bluuebunny/crossref_metadata_2025 huggingface dataset:
Disasters and Society: Comparing the Shang and Mycenaean Response to Natural Phenomena through Text and Archaeology
Authors: Westra Alexander Jan Dimitris , Miao Changhong , Liritzis Ioannis , Stefanakis Manolis
Published: July 2022

Disasters do and have happened throughout human existence. Their traces are found in the environmental record, archaeological evidence, and historical chronicles. Societal responses to these events vary and depend on ecological and cultural constraints and opportunities. These elements are being discovered more and more on a global scale. When looking at disasters in antiquity, restoring the environmental and geographical context on both the macro- and microscale is necessary. The relationships between global climatic processes and microgeographical approaches ought to be understood by examining detailed societal strategies conceived in response to threatening natural phenomena. Architectural designs, human geography, political geography, technological artefacts, and textual testimony are linked to a society’s inherited and real sense of natural threats, such as floods, earthquakes, fires, diseases, etc. The Shang and Mycenaean cultures are prime examples, among others, of Bronze Age societies with distinctive geographical, environmental, and cultural features and structures that defined their attitudes and responses to dangerous natural phenomena, such as floods, earthquakes, landslides, and drought. By leaning on two well-documented societies with little to no apparent similarities in environmental and cultural aspects and no credible evidence of contact, diffusion, or exchange, we can examine them free of the onus of diffused intangible and tangible cultural features. Even though some evidence of long-distance networks in the Bronze Age exists, they presumable had no impact on local adaptive strategies. The Aegean Sea and Yellow River cultural landscapes share many similarities and dissimilarities and vast territorial and cultural expansions. They have an apparent contemporaneity, and both recede and collapse at about the same time. Thus, through the microgeography of a few select Shang and Mycenaean sites and their relevant environmental, archaeological, and historical contexts, and through environmental effects on a global scale, we may understand chain events of scattered human societal changes, collapses, and revolutions on a structural level.
Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content
Authors: Verma Bhagyashree , Prasad Rajendra , Srivastava Prashant K. , Singh Prachi , Badola Anushree , Sharma Jyoti
Published: July 2022

The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval.
Offshore Oil Slick Detection: From Photo-Interpreter to Explainable Multi-Modal Deep Learning Models Using SAR Images and Contextual Data
Authors: Amri Emna , Dardouillet Pierre , Benoit Alexandre , Courteille Hermann , Bolon Philippe , Dubucq Dominique , Credoz Anthony
Published: July 2022

Ocean surface monitoring, emphasizing oil slick detection, has become essential due to its importance for oil exploration and ecosystem risk prevention. Automation is now mandatory since the manual annotation process of oil by photo-interpreters is time-consuming and cannot process the data collected continuously by the available spaceborne sensors. Studies on automatic detection methods mainly focus on Synthetic Aperture Radar (SAR) data exclusively to detect anthropogenic (spills) or natural (seeps) oil slicks, all using limited datasets. The main goal is to maximize the detection of oil slicks of both natures while being robust to other phenomena that generate false alarms, called “lookalikes”. To this end, this paper presents the automation of offshore oil slick detection on an extensive database of real and recent oil slick monitoring scenarios, including both types of slicks. It relies on slick annotations performed by expert photo-interpreters on Sentinel-1 SAR data over four years and three areas worldwide. In addition, contextual data such as wind estimates and infrastructure positions are included in the database as they are relevant data for oil detection. The contributions of this paper are: (i) A comparative study of deep learning approaches using SAR data. A semantic and instance segmentation analysis via FC-DenseNet and Mask R-CNN, respectively. (ii) A proposal for Fuse-FC-DenseNet, an extension of FC-DenseNet that fuses heterogeneous SAR and wind speed data for enhanced oil slick segmentation. (iii) An improved set of evaluation metrics dedicated to the task that considers contextual information. (iv) A visual explanation of deep learning predictions based on the SHapley Additive exPlanation (SHAP) method adapted to semantic segmentation. The proposed approach yields a detection performance of up to 94% of good detection with a false alarm reduction ranging from 14% to 34% compared to mono-modal models. These results provide new solutions to improve the detection of natural and anthropogenic oil slicks by providing tools that allow photo-interpreters to work more efficiently on a wide range of marine surfaces to be monitored worldwide. Such a tool will accelerate the oil slick detection task to keep up with the continuous sensor acquisition. This upstream work will allow us to study its possible integration into an industrial production pipeline. In addition, a prediction explanation is proposed, which can be integrated as a step to identify the appropriate methodology for presenting the predictions to the experts and understanding the obtained predictions and their sensitivity to contextual information. Thus it helps them to optimize their way of working.
Excitations of Seasonal Polar Motions Derived from Satellite Gravimetry and General Circulation Models: Comparisons of Harmonic and Inharmonic Analyses
Authors: Liu Haibo , Zhou Yan , Ray Jim , Luo Jiesi
Published: July 2022

