bluuebunny/crossref_metadata_2025 huggingface dataset:
Biotechnological Applications of Nanoencapsulated Essential Oils: A Review
Authors: Albuquerque Patrícia Melchionna , Azevedo Sidney Gomes , de Andrade Cleudiane Pereira , D’Ambros Natália Corrêa de Souza , Pérez Maria Tereza Martins , Manzato Lizandro
Published: December 2022

Essential oils (EOs) are complex mixtures of volatile and semi-volatile organic compounds that originate from different plant tissues, including flowers, buds, leaves and bark. According to their chemical composition, EOs have a characteristic aroma and present a wide spectrum of applications, namely in the food, agricultural, environmental, cosmetic and pharmaceutical sectors. These applications are mainly due to their biological properties. However, EOs are unstable and easily degradable if not protected from external factors such as oxidation, heat and light. Therefore, there is growing interest in the encapsulation of EOs, since polymeric nanocarriers serve as a barrier between the oil and the environment. In this context, nanoencapsulation seems to be an interesting approach as it not only prevents the exposure and degradation of EOs and their bioactive constituents by creating a physical barrier, but it also facilitates their controlled release, thus resulting in greater bioavailability and efficiency. In this review, we focused on selecting recent articles whose objective concerned the nanoencapsulation of essential oils from different plant species and highlighted their chemical constituents and their potential biotechnological applications. We also present the fundamentals of the most commonly used encapsulation methods, and the biopolymer carriers that are suitable for encapsulating EOs.
Flexural Strengthening of Large-Scale RC Beams with Nonprestressed and Prestressed CFRP Using Different Anchorages
Authors: Wang Hai-Tao , Bian Zhi-Ning , Xu Guo-Wen , Chen Min-Sheng , Xiong Hao , Liu Sai-Sai
Published: December 2022

Externally bonded carbon-fiber-reinforced polymer (CFRP) technology can be used by different methods based on the anchorage device, CFRP type, and prestressing/nonprestressing. However, a direct comparison between the strengthening efficacies of different methods is still lacking. Seven large-scale RC beams were tested in this study to investigate the influences of the anchorage method, CFRP type, prestress, and prestressing system on the flexural strengthening efficacy of RC beams. The test results showed that the ultimate load increased by 38.3%, whereas the cracking and yielding loads were slightly affected when the anchorage method was enhanced from CFRP U-wraps to wedge-clamp anchors. The CFRP plate and CFRP sheet could provide a rather close flexural strengthening efficacy under the same CFRP strengthening amount. Compared to the nonprestressed CFRP plate, the prestressed CFRP plate was highly superior in improving the flexural behavior of RC beams. The cracking, yielding, and ultimate loads of the prestressed CFRP-strengthened specimens were 57.1%, 22.9%, and 5.9%, respectively, higher than those of the nonprestressed CFRP-strengthened specimen with an effective anchorage. The two types of prestressing systems based on the adhesive-friction anchor and wedge-clamp anchor were proven to be effective for flexural strengthening of RC beams with prestressed CFRP plates, and they could provide almost the same strengthening effect.
Evaluation of Fossil Amber Birefringence and Inclusions Using Terahertz Time-Domain Spectroscopy
Authors: Clark Alexander T. , D’Anna Sophia , Nemati Jessy , Barden Phillip , Gatley Ian , Federici John
Published: December 2022

Using a cross-polarization transmission geometry, stress maps for the normalized birefringence and intrinsic stress direction of polymeric materials may be obtained using terahertz nondestructive evaluation. The analysis method utilizes a deconvolution method to determine the arrival times and amplitude of the cross-polarized terahertz pulses through a birefringent material. Using amber (a naturally occurring polymer) as a material of interest, stress maps show that inclusion-free Lebanese amber samples behave as classic uniaxial birefringent (photoelastic) materials whose principal stress directions, as inferred in the terahertz spectral range, agree well with visible photoelasticity measurements. Since amber samples, depending upon their source, may be either transparent or opaque to visible light, comparing birefringence measurements in the visible and terahertz spectral ranges cross-validates the stress measurements, thereby establishing a strong and unique stress analysis methodology for visibly opaque samples. While the material of interest for this paper is amber, the method is generally applicable for any terahertz-transparent polymer. The cross-polarization experimental configuration enables stress levels within the amber matrix to be visualized while also outlining highly localized regions of stress surrounding inclusions. Birefringence stress maps clearly show localized increases in stress magnitude and directional changes surrounding inclusions.
Recycling of Bottle Grade PET: Influence of HDPE Contamination on the Microstructure and Mechanical Performance of 3D Printed Parts
Authors: Vaucher Joanne , Demongeot Adrien , Michaud Véronique , Leterrier Yves
Published: December 2022

