bluuebunny/crossref_metadata_2025 huggingface dataset:
JPSS VIIRS SST Reanalysis Version 3
Authors: Jonasson Olafur , Ignatov Alexander , Pryamitsyn Victor , Petrenko Boris , Kihai Yury
Published: July 2022

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, ‘subskin’ (sensitive to skin SST) and ‘depth’ (proxy for in situ SST at depth of 20 cm), were produced from brightness temperatures (BTs) in the VIIRS bands centered at 8.6, 11 and 12 µm during the daytime and an additional 3.7 µm band at night, using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. The RAN3 dataset is fully archived at NASA JPL PO.DAAC and NOAA CoastWatch, and routinely supplemented in near real time (NRT) with a latency of a few hours. Delayed mode (DM) processing with a 2 months latency follows NRT, resulting in a more uniform science quality SST record. This paper documents and evaluates the performance of the VIIRS RAN3 dataset. Comparisons with in situ SSTs from drifters and tropical moorings (D+TM) as well as Argo floats (AFs) (both available from the NOAA iQuam system) show good agreement, generally within the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), in a clear-sky domain covering 18–20% of the global ocean. The nighttime SSTs compare with in situ data more closely, as expected due to the reduced diurnal thermocline. The daytime SSTs are also generally within NOAA specs but show some differences between the (D+TM) and AF validations as well as residual drift on the order of −0.1 K/decade. BT comparisons between two VIIRSs and MODIS-Aqua show good consistency in the 3.7 and 12 µm bands. The 11 µm band, while consistent between NPP and N20, shows residual drift with respect to MODIS-Aqua. Similar analyses of the 8.6 µm band are inconclusive, as the performance of the MODIS band 29 centered at 8.6 µm is degraded and unstable in time and cannot be used for comparisons.
Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China
Authors: Liu Deqiang , Huang Chuanrong , Feng Jie
Published: July 2022

This study quantitatively examines the contribution of assimilating observations in the regions with different dynamic instabilities to the analysis and prediction of an extreme rainstorm event in Fujian Province of China. The wind profiling radar (WPR) observations are classified into two groups, i.e., strong and weak instability areas (SIA and WIA), according to their local dynamic instability identified by the ensemble spread. Their performance of assimilation and prediction in terms of the wind and precipitation are evaluated and compared in detail. The results show that the wind analysis error by assimilating all of the WPR observations can be reduced by about 30%. In particular, the wind analysis errors by only assimilating the observations in the SIA are about 12% lower than those in the WIA. They are related to the existence of the low-level horizontal wind shear with strong instability in the SIA. The case study shows that the assimilation of observations in the SIA can effectively correct the wind fields on the two sides of the wind shear line, producing an improved precipitation forecast compared to observation assimilation in the WIA.
Water Quality Measurement and Modelling Based on Deep Learning Techniques: Case Study for the Parameter of Secchi Disk
Authors: Lin Feng , Gan Libo , Jin Qiannan , You Aiju , Hua Lei
Published: July 2022

The Secchi disk is often used to monitor the transparency of water. However, the results of personal measurement are easily affected by subjective experience and objective environment, and it is time-consuming. With the rapid development of computer technology, using image processing technology is more objective and accurate than personal observation. A transparency measurement algorithm is proposed by combining deep learning, image processing technology, and Secchi disk measurement. The white part of the Secchi disk is cropped by image processing. The classification network based on resnet18 is applied to classify the segmentation results and determine the critical position of the Secchi disk. Then, the semantic segmentation network Deeplabv3+ is used to segment the corresponding water gauge at this position, and subsequently segment the characters on the water gauge. The segmentation results are classified by the classification network based on resnet18. Finally, the transparency value is calculated according to the segmentation and classification results. The results from this algorithm are more accurate and objective than that of personal observation. The experiments show the effectiveness of this algorithm.
LAP-IoHT: A Lightweight Authentication Protocol for the Internet of Health Things
Authors: Chen Chien-Ming , Chen Zhaoting , Kumari Saru , Lin Meng-Chang
Published: July 2022

The Internet of Health Things (IoHT), which is an extension of the Internet of Things (IoT) in healthcare, has provided a new type of telemedicine approach. In IoHT, wearable sensors are used to collect patient health data, and information is transmitted remotely to doctors who can develop accurate treatment plans and provide timely telemedicine services to patients. However, patient health data are transmitted over a public channel, which means that the privacy and medical data of patients are at significant risk of leakage and can be confronted by serious security problems. We proposed a lightweight authentication protocol known as LAP-IoHT for IoHT environments to overcome the various threats that are currently faced by IoHT. We verified the security of LAP-IoHT using a Real-or-Random model and demonstrated its significant performance advantage by conducting a comparative analysis with other similar protocols for a better adaptation to the IoHT environment.
Detecting and Visualizing Stops in Dance Training by Neural Network Based on Velocity and Acceleration
Authors: Jin Yuuki , Suzuki Genki , Shioya Hiroyuki
Published: July 2022

Various genres of dance, such as Yosakoi Soran, have contributed to the health of many people and contributed to their sense of belonging to a community. However, due to the effects of COVID-19, various face-to-face activities have been restricted and group dance practice has become difficult. Hence, there is a need to facilitate remote dance practice. In this paper, we propose a system for detecting and visualizing the very important dance motions known as stops. We measure dance movements by motion capture and calculate the features of each movement based on velocity and acceleration. Using a neural network to learn motion features, the system detects stops and visualizes them using a human-like 3D model. In an experiment using dance data, the proposed method obtained highly accurate stop detection results and demonstrated its effectiveness as an information and communication technology support for remote group dance practice.
Improvement of Distance Measurement Based on Dispersive Interferometry Using Femtosecond Optical Frequency Comb
Authors: Niu Qiong , Song Mingyu , Zheng Jihui , Jia Linhua , Liu Junchen , Ni Lingman , Nian Ju , Cheng Xingrui , Zhang Fumin , Qu Xinghua
Published: July 2022