Due to the conservation of global angular momentum, polar motion (PM) is dominated by global mass redistributions and relative motions in the atmosphere, oceans and land water at seasonal time scales. Thus, accurately measured PM data can be used to validate the general circulation models (GCMs) for the atmosphere, oceans and land water. This study aims to analyze geophysical excitations and observed excitations obtained from PM observations from both the harmonic and wavelet analysis perspectives, in order to refine our understanding of the geophysical excitation of PM. The geophysical excitations are derived from two sets of GCMs and a monthly gravity model combining satellite gravity data and some GCM outputs using the PM theory for an Earth model with frequency-dependent responses, while the observed excitation is obtained from the PM data using the frequency-domain Liouville’s equation. Our results show that wavelet analysis can reveal the time-varying nature of all excitations and identify when changes happen and how strong they are, while harmonic analysis can only show the average amplitudes and phases. In particular, the monthly gravity model can correct the mismodeled GCM outputs, while the Earth’s frequency-dependent responses provide us with a better understanding of atmosphere–ocean–land water–solid Earth interactions.
Machine Learning to Identify Three Types of Oceanic Fronts Associated with the Changjiang Diluted Water in the East China Sea between 1997 and 2021
Authors: Kim Dae-Won , Kim So-Hyun , Jo Young-Heon
Published: July 2022

Long-term sea surface salinity (SSS) in the East China Sea (ECS) was estimated based on Ocean Color Climate Change Initiative (OC-CCI) data using machine learning during the summer season (June to September) from 1997 to 2021. Changjiang diluted water (CDW) in the ECS propagates northeastward and forms longitudinally-oriented ocean fronts. To determine the CDW’s distribution, three fronts were investigated: (1) a CDW front based on chlorophyll-a concentration (Chl), SSS, and sea surface temperature (SST); (2) a CDW front based on sea surface density (SSD); and (3) a CDW front for nutrient distribution. The Chl fronts matched well with the SSS fronts, suggesting that Chl variation in the ECS is highly correlated with the CDW. Furthermore, the SSD fronts spatially matched well with nitrogen concentration. Sea level anomaly (SLA) variation with SSD was also detected, indicating that CDW had sufficiently large effects on SLA so that they may be detectable by altimeter measurements. This result suggests that the influence of steric height changes and the inflow from rivers are significant in the ECS. Additionally, the continuous long-term SSD developed in this study enables researchers to detect the CDW front and its influence on the ECS marine environment.
A Novel Sensor for Undrained Shear Strength Measurement in Very Soft to Soft Marine Sediments Based on Optical Frequency Domain Reflectometry Technology
Authors: Wu Pei-Chen , Chen Wen-Bo , Yin Jian-Hua , Pan Yu , Lou Kai , Feng Wei-Qiang
Published: July 2022