As part of a project that aims to provide people with disabilities with simple assistive devices in Colombia, the possibility of creating a PET filament that can be printed by Fused Deposition Modelling (FDM) from beverage bottle waste was investigated, with the aim to remain as simple as possible in terms of plastic collection, sorting, processing, and printing. Recycled PET filaments were thus produced by extrusion from collected PET bottles, with the potential addition of HDPE, which comes from caps and rings. The microstructure, mechanical performance, and printing quality of parts produced with these filaments were investigated in comparison to commercial PET virgin and recycled filaments. HDPE presence as an immiscible blend did not affect the ease of extrusion or the quality of the printing, which were all satisfactory. In some conditions, the addition of 5 wt% of HDPE to recycled PET had a toughening effect on otherwise brittle samples. This behavior was attributed to the presence of elongated HDPE inclusions resulting from shear forces induced by the layer-by-layer printing, provided that the interface temperature remained high between layer depositions. This confirms that the mechanical performance of recycled PET is very sensitive to the processing conditions, especially in the case of 3D printing. Nonetheless, this low-cost process that did not require sophisticated compatibilization schemes allowed for the printing of parts with mechanical properties comparable to those obtained with high purity, commercially recycled filaments, opening interesting perspectives for a low-cost PET recycling process.
Supercritical CO2 Assisted TiO2 Preparation to Improve the UV Resistance Properties of Cotton Fiber
Authors: Ye Sihong , Sun Hui , Wu Juan , Wan Lingzhong , Ni Ying , Wang Rui , Xiang Zhouyang , Deng Xiaonan
Published: December 2022

Cotton fiber is favored by people because of its good moisture absorption, heat preservation, soft feel, comfortable wearing and other excellent performance. In recent years, due to the destruction of the ozone layer, the intensity of ultraviolet radiation at ground level has increased. Cotton fiber will degrade under long time ultraviolet irradiation, which limits the outdoor application of cotton fiber. In this study, titanium dioxide (TiO2) particles were prepared on the surface of cotton fibers with the help of supercritical carbon dioxide (SCCO2) to improve the UV resistance of cotton fibers. The effects of SCCO2 treatment on the morphology, surface composition, thermal stability, photostability and mechanical properties of TiO2 were studied by Fourier transform infrared spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, thermogravimetric analysis, UV-VIS spectroscopy, and single fiber test. The results showed that TiO2 particles were generated on the fiber surface, which reduced the photo-degradation rate of cotton fiber. This is because TiO2 can absorb UV rays and reduce the absorption of UV rays by the cotton fiber itself. The synthesis process of SCCO2 is simple and environmentally friendly, which provides a promising technology for the synthesis of metal nitrogen dioxide on natural plant fibers.
Research on Driving Factors of Collaborative Integration Implementation of Lean-Green Manufacturing System with Industry 4.0 Based on Fuzzy AHP-DEMATEL-ISM: From the Perspective of Enterprise Stakeholders
Authors: Zhu Xiaoyong , Xiao Yongmao , Xiao Gongwei , Deng Xiaojuan
Published: December 2022

