bluuebunny/crossref_metadata_2025 huggingface dataset:
Preliminary Structural Design of Coreless Spoiler by Topological Optimization
Authors: Israr Haris , Chwen Teh , Latif Ainullotfi , Wong King , Rahimian Koloor Seyed , Yidris Noorfaizal , Yahya Mohd
Published: October 2022

Most spoilers are made from a sandwich structure with a honeycomb component as its core. However, the honeycomb core is sensitive to water ingress, causing damage to the control surface due to its weak moisture-resistance behavior. This study aimed to conduct the design and analysis of an improved composite structure for a coreless spoiler. A spoiler design of an aircraft, the A320, was used for the case study. The weaknesses of a coreless spoiler were identified through finite element analysis via Abaqus software. Multi-spar and multi-rib designs were studied and compared for topological optimization. The variables used for evaluation were the Tsai–Hill failure index and the critical buckling load. The design with the most potential was considered for parametric optimization to obtain the most satisfactory configuration. The results showed that the upper skin of the spoiler without a honeycomb core failed the Tsai–Hill criteria. Furthermore, the results show that the multi-spar configuration outperformed the multi-rib configuration. The final multi-spar configuration achieved a mass reduction of 24% from the original spoiler and an additional 6% mass reduction by re-designing the internal structures without violating the design criteria. In conclusion, the weaknesses of the spoiler without a honeycomb core have been identified, and an improved design for a coreless spoiler has been proposed.
Managing Transport Processes in Thermal Cracking to Produce High-Quality Fuel from Extra-Heavy Waste Crude Oil Using a Semi-Batch Reactor
Authors: Almukhtar Riyadh , Hammoodi Sally I. , Majdi Hasan Shakir , Sukkar Khalid A.
Published: October 2022

Soil pollution from waste crude oil in emergency pits is a major problem at petroleum industry sites. In this work, extra-heavy waste crude oil was recovered from emergency pits and underwent many pre-purification processes to remove water and impurities. This type of oil was subjected to thermal cracking reactions in a semi-batch reactor constructed from stainless steel, with a volume of 500 mL. The cracking reactions were tested at operating temperatures of 400, 425, and 450 °C, with operating pressures of 1, 3, 5, and 7 bar. The results indicated that during thermal cracking, the reaction mechanism was highly dependent on the heat and mass transfer processes that occurred in the reactor. It was noted that the interaction between the optimal reaction temperature and operating pressure enhanced the product distribution and formation of high-quality liquid fuel with low gaseous and coke formations. The highest API of 30.5 was achieved for the liquid product at an operating temperature of 400 °C and a pressure of 3 bar. Additionally, an evaluation of the thermal cracking mechanism found that the transport processes that occurred in the reactor were the chief factor in providing a high-performance thermal cracking process.
New TLC Method Combined with Densitometry for Determination of Sertraline and Fluoxetine in Pharmaceutical Preparations
Authors: Pyka-Pająk Alina
Published: October 2022

TLC combined with densitometry was used, and chromatographic conditions were developed to determination both fluoxetine and sertraline in pharmaceuticals. The mobile phase with the composition of acetone, chloroform, and ammonia (10:5:1, v/v) allowed separation of the most degradation products of sertraline and fluoxetine from all nineteen used in the study of mobile phases. Therefore, this mobile phase was selected as optimal for the analysis of sertraline and fluoxetine in pharmaceutical formulations. The RF values of sertraline and fluoxetine differ from the degradation products of fluoxetine and sertraline. Therefore, the developed chromatographic conditions can be used for the simultaneous determination of fluoxetine and sertraline. Confirmation of the identity of the active substance can be made on the basis of the compliance of the RF values and the spectrodensitograms of the substance from the pharmaceutical preparation and the standard of fluoxetine or sertraline, respectively. The developed method was simple, economical, specific, precise, accurate, sensitive, and robust, with a good range of linearity for the quantification of fluoxetine and sertraline in pharmaceutical preparations. TLC in combination with densitometry can be recommended for the analysis of fluoxetine and sertraline in the absence of HPLC in the laboratory or to confirm results obtained with other analytical techniques.
Hybrid Dynamic Models of Bioprocesses Based on Elementary Flux Modes and Multilayer Perceptrons
Authors: Maton Maxime , Bogaerts Philippe , Vande Wouwer Alain
Published: October 2022