Due to the short supply of conventional fill materials, such as sand, land reclamation using dredged marine deposits has recently been proposed, in which marine deposits with high water content are blow-filled into reclaiming areas. The strength development of the filled marine soils is of great importance during the sedimentation and consolidation to guide the filling process and construction of reclamation. In this study, a novel sensor based on optical frequency domain reflectometry (OFDR) technology with a simple design was developed for undrained shear strength measurement. The novel sensor consists of an optical fiber and a series of polyoxymethylene coins. Owing to the merits of OFDR technology on high resolution, fully distributed sensing, and immunity to electromagnetic interference, the novel sensor can be used to determine undrained shear strength profiles of very soft to soft marine sediments/soils with good accuracy. The sensor was calibrated in remolded marine deposits with different water contents. The good feasibility and performance of the novel sensor for undrained shear strength measurement were well validated in two physical model tests on marine deposits treated by horizontal drains with vacuum preloading.
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging
Authors: Hauffen Jan Christian , Kästner Linh , Ahmadi Samim , Jung Peter , Caire Giuseppe , Ziegler Mathias
Published: July 2022

Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
Derivative Three-Dimensional Synchronous Fluorescence Analysis of Tear Fluid and Their Processing for the Diagnosis of Glaucoma
Authors: Dubayová Katarína , Krajčíková Kristína , Mareková Mária , Tomečková Vladimíra
Published: July 2022

Background: Sensitive and rapid diagnosis of the early stages of glaucoma from tear fluid is a great challenge for researchers. Methods: Tear fluid was analyzed using three-dimensional synchronous fluorescence spectroscopy (3D-SFS). Our previously published results briefly describe the main methods which applied the second derivative to a selected synchronous spectrum Δλ = 110 nm in distinguishing between healthy subjects (CTRL) and patients with glaucoma (POAG). Results: In this paper, a novel strategy was used to evaluate three-dimensional spectra from the tear fluid database of our patients. A series of synchronous excitation spectra were processed as a front view and presented as a single curve showcasing the overall fluorescence profile of the tear fluid. The second derivative spectrum provides two parameters that can enhance the distinction between CTRL and POAG tear fluid. Conclusions: Combining different types of 3D-SFS data can offer interesting and useful diagnostic tools and it can be used as input for machine learning and process automation.
Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review
Authors: Cui Guangzhen , Zhang Weili , Xiao Yanqiu , Yao Lei , Fang Zhanpeng
Published: July 2022

Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image–point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.
Estimation of Online State of Charge and State of Health Based on Neural Network Model Banks Using Lithium Batteries
Authors: Lee Jong-Hyun , Lee In-Soo
Published: July 2022

Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium–ion batteries in this study. The proposed method includes four neural network models—one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts.
A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
Authors: Wang Weiwei , Tian Ye , Bian Lang , Wang Guoyong , Meng Yansong , Zhang Lixin
Published: July 2022

Low Earth Orbit (LEO) satellites have stronger received signals and more rapid geometry changes than Global Navigation Satellite System (GNSS) satellites, making them attractive for positioning, navigation, and timing (PNT) applications. Due to the low altitude, the LEO constellation requires more satellites to cover the entire globe and more Pseudo Random Noise (PRN) codes to realize Code Division Multiple Access (CDMA), which means greater receiver storage resources and receiver acquisition time. In this paper, different from the traditional methods that assign a unique PRN code to each satellite, we propose a novel method in which several satellites share the same PRN code, and simply demonstrate the feasibility and benefits of this method. To determine the minimum number of PRN codes needed for a constellation, we build a mathematical model. After the algorithm comparison, we improve the recursive largest first (RLF) algorithm so that it has a higher running speed and a smaller approximate optimal solution within a certain time period. By studying polar-orbiting and walker constellations, we find that if other satellite parameters remain the same, the higher the orbital altitude is, the more PRN codes are needed, and no matter what the orbital inclination is, the minimum number of PRN codes remains the same. Overall, it is feasible and meaningful for several satellites sharing the same PRN code to save storage resources and reduce the satellite acquisition time of the receiver. If this new technology is applied, the storage resources and the average satellite acquisition time of the receiver will be, at most, one-third of previous ones.
Research on Forward Problem of Rail Detection Based on Magnetoacoustic Coupling
Authors: Huang Xin , Li Aijuan , Huang Zhen , Sun Yi , Song Yumin , Xu Ning
Published: July 2022