The existing research and practices have shown that the coordinated implementation of lean-green manufacturing can have a positive impact on the economic and environmental benefits, which is an effective means to ensure the environmental protection of the production process of manufacturing without damaging their profitability. Within the field of lean-green research, there is still a lack of research to analyze the driving factors for the collaborative implementation of integrated lean and green integration. Although, some scholars and researchers have studied lean and green integration paradigms, their research has mostly focused on lean-green integration practices and their impact on environmental performance and their respective operations. In the context of Industry 4.0, this article investigates the driving forces behind the collaborative integration implementation of a lean-green manufacturing system from the viewpoint of stakeholders. Specifically addressing the issues of correlation and ambiguity in the identification of driving factors, this manuscript proposes an Interpretation Structure Model (ISM) of fuzzy comprehensive Analytic Hierarchy Process (AHP), based on Decision-Making Trial and Evaluation Laboratory (DEMATEL), to determine the importance of the driving factors. Combined with the complex network theory, the evaluation index system is divided into four levels from eight factor categories, including endogenous lean-green driving factors and exogenous driving factors. The fuzzy AHP-DEMATEL-ISM is used to analyze the relationship between indicators and the structure of the indicator system. The complex network which is composed of the indicator system is divided into different levels. The importance of indicators is analyzed from the perspective of the global network, and key factors affecting the driving of lean-green system is analyzed. The integration of the lean-green manufacturing system and organizational synergy are promoted to jointly lead the enterprise toward sustainable development by paying particular attention to the primary impact indicators and aggressively cultivating the key impact indicators.
Agglomeration of Spray-Dried Milk Powder in a Spray Fluidized Bed: A Morphological Modeling
Authors: Singh Abhinandan Kumar , Tsotsas Evangelos
Published: December 2022

The type of solid substrate plays a critical role in determining the kinetics of the spray fluidized bed (SFB) agglomeration process. In the case of porous (also soft) primary particles (PPs), droplet aging is due to imbibition and drying. The surface properties of the substrate also change due to imbibition. The focus of the present work is to simulate the agglomeration of the spray-dried milk powder using the Monte Carlo (MC) method coupled with a drying-imbibition model. In order to extract the morphology of the formed agglomerates, an aggregation model is employed. Further, this aggregation model is employed to predict the number of positions on the PPs (later agglomerates) for droplet deposition; previously, the ‘concept of positions’ was used. The transient growth of different milk powders (whole and skim) is depicted using the enhanced MC model. The enhancement in the droplet deposition model had a prominent influence on the overall kinetics of agglomeration. As expected, this enhanced MC model predicted that the agglomeration rate of skim milk powder is higher than that of whole milk powder.
A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case
Authors: Lagasio Martina , Fagugli Giacomo , Ferraris Luca , Fiori Elisabetta , Gabellani Simone , Masi Rocco , Mazzarella Vincenzo , Milelli Massimo , Parodi Andrea , Pignone Flavio , Puca Silvia , Pulvirenti Luca , Silvestro Francesco , Squicciarino Giuseppe , Parodi Antonio
Published: December 2022

Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity.
Analysis of Permafrost Distribution and Change in the Mid-East Qinghai–Tibetan Plateau during 2012–2021 Using the New TLZ Model
Authors: Zhao Zhijian , Tonooka Hideyuki
Published: December 2022

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation–evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai–Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas.
An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention
Authors: Hong Yong , Li Deren , Luo Shupei , Chen Xin , Yang Yi , Wang Mi
Published: December 2022

Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer’s encoder–decoder structure. A multi-dimensional feature extraction backbone network was combined with a self-built raster semantic map which was stored in the encoder for correlation and generated target position encoding and multi-dimensional feature vectors. The decoder incorporated four methods: spatial clustering and semantic filtering of multi-view targets; dynamic matching of multi-dimensional features; space–time logic-based multi-target tracking, and space–time convergence network (STCN)-based parameter passing. Through the fusion of multiple decoding methods, multi-camera targets were tracked in three dimensions: temporal logic, spatial logic, and feature matching. For the MOT17 dataset, this study’s method significantly outperformed the current state-of-the-art method by 2.2% on the multiple object tracking accuracy (MOTA) metric. Furthermore, this study proposed a retrospective mechanism for the first time and adopted a reverse-order processing method to optimize the historical mislabeled targets for improving the identification F1-score (IDF1). For the self-built dataset OVIT-MOT01, the IDF1 improved from 0.948 to 0.967, and the multi-camera tracking accuracy (MCTA) improved from 0.878 to 0.909, which significantly improved the continuous tracking accuracy and reliability.
A Calibrated GPT3 (CGPT3) Model for the Site-Specific Zenith Hydrostatic Delay Estimation in the Chinese Mainland and Its Surrounding Areas
Authors: Li Junyu , Li Feijuan , Liu Lilong , Huang Liangke , Zhou Lv , He Hongchang
Published: December 2022