The derivation of minimal bioreaction models is of primary importance to develop monitoring and control strategies of cell/microorganism culture production. These minimal bioreaction models can be obtained based on the selection of a basis of elementary flux modes (EFMs) using an algorithm starting from a relatively large set of EFMs and progressively reducing their numbers based on geometric and least-squares residual criteria. The reaction rates associated with the selected EFMs usually have complex features resulting from the combination of different activation, inhibition and saturation effects from several culture species. Multilayer perceptrons (MLPs) are used in order to undertake the representation of these rates, resulting in a hybrid dynamic model combining the mass-balance equations provided by the EFMs to the rate equations described by the MLPs. To further reduce the number of kinetic parameters of the model, pruning algorithms for the MLPs are also considered. The whole procedure ends up with reduced-order macroscopic models that show promising prediction results, as illustrated with data of perfusion cultures of hybridoma cell line HB-58.
Step-by-Step Hybrid Conversion of Glucose to 5-acetoxymethyl-2-furfural Using Immobilized Enzymes and Cation Exchange Resin
Authors: Lee Kyung Won , Cho Jin Ku , Park Chulhwan , Kim Baek-Jin
Published: October 2022

An alternative to 5-hydroxymethyl-2-furfural (HMF), which is a promising furan derivative that can be used as a starting material for the preparation of non-petroleum-derived polymeric materials from sugars, is 5-acetoxymethyl-2-furfural (AMF). The less-hydrophilic acetyl group of AMF has advantages over the hydroxy group of HMF in terms of thermal stability and isolation. In previous studies, fructose has been used as a starting material along with lipases for the enzymatic synthesis of AMF. In this study, we designed a hybrid synthesis system that includes the isomerization and esterification of glucose into AMF. For the step-by-step conversion of glucose to 1,6-diacetylfructose (DAF), glucose-isomerase and immobilized lipase (Novozym 435) were used as enzymes. Furthermore, for the synthesis of AMF, the direct dehydration of DAF was performed using a cation exchange resin (Amberlyst 15), combined with several industrial solvents, such as dimethylsulfoxide (DMSO), acetonitrile (AN) and dimethylformamide (DMF) for the synthesis of AMF. In order to improve the final yield of AMF, we determined the best solvent conditions. While the AMF yield after the direct dehydration of DAF in a single solvent was maximum 24%, an AMF and HMF yield in the mixed solvent such as dioxane and DMS (9:1) was achieved each 65% and 15%. According to these results, we found that the addition of dioxane in aprotic polar solvents could affect the dehydration reaction and dramatically improve the formation of AMF and HMF.
Potential Predictors for Cognitive Decline in Vascular Dementia: A Machine Learning Analysis
Authors: Murdaca Giuseppe , Banchero Sara , Casciaro Marco , Tonacci Alessandro , Billeci Lucia , Nencioni Alessio , Pioggia Giovanni , Genovese Sara , Monacelli Fiammetta , Gangemi Sebastiano
Published: October 2022

Vascular dementia (VD) is a cognitive impairment typical of advanced age with vascular etiology. It results from several vascular micro-accidents involving brain vessels carrying less oxygen and nutrients than it needs. This being a degenerative disease, the diagnosis often arrives too late, when the brain tissue is already damaged. Thus, prevention is the best solution to avoid irreversible cognitive impairment in patients with specific risk factors. Using the machine learning (ML) approach, our group evaluated Mini-Mental State Examination (MMSE) changes in patients affected by Alzheimer’s disease by considering different clinical parameters. We decided to apply a similar ML scheme to VD due to the consistent data obtained from the first work, including the assessment of various ML models (LASSO, RIDGE, Elastic Net, CART, Random Forest) for the outcome prediction (i.e., the MMSE modification throughout time). MMSE at recruitment, folate, MCV, PTH, creatinine, vitamin B12, TSH, and hemoglobinwere the best predictive parameters individuated by the best ML model: Random Forest. ML results can be useful inidentify predictive biomarkers for cognitive worsening in VD early and also for focusing on necessary examinations at the first visits to draw the most predictive features, saving time and money and reducethe burden on the patients themselves. Such results should be integrated with brain imaging, physiological signal measurements, and sensory patterns, particularly forthose senses already demonstrated to have a significant link with neurodegeneration. Adjusting compound deficit by administering nutraceuticals could support treatment effectiveness and lead to a better quality of life for patients, families, and caregivers, with a consistent impact on the national health systems load.
Deep Hierarchical Interval Type 2 Self-Organizing Fuzzy System for Data-Driven Robot Control
Authors: Mei Zhen , Zhao Tao , Liu Nian
Published: October 2022