According to the characteristics of rail defects, a rail microcrack detection method based on magnetoacoustic coupling effect is proposed in this paper. Firstly, the basic principle of a rail microcrack detection method based on magnetoacoustic coupling effect is described, and then the model is analyzed theoretically. Through simulation calculation, the current density distribution and Lorentz force distribution generated by electromagnetic excitation, the motion characteristics of particles under Lorentz force and the sound field distribution characteristics of magnetoacoustic signals generated by Lorentz force are obtained. Finally, an experimental platform was set up and the steel ring model was preliminarily tested. The magnetic and acoustic signals of the two steel ring boundaries excited by an electromagnetic field were collected. These signals correspond to the position distribution of the steel ring. The state change of rail microstructure will cause a change in the conductivity characteristics of rail materials, and will affect the characteristics and distribution of sound pressure in the detection. Therefore, the detection method based on the magnetoacoustic coupling effect can detect the surface microcracks of high-speed rail. This method has great feasibility and development potential in the field of rail flaw detection.
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Authors: Levi Dan , Gispan Liran , Giladi Niv , Fetaya Ethan
Published: July 2022

Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications, and in particular, safety-critical ones. In this work, we study the calibration of uncertainty prediction for regression tasks which often arise in real-world systems. We show that the existing definition for the calibration of regression uncertainty has severe limitations in distinguishing informative from non-informative uncertainty predictions. We propose a new definition that escapes this caveat and an evaluation method using a simple histogram-based approach. Our method clusters examples with similar uncertainty prediction and compares the prediction with the empirical uncertainty on these examples. We also propose a simple, scaling-based calibration method that preforms as well as much more complex ones. We show results on both a synthetic, controlled problem and on the object detection bounding-box regression task using the COCO and KITTI datasets.
Ensemble Capsule Network with an Attention Mechanism for the Fault Diagnosis of Bearings from Imbalanced Data Samples
Authors: Xu Zengbing , Lee Carman , Lv Yaqiong , Chan Jeffery
Published: July 2022

In order to solve the problem of imbalanced and noisy data samples for the fault diagnosis of rolling bearings, a novel ensemble capsule network (Capsnet) with a convolutional block attention module (CBAM) that is based on a weighted majority voting method is proposed in this study. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was used to decompose the raw vibration signal into different IMF signals, which are noise reduction signals. Secondly, the IMF signals were input into the Capsnet with CBAM in order to diagnose the fault category preliminarily. Finally, the weighted majority voting method was utilized so as to fuse all of the preliminary diagnosis results in order to obtain the final diagnostic decision. In order to verify the effectiveness of the proposed ensemble of Capsnet with CBAM, this method was applied to the fault diagnosis of rolling bearings with imbalanced and different SNR data samples. The diagnostic results show that the proposed diagnostic method can achieve higher levels of accuracy than other methods, such as single CNN, single Capsnet, ensemble CNN and an ensemble capsule network without CBAM and that it has stronger immunity to noise than an ensemble capsule network without CBAM.
Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview
Authors: Al-Saedi Ahmed A. , Boeva Veselka , Casalicchio Emiliano , Exner Peter
Published: July 2022

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.
Applying Image Recognition and Tracking Methods for Fish Physiology Detection Based on a Visual Sensor
Authors: Liang Jia-Ming , Mishra Shashank , Cheng Yu-Lin
Published: July 2022