The prior zenith hydrostatic delay (ZHD) is an essential parameter for the Global Navigation Satellite System (GNSS) and very long baseline interferometry (VLBI) high-precision data processing. Meanwhile, the precise ZHD facilitates the separation of the high-precision zenith wet delay (ZWD) to derive precipitable water vapor (PWV). This paper analyzes the temporal variations in the residuals between GPT3 ZHD and reference ZHD from radiosonde (RS) sites, and a calibrated GPT3 (CGPT3) model is proposed for the site-specific ZHD estimation in the Chinese mainland and its surrounding areas based on the annual, semi-annual, and diurnal variations in residuals. Based on the validation using modeling RS data, the mean absolute error (MAE) and root mean square (RMS) of the CGPT3 model are 7.3 and 9.6 mm, respectively. The validation with RS ZHD not involved in the modeling suggests that the MAE and RMS of the CGPT3 model are 7.9 and 10.2 mm, respectively. These results show improvements of 16.8%/16.8% and 14.3%/13.6%, respectively, compared with the MAE and RMS of the GPT3 model and the newly proposed model (GTrop). In addition, the CGPT3 model has excellent spatial and temporal stability in the study area.
Snowmelt Flood Susceptibility Assessment in Kunlun Mountains Based on the Swin Transformer Deep Learning Method
Authors: Yang Ruibiao , Zheng Guoxiong , Hu Ping , Liu Ying , Xu Wenqiang , Bao Anming
Published: December 2022

Modeling and assessing the susceptibility of snowmelt floods is critical for flood hazard management. However, the current research on snowmelt flood susceptibility lacks a valid large-scale modeling approach. In this study, a novel high-performance deep learning model called Swin Transformer was used to assess snowmelt susceptibility in the Kunlun Mountains region, where snowmelt floods occur frequently. Support vector machine (SVM), random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) were also involved in the performance comparison. Eighteen potential conditioning factors were combined with a historical flood inventory to form the database. Apart from the susceptibility assessment, sensitivity analysis was also conducted to reflect the impact of the conditioning factors on the susceptibility of different types of snowmelt floods. The results showed that Swin Transformer achieved the highest score in the model performance test (AUC = 0.99) and successfully identified the relationship between conditioning factors and snowmelt flooding. Elevation and distance to rivers are the most important factors that affect snowmelt flooding in the study region, whereas rainfall and snow water equivalent are the dominant natural factors for mixed and warming types. In addition, the north-central parts of the study area have high susceptibility to snowmelt flooding. The methods and results can provide scientific support for snowmelt flood modeling and disaster management.
Cascaded U-Net with Training Wheel Attention Module for Change Detection in Satellite Images
Authors: Adil Elyar , Yang Xiangli , Huang Pingping , Liu Xiaolong , Tan Weixian , Yang Jianxi
Published: December 2022

Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder–decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data.
Object Counting in Remote Sensing via Triple Attention and Scale-Aware Network
Authors: Guo Xiangyu , Anisetti Marco , Gao Mingliang , Jeon Gwanggil
Published: December 2022

Object counting is a fundamental task in remote sensing analysis. Nevertheless, it has been barely studied compared with object counting in natural images due to the challenging factors, e.g., background clutter and scale variation. This paper proposes a triple attention and scale-aware network (TASNet). Specifically, a triple view attention (TVA) module is adopted to remedy the background clutter, which executes three-dimension attention operations on the input tensor. In this case, it can capture the interaction dependencies between three dimensions to distinguish the object region. Meanwhile, a pyramid feature aggregation (PFA) module is employed to relieve the scale variation. The PFA module is built in a four-branch architecture, and each branch has a similar structure composed of dilated convolution layers to enlarge the receptive field. Furthermore, a scale transmit connection is introduced to enable the lower branch to acquire the upper branch’s scale, increasing the output’s scale diversity. Experimental results on remote sensing datasets prove that the proposed model can address the issues of background clutter and scale variation. Moreover, it outperforms the state-of-the-art (SOTA) competitors subjectively and objectively.
Self-Sensing Antenna for Soil Moisture: Beacon Approach
Authors: Škiljo Maja , Blažević Zoran , Dujić-Rodić Lea , Perković Toni , Šolić Petar
Published: December 2022