To solve the dimensional explosion problem, this paper proposes a new architecture for the fuzzy system, the deep hierarchical self-organizing interval type-2 fuzzy system (DHSOIT2FS). Each sub-fuzzy system is a self-organizing interval type-2 fuzzy system, constructed online, with rules constructed by a rule online update algorithm, consequent parameters updated by iterative least squares, and antecedent parameters are updated using a gradient descent algorithm. DHSOIT2FS uses a classic serial-layered structure to build the overall framework. The first layer uses the first two dimensions of data as input. Each subsequent layer uses the output of the previous layer with the next dimensional data as input until it is built. During the training process, each data point is trained with DHSOIT2FS before passing in the next data point to achieve online construction. The effectiveness of the approach in this paper is illustrated using two numerical simulation examples. The proposed method is also applied to a data-driven control example of a single-link robot and achieves good tracking results.
Mathematical Modeling to Describe Drying Behavior of Kyoho (Vitis labruscana) Skin Waste: Drying Kinetics and Quality Attributes
Authors: Sridhar Kandi , Charles Albert Linton
Published: October 2022

Grape skin (Kyoho: Vitis labruscana), a by-product of processed grapes, was experimentally investigated for its drying behavior at different drying temperatures with five thin layer drying models. Moreover, we determined the effect of drying temperature on the bioactive capacity of Kyoho skin. The experimental moisture ratio decreased with increasing drying temperature. The drying process was predicted by mathematical models, such as Page (303.15 K: R2 = 0.9815, 333.15 K: R2 = 0.9685) and two-term (313.15 K: R2 = 0.9639, 323.15 K: R2 = 0.9737) models. Moisture diffusivity (Deff) ranged from 2.87 × 10−8 to 9.82 × 10−8 m2/s, with an activation energy (Ea) of 33.78 ± 1.06 kJ/mol. Total phenolic compounds (0.37 ± 0.04 to 0.23 ± 0.03 mg GAE/g) and antioxidant activities (DPPH• activity of 93.06 to 73.31%) of Kyoho skin were significantly affected by drying temperature. Thus, this study concluded that the drying process decreased the bioactive potential of grape skin; therefore, we recommend that the food processing industry needs to consider drying variables during the processing of grape skin-based value-added products for improved food production.
An Intensified Green Process for the Coproduction of DMC and DMO by the Oxidative Carbonylation of Methanol
Authors: Al-Rabiah Abdulrahman A. , Almutlaq Abdulaziz M. , Bashth Omar S. , Alyasser Taher M. , Alshehri Fayez A. , Alofai Mohammed S. , Alshehri Abdulelah S.
Published: October 2022

Dimethyl carbonate (DMC) is an eco-friendly and sustainable compound with widespread industrial applications. Various extensive routes have been exploited in the chemical industry to produce DMC. However, these routes have several environmental and energy drawbacks. In this study, a promising novel industrial scheme for the synthesis of DMC via the oxidative carbonylation of vaporized methanol with dimethyl oxalate (DMO) as a byproduct is investigated. A methanol conversion of 81.86% and a DMC selectivity of 83.47% were achieved using an isothermal fixed-bed reactor at 130 °C. The DMC is withdrawn at a purity of >99 mol% via pressure-swing azeotropic distillations. Heat integration was performed to optimize energy consumption, reducing the energy requirements by 28%. An economic evaluation was performed for estimating the profitability via cash-flow diagrams, predicting a payback period of 3.7 years. The proposed green process exhibits several benefits, including high profitability and being environmentally friendly. It also eliminates the use or production of hazardous materials, and it enhances safety characteristics.
Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences
Authors: McGillivray Barbara , Marongiu Paola , Pedrazzini Nilo , Ribary Marton , Wigdorowitz Mandy , Zordan Eleonora
Published: October 2022

