, while storage decreases the amount of total fatty acids (TFA), but the double bond index (DBI) indicate that cell membrane fluidity remains unaffected.