On the way from the Internet of things (IoT) to the Internet of underground things (IoUT) the main challenge is antenna design. The enabling technologies still rely on simple design and low cost, but the systems are more complex. The LoRa-based system combined with a machine learning approach can be used for the estimation of soil moisture by using signal strength data, but for the improvement of the system performance we propose the optimization of the antenna for underground use. The soil properties are frequency-dependent and varying in time, which may cause variations in the signal wavelength and input impedance of the antenna underground. Instead of using wideband antenna design or standard helical antenna provided in LoRa module, which are typical in the IoUT research community for communication links, we propose a narrow-band antenna design for the application in soil moisture sensing. It is shown that the approach of simply matching the antenna buried in dry sand can provide a substantial signal level difference, ranging from approximately 10 dB (achieved by proof-of-concept measurements) to as much as 40 dB (calculated by a full wave simulator) in reflection coefficient when the moisture content is being increased by 20%. This can ensure more reliable radio sensing in novel sensorless technology where soil moisture information is extracted from the signal strength of a transmitting device.
XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification
Authors: Abbas Asmaa , Gaber Mohamed Medhat , Abdelsamea Mohammed M.
Published: December 2022

Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations.
Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans
Authors: Usman Muhammad , Rehman Azka , Saleem Amal Muhammad , Jawaid Rabeea , Byon Shi-Sub , Kim Sung-Hyun , Lee Byoung-Dai , Heo Min-Suk , Shin Yeong-Gil
Published: December 2022

Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts manually determine the implant position and dimensions from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. In particular, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we designed 3D deeply supervised attention UNet architecture for localizing the Volumes Of Interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the Multi-Scale input Residual UNet (MSiR-UNet) architecture to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 and 15 CBCT scans from our dataset and from the public dataset, respectively. The results demonstrate that our technique improves the existing performance of mandibular canal segmentation to a clinically acceptable range. Moreover, it is robust against the types of CBCT scans in terms of field of view.
Experimental Validation of Shifted Position-Diffuse Reflectance Imaging (SP-DRI) on Optical Phantoms
Authors: Späth Moritz , Romboy Alexander , Nzenwata Ijeoma , Rohde Maximilian , Ni Dongqin , Ackermann Lisa , Stelzle Florian , Hohmann Martin , Klämpfl Florian
Published: December 2022

Numerous diseases such as hemorrhage, sepsis or cardiogenic shock induce a heterogeneous perfusion of the capillaries. To detect such alterations in the human blood flow pattern, diagnostic devices must provide an appropriately high spatial resolution. Shifted position-diffuse reflectance imaging (SP-DRI) has the potential to do so; it is an all-optical diagnostic technique. So far, SP-DRI has mainly been developed using Monte Carlo simulations. The present study is therefore validating this algorithm experimentally on realistic optical phantoms with thread structures down to 10 μm in diameter; a SP-DRI sensor prototype was developed and realized by means of additive manufacturing. SP-DRI turned out to be functional within this experimental framework. The position of the structures within the optical phantoms become clearly visible using SP-DRI, and the structure thickness is reflected as modulation in the SP-DRI signal amplitude; this performed well for a shift along the x axis as well as along the y axis. Moreover, SP-DRI successfully masked the pronounced influence of the illumination cone on the data. The algorithm showed significantly superior to a mere raw data inspection. Within the scope of the study, the constructive design of the SP-DRI sensor prototype is discussed and potential for improvement is explored.
Learning Geometric Feature Embedding with Transformers for Image Matching
Authors: Nan Xiaohu , Ding Lei
Published: December 2022