The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem. In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ). In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets. Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads. HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.
Daoist Reflections on the See-Saw of Contingency and Autonomy: The Laozi and Zhuangzi in Dialogue with Sandel, Rosa, Rorty, Gray
Authors: D’Ambrosio Paul Joseph
Published: October 2022

Nearly all philosophical inquiry is rooted in contingency. From ontology and theories of God to politics and ethics, dealing with, explaining, planning for, or even following contingency is a consistent theme. In the background of their recent works, Michael Sandel, Hartmut Rosa, John Gray, and Richard Rorty all see contingency and autonomy in a see-saw relationship: more of one correspondingly results in less of the other. Daoist philosophical reflections provide a different take on contingency. We can still have an experience of “self” and of making choices without positing any notion of autonomy outside of contingency.
Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis
Authors: Woods Thomas , Miljkovic Tatjana
Published: October 2022

We propose a new approach for estimating the state-level direct and indirect economic cost of obesity in the United States for the time period 1996 to 2018. Our unique top-down methodology integrates a prevalence-based method with various medical-level costs, economic, demographic, and socio-economic factors. Using this approach, we investigate the relationship between the estimates of the total obesity-related costs and the health insurance premium by state in order to evaluate the state burden of obesity. Our estimate of the total national economic cost attributed to obesity is approximately $422 billion in 2018, representing about 2% of the national GDP for the same year. Using exponential smoothing models, we forecast that the total cost would reach $475 billion in 2021 without accounting for the impact of COVID-19 on obesity. The top states driving the cost estimates are California, Texas, New York, and Florida. A bootstrapping technique is employed to the state-level estimated cost in order to determine the average cost per person. We hope that our study will promote interest in this topic and open discussion for further research in this area.
Robust Prescribed Trajectory Tracking Control of a Robot Manipulator Using Adaptive Finite-Time Sliding Mode and Extreme Learning Machine Method
Authors: Raoufi Mona , Habibi Hamed , Yazdani Amirmehdi , Wang Hai
Published: October 2022

This study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications.
U-Net for Taiwan Shoreline Detection from SAR Images
Authors: Chang Lena , Chen Yi-Ting , Wu Meng-Che , Alkhaleefah Mohammad , Chang Yang-Lang
Published: October 2022

Climate change and global warming lead to changes in the sea level and shoreline, which pose a huge threat to island regions. Therefore, it is important to effectively detect the shoreline changes. Taiwan is a typical island, located at the junction of the East China Sea and the South China Sea in the Pacific Northwest, and is deeply affected by shoreline changes. In this research, Taiwan was selected as the study area. In this research, an efficient shoreline detection method was proposed based on the semantic segmentation U-Net model using the Sentinel-1 synthetic aperture radar (SAR) data of Taiwan island. In addition, the batch normalization (BN) module was added to the convolution layers in the U-Net architecture to further improve the generalization ability of U-Net and accelerate the training process. A self-built shoreline dataset was introduced to train the U-Net model and test its detection efficiency. The dataset consists of a total of 4029 SAR images covering all coastal areas of Taiwan. The training samples of the dataset were annotated by morphological processing and manual inspection. The segmentation results of U-Net were then processed by edge detection and morphological postprocessing to extract the shoreline. The experimental results showed that the proposed method could achieve a satisfactory detection performance compared with the related methods using the data provided by the Ministry of the Interior of Taiwan from 2016 to 2019 for different coastal landforms in Taiwan. Within a 5-pixel difference between the detected shoreline and the ground truth data, the F1-Meaure of the proposed method exceeded 80%. In addition, the potential of this method in shoreline change detection was validated with a sandbar located on the southwestern coast of Taiwan. Finally, the entire shoreline of Taiwan has been described by the proposed approach and the detected shoreline length was close to the actual length.
Comparison of Physical-Based Models to Measure Forest Resilience to Fire as a Function of Burn Severity
Authors: Fernández-Guisuraga José Manuel , Suárez-Seoane Susana , Quintano Carmen , Fernández-Manso Alfonso , Calvo Leonor
Published: October 2022