The proportion of pet keeping has increased significantly. According to the survey results of Business Next, the proportion of Taiwan families keeping pets was 70% in 2020. Among them, the total number of fish pets was close to 33% of the overall pet proportion. Therefore, aquarium pets have become indispensable companions for families. At present, many studies have discussed intelligent aquarium systems. Through image recognition based on visual sensors, we may be able to detect and interpret the physiological status of the fish according to their physiological appearance. In this way, it can help to notify the owner as soon as possible to treat the fish or isolate them individually, so as to avoid the spread of infection. However, most aquarium pets are kept in groups. Traditional image recognition technologies often fail to recognize each fish’s physiological states precisely because of fish swimming behaviors, such as grouping swimming, shading with each other, flipping over, and so on. In view of this, this paper tries to address such problems and then proposes a practical scheme, which includes three phases. Specifically, the first phase tries to enhance the image recognition model for small features based on the prioritizing rules, thus improving the instant recognition capability. Then, the second phase exploits a designed fish-ID tracking mechanism and analyzes the physiological state of the same fish-ID through coherent frames, which can avoid temporal misidentification. Finally, the third phase leverages a fish-ID correction mechanism, which can detect and correct their IDs periodically and dynamically to avoid tracking confusion, and thus potentially improve the recognition accuracy. According to the experiment results, it was verified that our scheme has better recognition performance. The best accuracy and correctness ratio can reach up to 94.9% and 92.67%, which are improved at least 8.41% and 26.95%, respectively, as compared with the existing schemes.
Using Nonlinear Vibroartrographic Parameters for Age-Related Changes Assessment in Knee Arthrokinematics
Authors: Kręcisz Krzysztof , Bączkowicz Dawid , Kawala-Sterniuk Aleksandra
Published: July 2022

Changes in articular surfaces can be associated with the aging process and as such may lead to quantitative and qualitative impairment of joint motion. This study is aiming to evaluate the age-related quality of the knee joint arthrokinematic motion using nonlinear parameters of the vibroarthrographic (VAG) signal. To analyse the age-related quality of the patellofemoral joint (PFJ), motion vibroarthrography was used. The data that were subject to analysis represent 220 participants divided into five age groups. The VAG signals were acquired during flexion/extension knee motion and described with the following nonlinear parameters: recurrence rate (RR) and multi-scale entropy (MSE). RR and MSE decrease almost in a linear way with age (main effects of group p<0.001; means (SD): RR=0.101(0.057)−0.020(0.017); and MSE=20.9(8.56)−13.6(6.24)). The RR post-hoc analysis showed that there were statistically significant differences (p<0.01) in all comparisons with the exception of the 5th–6th life decade. For MSE, statistically significant differences (p<0.01) occurred for: 3rd–7th, 4th–7th, 5th–7th and 6th life decades. Our results imply that degenerative age-related changes are associated with lower repeatability, greater heterogeneity in state space dynamics, and greater regularity in the time domain of VAG signal. In comparison with linear VAG measures, our results provide additional information about the nature of changes of the vibration dynamics of PFJ motion with age.
Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System
Authors: Kang Sung-wook , Jang Min-ho , Lee Seongwook
Published: July 2022

In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.
A Frequency-Reconfigurable Filtenna for GSM, 4G-LTE, ISM, and 5G Sub-6 GHz Band Applications
Authors: Awan Wahaj Abbas , Hussain Niamat , Kim Sunggoo , Kim Nam
Published: July 2022

This paper presents the design and realization of a flexible and frequency-reconfigurable antenna with harmonic suppression for multiple wireless applications. The antenna structure is derived from a quarter-wave monopole by etching slots. Afterward, the high-order unwanted harmonics are eliminated by adding a filtering stub to the feedline to avoid signal interference. Lastly, frequency reconfigurability is achieved using pin diodes by connecting and disconnecting the stubs and the rectangular patch. The antenna is fabricated on the commercially available thin (0.254 mm) conformal substrate of Rogers RT5880. The proposed antenna resonates (|S11| < –10 dB) at five different reconfigurable bands of 3.5 GHz (3.17–3.82 GHz), 2.45 GHz (2.27–2.64 GHz), 2.1 GHz (2.02–2.29 GHz), 1.9 GHz (1.81–2.05 GHz), and 1.8 GHz (1.66–1.93 GHz), which are globally used for 5G sub-6 GHz in industrial, medical, and scientific (ISM) bands, 4G long-term evolution (LTE) bands, and global system for mobile communication (GSM) bands. The simulated and measured results show that the antenna offers excellent performance in terms of good impedance matching with controllable resonant bands, high gain (>2 dBi), stable radiation patterns, and efficiency (>87%). Moreover, the conformal analysis shows that the antenna retains its performance both in flat and bending conditions, making it suitable for flexible electronics. In addition, the antenna is compared with the state-of-the-art works for similar applications to show its potential for the targeted band spectrums.
Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals
Authors: Li Xilin , Leung Frank H. F. , Su Steven , Ling Sai Ho
Published: July 2022