Local feature matching is a part of many large vision tasks. Local feature matching usually consists of three parts: feature detection, description, and matching. The matching task usually serves a downstream task, such as camera pose estimation, so geometric information is crucial for the matching task. We propose the geometric feature embedding matching method (GFM) for local feature matching. We propose the adaptive keypoint geometric embedding module dynamic adjust keypoint position information and the orientation geometric embedding displayed modeling of geometric information about rotation. Subsequently, we interleave the use of self-attention and cross-attention for local feature enhancement. The predicted correspondences are multiplied by the local features. The correspondences are solved by computing dual-softmax. An intuitive human extraction and matching scheme is implemented. In order to verify the effectiveness of our proposed method, we performed validation on three datasets (MegaDepth, Hpatches, Aachen Day-Night v1.1) according to their respective metrics, and the results showed that our method achieved satisfactory results in all scenes.
Validity of a Smartphone Application in Calculating Measures of Heart Rate Variability
Authors: Himariotis Andreas T. , Coffey Kyle F. , Noel Sabrina E. , Cornell David J.
Published: December 2022

The purpose of the current study was to determine the concurrent validity of the Elite HRV smartphone application when calculating heart rate variability (HRV) metrics in reference to an independent software criterion. A total of 5 minutes of R–R interval and natural log of root mean square of the successive differences (lnRMSSD) resting HRV data were simultaneously collected using two Polar H10 heart rate monitors (HRMs) in both the seated and supine positions from 22 participants (14 males, 8 females). One H10 HRM was paired with a Polar V800 watch and one with the Elite HRV application. When no artifact correction was applied, significant, but small, differences in the lnRMSSD data were observed between the software in the seated position (p = 0.022), and trivial and nonstatistically significant differences were observed in the supine position (p = 0.087). However, significant differences (p > 0.05) in the lnRMSSD data were no longer identifiable in either the seated or the supine positions when applying Very Low, Low, or Automatic artifact-correction filters. Additionally, excellent agreements (ICC3,1 = 0.938 − 0.998) and very strong to near-perfect (r = 0.889 − 0.997) relationships were observed throughout all correction levels. The Elite HRV smartphone application is a valid tool for calculating resting lnRMSSD HRV metrics.
Effect of Spraying Polyurea on the Anti-Blast Performance of the Ultra-High Performance Concrete Slab
Authors: Gao Bin , Wu Jun , Chen Qinyi , Yu Jun , Yu Haitao
Published: December 2022

Recently, polyurea has been applied to improve the anti-blast performance of metal plates, masonry walls, and concrete structures. However, the strengthening effectiveness of polyurea on ultra-high performance concrete (UHPC) slabs with an overall response is still unclear. Hence, this paper examined the strengthening effectiveness of polyurea on the anti-blast performance of UHPC slabs under near-field explosion by the finite element (FE) method. First, a benchmark finite element model for UHPC and polyurea-UHPC (PUHPC) slabs under blast loading was established and validated by field blast tests, with scaled distances ranging from 0.4 m/kg1/3 to 0.8 m/kg1/3. After that, parametric analysis was conducted to fully understand the strengthening effectiveness of polyurea on the anti-blast performance of the UHPC slab. Factors including the scaled distance, polyurea thickness, span-to-depth ratio of the slab, and longitudinal reinforcement ratio were considered. The results showed that (1) spraying polyurea on the rear face of the UHPC slab can reduce the width of cracks and mitigate the damage of specimens; (2) the strengthening effectiveness of polyurea on the UHPC slab became prominent when the UHPC slab suffered a larger maximum deflection; (3) in terms of the deflection and energy absorption capacity of PUHPC slabs, the optimum thickness of sprayed polyurea was determined to be 8 mm to 12 mm; and (4) by adopting the multiple nonlinear regression method, a prediction formula was developed to quickly obtain the end rotation of the UHPC slab strengthened with polyurea under near-field explosions.
Classification of Gravity Matching Areas Using PSO-BP Neural Networks based on PCA and Satellite Altimetry Data over the Western Pacific
Authors: Zong Jingwen , Bian Shaofeng , Tong Yude , Ji Bing , Li Houpu , Xi Menghan
Published: December 2022