We aimed to compare the potential of physical-based models (radiative transfer and pixel unmixing models) for evaluating the short-term resilience to fire of several shrubland communities as a function of their regenerative strategy and burn severity. The study site was located within the perimeter of a wildfire that occurred in summer 2017 in the northwestern Iberian Peninsula. A pre- and post-fire time series of Sentinel-2 satellite imagery was acquired to estimate fractional vegetation cover (FVC) from the (i) PROSAIL-D radiative transfer model inversion using the random forest algorithm, and (ii) multiple endmember spectral mixture analysis (MESMA). The FVC retrieval was validated throughout the time series by means of field data stratified by plant community type (i.e., regenerative strategy). The inversion of PROSAIL-D featured the highest overall fit for the entire time series (R2 > 0.75), followed by MESMA (R2 > 0.64). We estimated the resilience of shrubland communities in terms of FVC recovery using an impact-normalized resilience index and a linear model. High burn severity negatively influenced the short-term resilience of shrublands dominated by facultative seeder species. In contrast, shrublands dominated by resprouters reached pre-fire FVC values regardless of burn severity.
Drought Resistance of Vegetation and Its Change Characteristics before and after the Implementation of the Grain for Green Program on the Loess Plateau, China
Authors: Wang Dong , Yue Dongxia , Zhou Yanyan , Huo Feibiao , Bao Qiong , Li Kai
Published: October 2022

Drought affects the growth and productivity of vegetation, and the analysis of drought resistance of vegetation can help ecological and environmental protection and sustainable development in drought-prone areas. The Loess Plateau (LP) is a drought-prone area in China with an extremely fragile ecological environment. This study analyzed the drought resistance of vegetation across different climate regions and vegetation biotypes, explored the characteristics of changes in vegetation drought resistance before and after the implementation of the Grain for Green Program (GGP), and evaluated the relative contribution of climatic factors and human activities to the change in drought resistance of vegetation. The following conclusions are obtained. (1) The drought resistance of vegetation on the LP basically showed a spatial pattern of increasing from northwest to southeast with the degree of aridity. The vegetation in the semi-humid and arid regions showed the strongest and weakest drought resistance, respectively. (2) The drought resistance of vegetation on the LP mainly showed an increasing trend since the GGP was implemented, but there were differences in different climatic zones. In semi-humid regions, the drought resistance of vegetation mainly showed a weakening trend, while in arid and semi-arid regions, it mainly showed an increasing trend. There were differences between vegetation biotypes as well; the drought resistance of forest and grassland showed a different trend in different climatic zones, while that of crops in all climatic zones showed an increasing trend. In the area with cropland returned, the drought resistance tended to increase where crops turned to forests, but the area where crops turned to grassland showed a weakening trend. (3) The positive contribution of climate change and human activities leads to the enhancement of drought resistance of vegetation in most areas of the LP, and the weakening of drought resistance of vegetation in semi-humid regions is dominated by the negative contribution of climate change. The negative contribution of human activities is the main reason for the decrease in drought resistance of vegetation in the area of returning cropland to grassland. This study can provide a reference for ecological protection and high-quality development of the LP.
A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico
Authors: Yan Xingguang , Li Jing , Yang Di , Li Jiwei , Ma Tianyue , Su Yiting , Shao Jiahao , Zhang Rui
Published: October 2022

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands.
Kernel Minimum Noise Fraction Transformation-Based Background Separation Model for Hyperspectral Anomaly Detection
Authors: Xue Tianru , Jia Jianxin , Xie Hui , Zhang Changxing , Deng Xuan , Wang Yueming
Published: October 2022