Introduction: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. Methods: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. Results: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60 s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.
An All-Fiber Optical Sensor Combined with FBG and CFBG-FP for Attitude Estimation
Authors: Li Yifan , Xiong Shuidong , Xia Ji
Published: July 2022

In order to meet the needs of attitude correction of the optical fiber sensor array, an all-fiber optical sensor combined with FBG and CFBG-FP is proposed to monitor its attitude angle. The FBG sensor for pitch and roll angles detection employs a geometric model solution to shift the center wavelength of the three fiber Bragg gratings connected to it through the mass sphere, allowing for pitch and roll angle detection. Based on the magnetostrictive effect, the CFBG-FP sensor for heading angle detection employs changes of the cavity length of the chirped grating Fabry-Pérot cavity connected to its surface. The method of interpolation data processing and derivation of the attitude angle matrix is used to realize heading angle detection. The experimental results indicate that the FBG sensor for the pitch and roll angles detection has an effective detection range of 180 degrees and an angle measurement error of less than 1.5 degrees. Meanwhile, in the region beyond 10 degrees in the geomagnetic north-south direction, the angle measurement error of the CFBG-FP sensor for the heading angle detection is less than 1.3 degrees. Moreover, the impact of temperature in the system is minimized, demonstrating the validity of this combined fiber optical approach in the attitude angle measurements.
Apoptosis Induction Associated with Enhanced ER Stress Response and Up-Regulation of c-Jun/p38 MAPK Proteins in Human Cervical Cancer Cells by Colocasia esculenta var. aquatilis Hassk Extract
Authors: Chomlamay Natharika , Poorahong Watcharaporn , Innajak Sukanda , Watanapokasin Ramida
Published: July 2022

Colocasia esculenta var. Aquatilis Hassk, elephant ear (CF-EE) has been widely used as traditional food and medicine. It also shows other therapeutic properties, such as antimicrobial and anti-cancer activity. In this study, we aim to investigate the effect of CF-EE extract on apoptosis induction associated with ER stress in cervical cancer HeLa cells. Cell viability was determined by MTT assay. Assessments of nuclear morphological changes, mitochondrial membrane potential, and intracellular reactive oxygen species (ROS) production were conducted by hoeshst33342, JC-1, and DCFH-DA fluorescence staining, respectively. Sub-G1 DNA content was analyzed by flow cytometry, and protein expression was determined by Western blotting. The results demonstrate that CF-EE extract suppressed HeLa cell growth and induced nuclear condensation and apoptotic bodies. There was also a loss of mitochondrial membrane potential and increased apoptosis marker protein expression, including Bax, cleaved-caspase-7, and cleaved-PARP. In addition, the results show that CF-EE extract induced ROS, increased ER stress proteins (GRP78 and CHOP), enhanced p38 and c-Jun phosphorylation, and inhibited Akt expression in HeLa cells. In summary, CF-EE extract induced apoptotic cell death-associated ROS-induced ER stress and the MAPK/AKT signaling pathway. Therefore, CF-EE extract has anticancer therapeutic potential for cervical cancer treatment in the future.
Diversified Organizational Inequality Regimes and Ideal Workers in a “Growth-Driven,” “Diverse,” “Flexible” Australian Company: A Multilevel Grounded Theory
Authors: Turnbull Beth , Graham Melissa , Taket Ann
Published: July 2022