For inertial navigation systems (INS), as one of the major methods for underwater navigation, errors diverge over time. With the development of geophysical navigation technology, gravity navigation has become an effective method of navigation. Significant changes in the gravity characteristic of the matching region ensure that gravity matching navigation works effectively. In this paper, we combine artificial intelligence algorithms and statistical metrics to classify gravity-matching navigation regions. Firstly, this paper analyzes and extracts gravity anomaly data from a matching region in different ways. Then, a particle swarm optimization (PSO) algorithm is used to optimize the network weights of a back propagation (BP) NN. Finally, based on principal component analysis (PCA) theory and PSO-BP NN, this paper proposes the PPBA method to classify the matching area. Moreover, the Terrain Contour Matching (TERCOM) matching algorithm and gravity anomaly data from the Western Pacific are used to verify the classification performance of the PPBA method. The experiments prove that the PPBA method has a high classification accuracy, and the classification results are consistent with the matching navigation experimental results. This work can provide a reference for designing navigation regions and navigation routes for submarines.
Machine Learning and Wearable Sensors for the Early Detection of Balance Disorders in Parkinson’s Disease
Authors: Castelli Gattinara Di Zubiena Francesco , Menna Greta , Mileti Ilaria , Zampogna Alessandro , Asci Francesco , Paoloni Marco , Suppa Antonio , Del Prete Zaccaria , Palermo Eduardo
Published: December 2022

Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson’s disease (PD). However, this approach is burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered “optimum” in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.
Comparing Polyphosphate and Orthophosphate Treatments of Solution-Precipitated Aragonite Powders
Authors: Gao Boyang , Poduska Kristin M.
Published: December 2022

The aqueous and thermal stabilities of aragonite (CaCO3) powders against phase conversion are important for industrial applications that rely on calcium carbonate. We describe the synthesis and characterization of solution-precipitated aragonite powders before and after exposure to different aqueous polyphosphate (SHMP) or orthophosphate (PO4) treatments with concentrations ranging between 1–10 mM (∼1 g/L). Based on infrared spectra, differential scanning calorimetry, and thermogravimetric analyses, results show that orthophosphate treatments lead to secondary phase formation and complex thermal annealing behaviors. In contrast, polyphosphate treatments help to prevent against aragonite dissolution during water exposure, and also provide a slight increase in the thermal stability of aragonite with regard to conversion to calcite.
Gradient Boosting for Health IoT Federated Learning
Authors: Wassan Sobia , Suhail Beenish , Mubeen Riaqa , Raj Bhavana , Agarwal Ujjwal , Khatri Eti , Gopinathan Sujith , Dhiman Gaurav
Published: December 2022

Federated learning preserves the privacy of user data through Machine Learning (ML). It enables the training of an ML model during this process. The Healthcare Internet of Things (HIoT) can be used for intelligent technology, remote detection, remote medical care, and remote monitoring. The databases of many medical institutes include a vast quantity of medical information. Nonetheless, based on its specific nature of health information, susceptibilities to private information, and since it cannot be pooled related to data islands, Federated Learning (FL) offers a solution as a shared collaborative artificial intelligence technology. However, FL addresses a series of security and privacy issues. An adaptive Differential Security Federated Learning Healthcare IoT (DPFL-HIoT) model is proposed in this study. We propose differential privacy federated learning with an adaptive GBTM model algorithm for local updates, which helps adapt the model’s parameters based on the data characteristics and gradients. By training and applying a Gradient Boosted Trees model, the GBTM model identifies medical fraud based on patient information. This model is validated to check performance. Real-world experiments show that our proposed algorithm effectively protects data privacy.
CoviBlock: A Secure Blockchain-Based Smart Healthcare Assisting System
Authors: Egala Bhaskara S. , Pradhan Ashok K. , Gupta Shubham , Sahoo Kshira Sagar , Bilal Muhammad , Kwak Kyung-Sup
Published: December 2022