A significant challenge in methods for anomaly detection (AD) in hyperspectral images (HSIs) is determining how to construct an efficient representation for anomalies and background information. Considering the high-order structures of HSIs and the estimation of anomalies and background information in AD, this article proposes a kernel minimum noise fraction transformation-based background separation model (KMNF-BSM) to separate the anomalies and background information. First, spectral-domain KMNF transformation is performed on the original hyperspectral data to fully mine the high-order correlation between spectral bands. Then, a BSM that combines the outlier removal, the iteration strategy, and the Reed–Xiaoli detector (RXD) is proposed to obtain accurate anomalous and background pixel sets based on the extracted features. Finally, the anomalous and background pixel sets are used as input for anomaly detectors to improve the background suppression and anomaly detection capabilities. Experiments on several HSIs with different spatial and spectral resolutions over different scenes are performed. The results demonstrate that the KMNF-BSM-based algorithms have better target detectability and background suppressibility than other state-of-the-art algorithms.
A Novel Low-Cost GNSS Solution for the Real-Time Deformation Monitoring of Cable Saddle Pushing: A Case Study of Guojiatuo Suspension Bridge
Authors: Zhao Lidu , Yang Yihui , Xiang Zhongfu , Zhang Shuangcheng , Li Xinrui , Wang Xuqiao , Ma Xiaping , Hu Chuan , Pan Jianping , Zhou Yin , Chen Maolin
Published: October 2022

Extreme loadings, a hostile environment and dangerous operation lead to the unsafe state of bridges under construction, especially large-span bridges. Global Navigation Satellite Systems (GNSS) tend to be the best choice for real-time deformation monitoring due to the significant advantage of automation, continuation, all-weather operation and high precision. Unfortunately, the traditional geodetic GNSS instrument with its high price and large volume is limited in its applications. Hence, we design and develop low-cost GNSS equipment by simplifying the monitoring module. The performance of the proposed solution is evaluated through an experimental dynamic scenario, proving its ability to track abrupt deformation down to 3–5 mm. We take Chongqing Guojiatuo Suspension Bridge in China as a case study. We build a real-time low-cost GNSS monitoring cloud platform. The low-cost bridge GNSS monitoring stations are located at the top of the south and north towers, midspan upstream and downstream respectively and the reference station is located in the stable zone 400 m away from the bridge management buildings. We conducted a detailed experimental assessment of low-cost GNSS on 5 April and a real-time deformation detection experiment of the towers and main cables during the dynamic cable saddle pushing process on 26 February 2022. In the static experiment, the standard deviation of the residual using the multi-GNSS solution is 2 mm in the horizontal direction and 5 mm in the vertical direction. The multi-GNSS solution significantly outperforms the BDS/GPS single system. The dynamic experiment shows that, compared with the movement measured by the robotic total station, the horizontal error of the south tower and north tower measured by low-cost GNSS is below 0.005 m and 0.008 m respectively. This study highlights the potential of low-cost GNSS solutions for Structural Health Monitoring (SHM) applications.
New Application: A Hand Air Writing System Based on Radar Dual View Sequential Feature Fusion Idea
Authors: Zhao Yinan , Liu Tao , Feng Xiang , Zhao Zhanfeng , Cui Wenqing , Fan Yu
Published: October 2022

In recent years, non-contact human–computer interactions have aroused much attention. In this paper, we mainly propose a dual view observation system based on the frontal and side millimeter-wave radars (MWR) to collect echo data of the Air writing digits “0~9”, simultaneously. Additionally, we also propose a novel distance approximation method to make the trajectory reconstruction more efficient. To exploit these characteristics of spatial-temporal adjacency in handwriting digits, we propose a novel clustering algorithm, named the constrained density-based spatial clustering of application with noise (CDBSCAN), to remove background noise or clutter. Moreover, we also design a robust gesture segmentation method by using twice-difference and high–low thresholds. In our trials and comparisons, based on the trajectories formulated by echo data series of time–distance and time–velocity of dual views, we present a lightweight-based convolution neural network (CNN) to realize these digits recognition. Experiment results show that our system has a relatively high recognition accuracy, which would provide a feasible application for future human–computer interaction scenarios.
Application of Distributed Optical Fiber Sensing Technology to the Detection and Monitoring of Internal Swelling Pathologies in Massive Concrete Blocks
Authors: Alj Ismail , Quiertant Marc , Khadour Aghiad , Grando Quentin , Benzarti Karim
Published: October 2022