Interacting global, societal and organizational contexts produce unique organizational inequality regimes. This paper aims to understand multilevel processes influencing gendered, classed, raced and aged inequality regimes and worker hierarchies within “ComCo”, an Australian subsidiary of a multinational company. Our qualitative critical feminist-grounded theory approach triangulated organizational documentation, employee interviews and open-ended questionnaire responses. The emergent theory suggested that ComCo’s globally and societally embedded neoliberal-capitalist–masculine growth imperative produced no longer simplistically one-sided, but multifaceted and diversified masculine–individual–white and feminine–collaborative–colored growth mechanisms, including ideal workers broadening from quantitatively extreme to qualitatively conformant qualities and practices, to constitute not merely unencumbered masculine, but all workers, as existing for company growth. However, feminine–collective–colored mechanisms, co-opted to supporting growth, remained subordinated to masculine–individual–white mechanisms constructed as more effective at delivering growth, reinforcing ComCo’s inequality regimes and worker hierarchies despite diversity initiatives. Organizations must identify and address processes reinforcing inequality regimes to genuinely promote employment equity and diversity.
Fostering Policy Change in Anti-Poverty Schemes in Italy: Still a Long Way to Go
Authors: Maino Franca , De Tommaso Celestina Valeria
Published: July 2022

This article explores the poverty phenomenon and anti-poverty policies in Italy, before and after the COVID-19 pandemic. It aims to contribute to the mainstream literature on policy change, looking at how the convergence of multiple streams (problem, policy, and political) contributed to achieving the adoption of the Italian Minimum Income scheme, the Citizenship Income. Despite increasing political and public awareness of poverty, the 2022 Budget Law failed to achieve a structural reform—considering amendments proposed by the Ministry of Social Policy’s Commission and the Italian Anti-Poverty Network—to improve both the equity and efficiency of the anti-poverty measure. Strong path dependency in the conceptualization and implementation of the anti-poverty tool is still evident; policy change thus has a long way to go.
Effect of Corporate Social Responsibility on Consumer Satisfaction and Consumer Loyalty of Private Banking Companies in Peru
Authors: Leclercq-Machado Luigi , Alvarez-Risco Aldo , Esquerre-Botton Sharon , Almanza-Cruz Camila , de las Mercedes Anderson-Seminario Maria , Del-Aguila-Arcentales Shyla , Yáñez Jaime A.
Published: July 2022

This study aimed to determine the effect of corporate social responsibility (CSR) on consumer satisfaction and loyalty in the Peruvian private banking sector. A total of 390 bank consumers fulfilled an online survey. It evaluated the effect of corporate social responsibility (CSR), customer satisfaction (CS), and customer trust (CT) on customer loyalty. The results suggest that CSR and customer satisfaction, through customer trust, have a positive effect on customer loyalty. The model explained 63.6% of customer loyalty. Outcomes of the bootstrapping test showed that the path coefficients were significant. The research findings may help bank managers to understand customers’ satisfaction and trust, which can create a preference and loyalty for their firms. The study’s novelty is based on the use of the partial least square structural equation modeling technique (PLS-SEM) to evaluate CSR in the financial sector in Peru.
A Correlation Analysis Method for Geographical Object Flows from a Geoeconomic Perspective
Authors: Zhao Wenshuang , Jiang Nan , Yu Xinkai , Chen Yunhai , Zhao Xinke
Published: July 2022

Geographic object flow is the reason behind the relationship of geographic units. There are interactions in the process of dynamic change of a geographic object flow, and its regularity, which can reflect the relationship or pattern of geographic units in a region. In this paper, an association rule mining method for the geographic object flow linkage process is studied from a geoeconomics perspective. Additionally, an association rule mining algorithm with hierarchical constraints is proposed. Data segmentation is performed according to the time series characteristics of geographic object flow data. The basic attributes for the association rule mining are determined based on the basic parameters of geographic object flows, and a database for the association rule mining is formed according to the characteristics of the hierarchical structure of the geographic object flows. Based on the obtained data, the association rule mining algorithm with hierarchical constraints obtained using a parent–child matrix is improved by adding the Apriori algorithm. With the Indo-Pacific region as an example, the trade flow association rules for 25 countries in the region from 2010 to 2021 are selected. In addition, a mathematical statistical analysis of the strongly associated mined trade flows and geoeconomic factors is conducted in terms of both a basic feature analysis of trade flow associations and a country-oriented trade flow association analysis by considering domain knowledge. The effectiveness of the method has been evaluated from various perspectives such as correlation analysis, mathematical statistics, and comparison with the findings of existing studies and proved the validity of the method.
Biomass-Based Oxygen Reduction Reaction Catalysts from the Perspective of Ecological Aesthetics—Duckweed Has More Advantages than Soybean
Authors: Zhang Meiping , Zhang Yanqi , Cui Jiajia , Zhang Zongyao , Yan Zaoxue
Published: July 2022