The recent COVID-19 pandemic has underlined the significance of digital health record management systems for pandemic mitigation. Existing smart healthcare systems (SHSs) fail to preserve system-level medical record openness and privacy while including mitigating measures such as testing, tracking, and treating (3T). In addition, current centralised compute architectures are susceptible to denial of service assaults because of DDoS or bottleneck difficulties. In addition, these current SHSs are susceptible to leakage of sensitive data, unauthorised data modification, and non-repudiation. In centralised models of the current system, a third party controls the data, and data owners may not have total control over their data. The Coviblock, a novel, decentralised, blockchain-based smart healthcare assistance system, is proposed in this study to support medical record privacy and security in the pandemic mitigation process without sacrificing system usability. The Coviblock ensures system-level openness and trustworthiness in the administration and use of medical records. Edge computing and the InterPlanetary File System (IPFS) are recommended as part of a decentralised distributed storage system (DDSS) to reduce the latency and the cost of data operations on the blockchain (IPFS). Using blockchain ledgers, the DDSS ensures system-level transparency and event traceability in the administration of medical records. A distributed, decentralised resource access control mechanism (DDRAC) is also proposed to guarantee the secrecy and privacy of DDSS data. To confirm the Coviblock’s real-time behaviour on an Ethereum test network, a prototype of the technology is constructed and examined. To demonstrate the benefits of the proposed system, we compare it to current cloud-based health cyber–physical systems (H-CPSs) with blockchain. According to the experimental research, the Coviblock maintains the same level of security and privacy as existing H-CPSs while performing considerably better. Lastly, the suggested system greatly reduces latency in operations, such as 32 milliseconds (ms) to produce a new record, 29 ms to update vaccination data, and 27 ms to validate a given certificate through the DDSS.
Text Mining Applications in the Construction Industry: Current Status, Research Gaps, and Prospects
Authors: Xu Na , Zhou Xueqing , Guo Chaoran , Xiao Bai , Wei Fei , Hu Yuting
Published: December 2022

With the advent of the Industry 4.0 era, information technology has been widely developed and applied in the construction engineering field. Text mining techniques can extract interesting and important data hidden in plain text, potentially allowing problems in the construction field to be addressed. Although text mining techniques have been used in the construction field for many years, there is a lack of recent reviews focused on their development and application from a literature analysis perspective; therefore, we conducted a review with the aim of filling this gap. We use a combination of bibliometric and manual literature analyses to systematically review the text mining-based literature related to the construction field from 1997 to 2022. Specifically, publication analysis, collaboration analysis, co-citation analysis, and keyword analysis were conducted on 185 articles collected from the SCOPUS database. Based on a read-through of the 185 papers, the current research topics in text mining were manually determined and sorted, including tasks and methods, application areas, and core methods and algorithms. The presented results provide a comprehensive understanding of the current state of TM techniques, thereby contributing to the further development of TM techniques in the construction industry.
Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions
Authors: Tlapa Diego , Franco-Alucano Ignacio , Limon-Romero Jorge , Baez-Lopez Yolanda , Tortorella Guilherme
Published: December 2022

In the Industry 4.0 era, healthcare services have experienced more dual interventions that integrate lean and six sigma with simulation modeling. This systematic review, which focuses on evidence-based practice and complies with the PRISMA guidelines, aims to evaluate the effects of these dual interventions on healthcare services and provide insights into which paradigms and tools produce the best results. Our review identified 4018 studies, of which 39 studies met the inclusion criteria and were selected. The predominantly positive results reported in 73 outcomes were mostly related to patient flow: length of stay, waiting time, and turnaround time. In contrast, there is little reported evidence of the impact on patient health and satisfaction, staff wellbeing, resource use, and savings. Discrete event simulation stands out in 74% of the interventions as the main simulation paradigm. Meanwhile, 66% of the interventions utilized lean, followed by lean-six sigma with 28%. Our findings confirm that dual interventions focus mainly on utilization and access to healthcare services, particularly on either patient flow problems or problems concerning the allocation of resources; however, most interventions lack evidence of implementation. Therefore, this study promotes further research and encourages practical applications including the use of Industry 4.0 technologies.