This paper presents an experimental application of Distributed Optical Fiber Sensors (DOFS) for the Structural Health Monitoring (SHM) of concrete structures affected by internal swelling pathologies. In the framework of a large research project aiming to assess the possible extension of the operating lifetime of nuclear power plants from 40 to 60 years, massive blocks were cast from reactive concrete mixtures intended to develop delayed ettringite formation and alkali–silica reaction. These blocks were subjected to specific ageing conditions to initiate and accelerate the concrete pathologies. Some of the blocks were instrumented with DOFS bonded to the surface and embedded in the concrete. Using an interrogator device based on Rayleigh backscattering and a suitable procedure to eliminate temperature effects, distributed strain measurements were then performed at different time intervals. The first results of this ongoing study made it possible to demonstrate the feasibility and effectiveness of this sensing technology for detecting and monitoring expansion induced by swelling pathologies in representative-scale concrete structures.
An Experimental Ultrasound System for Qualitative Tomographic Imaging
Authors: Ambrosanio Michele , Franceschini Stefano , Autorino Maria Maddalena , Baselice Fabio , Pascazio Vito
Published: October 2022

The advancement of new promising techniques in the field of biomedical imaging has always been paramount for the research community. Recently, ultrasound tomography has proved to be a good candidate for non-invasive and safe diagnostics. In particular, breast cancer imaging may benefit from this approach, as frequent screening and early diagnosis require decreased system size and costs compared to conventional imaging techniques. Furthermore, a major advantage of these approaches consists in the operator-independent feature, which is very desirable compared to conventional hand-held ultrasound imaging. In this framework, the authors present some imaging results on an experimental campaign acquired via an in-house ultrasound tomographic system designed and built at the University of Naples Parthenope. Imaging performance was evaluated via different tests, showing good potentiality in structural information retrieval. This study represents a first proof of concept in order to validate the system and to consider further realistic cases in near future applications.
A Practical Approach to the Analysis and Optimization of Neural Networks on Embedded Systems
Authors: Merone Mario , Graziosi Alessandro , Lapadula Valerio , Petrosino Lorenzo , d’Angelis Onorato , Vollero Luca
Published: October 2022

The exponential increase in internet data poses several challenges to cloud systems and data centers, such as scalability, power overheads, network load, and data security. To overcome these limitations, research is focusing on the development of edge computing systems, i.e., based on a distributed computing model in which data processing occurs as close as possible to where the data are collected. Edge computing, indeed, mitigates the limitations of cloud computing, implementing artificial intelligence algorithms directly on the embedded devices enabling low latency responses without network overhead or high costs, and improving solution scalability. Today, the hardware improvements of the edge devices make them capable of performing, even if with some constraints, complex computations, such as those required by Deep Neural Networks. Nevertheless, to efficiently implement deep learning algorithms on devices with limited computing power, it is necessary to minimize the production time and to quickly identify, deploy, and, if necessary, optimize the best Neural Network solution. This study focuses on developing a universal method to identify and port the best Neural Network on an edge system, valid regardless of the device, Neural Network, and task typology. The method is based on three steps: a trade-off step to obtain the best Neural Network within different solutions under investigation; an optimization step to find the best configurations of parameters under different acceleration techniques; eventually, an explainability step using local interpretable model-agnostic explanations (LIME), which provides a global approach to quantify the goodness of the classifier decision criteria. We evaluated several MobileNets on the Fudan Shangai-Tech dataset to test the proposed approach.
Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings
Authors: Guo Jing , Ma Biao , Zou Tiangang , Gui Lin , Li Yongbo
Published: October 2022

When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.
A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States
Authors: Kaklauskas Arturas , Abraham Ajith , Ubarte Ieva , Kliukas Romualdas , Luksaite Vaida , Binkyte-Veliene Arune , Vetloviene Ingrida , Kaklauskiene Loreta
Published: October 2022

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.
Simultaneous Multiplexed Quantification of Banned Sudan Dyes Using Surface Enhanced Raman Scattering and Chemometrics
Authors: Alomar Taghrid S. , AlMasoud Najla , Xu Yun , Lima Cassio , Akbali Baris , Maher Simon , Goodacre Royston
Published: October 2022