Ecological aesthetics encourages the harmonization of humans and nature. In this paper, we integrate ecological aesthetics into the development of oxygen reduction reaction (ORR) catalysts of H2/O2 fuel cells. Moldy soybean and duckweed as raw materials are adopted to prepare biomass-based ORR catalysts, both of which have advantages in activity, stability, environmental protection and resource richness over the conventional expensive and scarce noble metal-based catalysts. Therefore, duckweed is more environmentally friendly, entails a simpler preparation process and has a better catalytic performance, ultimately being more in line with ecological aesthetics.
Perceived Walkability and Respective Urban Determinants: Insights from Bologna and Porto
Authors: Fonseca Fernando , Papageorgiou George , Tondelli Simona , Ribeiro Paulo , Conticelli Elisa , Jabbari Mona , Ramos Rui
Published: July 2022

Walking is undoubtedly a sustainable and healthy mode of transport. However, the decision to walk is influenced by many built environment and streetscape attributes. Specifically, the term walkability is used to describe the extent to which the urban environment is pedestrian-friendly, usually by quantifying multiple built environment attributes at the neighbourhood scale. The present study adopts a qualitative approach to evaluate perceived walkability. Based on a questionnaire (n = 1438) administered in the cities of Bologna and Porto, this paper analyses how respondents perceived and evaluated 19 built environment and streetscape attributes. An Exploratory Factor Analysis was carried out to examine the correlations between the various attributes and to identify the underlying walkability determinants. The analysis indicated that 13 attributes were highly correlated, resulting in four determinants: (i) urban ambiance, which includes land use and street design attributes, such as land use mix, enclosure, transparency, and architectural and landscape diversity; (ii) pedestrian infrastructure, which is related to sidewalk conditions; (iii) street connectivity and proximity to community facilities; and iv) access to other modes of transport. In turn, traffic safety and security were not correlated with perceived walkability in both cities. These findings suggest that specific urban design and pedestrian infrastructure attributes should be highly considered when formulating policies aiming to create more pedestrian-friendly cities, as well as in walkability studies and when developing walkability scores and indexes.
Issues Concerning Interfaces with Inorganic Solid Electrolytes in All-Solid-State Lithium Metal Batteries
Authors: Sun Zhouting , Liu Mingyi , Zhu Yong , Xu Ruochen , Chen Zhiqiang , Zhang Peng , Lu Zeyu , Wang Pengcheng , Wang Chengrui
Published: July 2022

All-solid-state batteries have attracted wide attention for high-performance and safe batteries. The combination of solid electrolytes and lithium metal anodes makes high-energy batteries practical for next-generation high-performance devices. However, when a solid electrolyte replaces the liquid electrolyte, many different interface/interphase issues have arisen from the contact with electrodes. Poor wettability and unstable chemical/electrochemical reaction at the interfaces with lithium metal anodes will lead to poor lithium diffusion kinetics and combustion of fresh lithium and active materials in the electrolyte. Element cross-diffusion and charge layer formation at the interfaces with cathodes also impede the lithium ionic conductivity and increase the charge transfer resistance. The abovementioned interface issues hinder the electrochemical performance of all-solid-state lithium metal batteries. This review demonstrates the formation and mechanism of these interface issues between solid electrolytes and anodes/cathodes. Aiming to address the problems, we review and propose modification strategies to weaken interface resistance and improve the electrochemical performance of all-solid-state lithium metal batteries.