Azo compounds such as the Sudan dyes I–IV are frequently used illegally as colorants and added to a wide range of foods. These compounds have been linked to a number of food safety hazards. Several methods have been proposed to detect food contamination by azo compounds and most of these are laboratory based; however, the development of reliable and portable methods for the detection and quantification of food contaminated by these chemicals in low concentration is still needed due to their potentially carcinogenic properties. In this study, we investigated the ability of surface enhanced Raman scattering (SERS) combined with chemometrics to quantify Sudan I–IV dyes. SERS spectra were acquired using a portable Raman device and gold nanoparticles were employed as the SERS substrate. As these dyes are hydrophobic, they were first dissolved in water: acetonitrile (1:10, v/v) as single Sudan dyes (I–IV) at varying concentrations. SERS was performed at 785 nm and the spectra were analyzed by using partial least squares regression (PLS-R) with double cross-validations. The coefficient of determination (Q2) were 0.9286, 0.9206, 0.8676 and 0.9705 for Sudan I to IV, respectively; the corresponding limits of detection (LOD) for these dyes were estimated to be 6.27 × 10−6, 5.35 × 10−5, 9.40 × 10−6 and 1.84 × 10−6 M. Next, quadruplex mixtures were made containing all four Sudan dyes. As the number of possible combinations needed to cover the full concentration range at 5% intervals would have meant collecting SERS spectra from 194,481 samples (214 combinations) we used a sustainable solution based on Latin hypercubic sampling and reduced the number of mixtures to be analyzed to just 90. After collecting SERS spectra from these mixture PLS-R models with bootstrapping validations were employed. The results were slightly worse in which the Q2 for Sudan I to IV were 0.8593, 0.7255, 0.5207 and 0.5940 when PLS1 models (i.e., one model for one dye) was employed and they changed to 0.8329, 0.7288, 0.5032 and 0.5459 when PLS2 models were employed (i.e., four dyes were modelled simultaneously). These results showed the potential of SERS to be used as a high-throughput, low-cost, and reliable methods for detecting and quantifying multiple Sudan dyes in low concentration from illegally adulterated samples.
State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer Learning
Authors: Yao Lei , Wen Jishu , Xu Shiming , Zheng Jie , Hou Junjian , Fang Zhanpeng , Xiao Yanqiu
Published: October 2022

Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation.
A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification
Authors: Stephen Okeke , Madanian Samaneh , Nguyen Minh
Published: October 2022

Manual or traditional industrial product inspection and defect-recognition models have some limitations, including process complexity, time-consuming, error-prone, and expensiveness. These issues negatively impact the quality control processes. Therefore, an efficient, rapid, and intelligent model is required to improve industrial products’ production fault recognition and classification for optimal visual inspections and quality control. However, intelligent models obtained with a tradeoff of high accuracy for high latency are tedious for real-time implementation and inferencing. This work proposes an ensemble deep-leaning architectural framework based on a deep learning model architectural voting policy to compute and learn the hierarchical and high-level features in industrial artefacts. The voting policy is formulated with respect to three crucial viable model characteristics: model optimality, efficiency, and performance accuracy. In the study, three publicly available industrial produce datasets were used for the proposed model’s various experiments and validation process, with remarkable results recorded, demonstrating a significant increase in fault recognition and classification performance in industrial products. In the study, three publicly available industrial produce datasets were used for the proposed model’s various experiments and validation process, with remarkable results recorded, demonstrating a significant increase in fault recognition and classification performance in industrial products.
A Self-Learning Mechanism-Based Approach to Helicopter Entry and Departure Recognition
Authors: Lyu Zonglei , Chang Xuepeng , An Wei , Yu Tong
Published: October 2022

In order to accurately record the entry and departure times of helicopters and reduce the incidence of general aviation accidents, this paper proposes a helicopter entry and departure recognition method based on a self-learning mechanism, which is supplemented by a lightweight object detection module and an image classification module. The original image data obtained from the lightweight object detection module are used to construct an Automatic Selector of Data (Auto-SD) and an Adjustment Evaluator of Data Bias (Ad-EDB), whereby Auto-SD automatically generates a pseudo-clustering of the original image data. Ad-EDB then performs the adjustment evaluation and selects the best matching module for image classification. The self-learning mechanism constructed in this paper is applied to the helicopter entry and departure recognition scenario, and the ResNet18 residual network is selected for state classification. As regards the self-built helicopter entry and departure data set, the accuracy reaches 97.83%, which is 6.51% better than the bounding box detection method. To a certain extent, the strong reliance on manual annotation for helicopter entry and departure status classification scenarios is lifted, and the data auto-selector is continuously optimized using the preorder classification results to establish a circular learning loop in the algorithm.