Since the dispersive interferometry (DPI) based on optical frequency combs (OFCs) was proposed, it has been widely used in absolute distance measurements with long-distance and high precision. However, it has a serious problem for the traditional DPI based on the mode-locked OFC. The error of measurements caused by using the fast Fourier transform (FFT) algorithm to process signals cannot be overcome, which is due to the non-uniform sampling intervals in the frequency domain of spectrometers. Therefore, in this paper, we propose a new mathematical model with a simple form of OFC to simulate and analyze various properties of the OFC and the principle of DPI. Moreover, we carry out an experimental verification, in which we adopt the Lomb–Scargle algorithm to improve the accuracy of measurements of DPI. The results show that the Lomb–Scargle algorithm can effectively reduce the error caused by the resolution, and the error of absolute distance measurement is less than 12 μm in the distance of 70 m based on the mode-locked OFC.
Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers
Authors: Erdoğan Sinem Burcu , Yükselen Gülnaz
Published: July 2022

Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.
A Composite DC–DC Converter Based on the Versatile Buck–Boost Topology for Electric Vehicle Applications
Authors: González-Castaño Catalina , Restrepo Carlos , Flores-Bahamonde Freddy , Rodriguez Jose
Published: July 2022

The composite converter allows integrating the high-efficiency converter modules to achieve superior efficiency performance, becoming a prominent solution for electric transport power conversion. In this work, the versatile buck–boost dc–dc converter is proposed to be integrated into an electric vehicle composite architecture that requires a wide voltage range in the dc link to improve the electric motor efficiency. The inductor core of this versatile buck–boost converter has been redesigned for high voltage applications. The versatile buck–boost converter module of the composite architecture is in charge of the control stage. It provides a dc bus voltage regulation at a wide voltage operation range, which requires step-up (boost) and step-down (buck) operating modes. The PLECS thermal simulation of the composite architecture shows a superior power conversion efficiency of the proposed topology over the well-known classical noninverting buck–boost converter under the same operating conditions. The obtained results have been validated via experimental efficiency measures and experimental transient responses of the versatile buck–boost converter. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 4.4 kW powertrain is presented using a PLECS RT Box 1 device. The HIL simulation results verified the accuracy of the theoretical analysis and the effectiveness of the proposed architecture.
A Wi-Fi Indoor Positioning Method Based on an Integration of EMDT and WKNN
Authors: Zhou Rong , Meng Fengying , Zhou Jing , Teng Jing
Published: July 2022

In indoor positioning, signal fluctuation is one of the main factors affecting positioning accuracy. To solve this problem, a new method based on an integration of the empirical mode decomposition threshold smoothing method (EMDT) and improved weighted K nearest neighbor (WKNN), named EMDT-WKNN, is proposed in this paper. First, the nonlinear and non-stationary received signal strength indication (RSSI) sequences are constructed. Secondly, intrinsic mode functions (IMF) selection criteria based on energy analysis method and fluctuation coefficients is proposed. Thirdly, the EMDT method is employed to smooth the RSSI fluctuation. Finally, to further avoid the influence of RSSI fluctuation on the positioning accuracy, the deviated matching points are removed, and more precise combined weights are constructed by combining the geometric distance of the matching points and the Euclidean distance of fingerprints in the positioning method-WKNN. The experimental results show that, on an underground parking dataset, the positioning accuracy based on EMDT-WKNN can reach 1.73 m in the 75th percentile positioning error, which is 27.6% better than 2.39 m of the original RSSI positioning method.
Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features
Authors: Chen Yanli , Chen Zhipeng , Gui Weihua , Yang Chunhua
Published: July 2022

Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.
Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN
Authors: Ahang Maryam , Jalayer Masoud , Shojaeinasab Ardeshir , Ogunfowora Oluwaseyi , Charter Todd , Najjaran Homayoun
Published: July 2022

Bearings are vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring are essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data are ample as systems usually work in desired conditions. On the other hand, fault data are rare, and in many conditions, there are no data recorded for the fault classes. Accessing fault data is crucial for developing data-driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on conditional generative adversarial networks (CGANs) was introduced. Trained on the normal and fault data on actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method was validated on a real-world bearing dataset, and fault data were generated for different conditions. Several state-of-the-art classifiers and visualization models were implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.
Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
Authors: Santos Rodrigues Ana , Augustauskas Rytis , Lukoševičius Mantas , Laguna Pablo , Marozas Vaidotas
Published: July 2022

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector’s coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.
Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing
Authors: Jia Xiangxiang , Guo Baofeng
Published: July 2022

Hyperspectral unmixing (HU) is a technique for estimating a set of pure source signals (end members) and their proportions (abundances) from each pixel of the hyperspectral image. Non-negative matrix factorization (NMF) can decompose the observation matrix into the product of two non-negative matrices simultaneously and can be used in HU. Unfortunately, a limitation of many traditional NMF-based methods, i.e., the non-convexity of the objective function, may lead to a sub-optimal solution. Thus, we put forward a new unmixing method based on NMF under smoothing and sparse constraints to obtain a better solution. First, considering the sparseness of the abundance matrix, a weight sparse regularization is introduced into the NMF model to ensure the sparseness of the abundance matrix. Second, according to the similarity prior of the same feature in the adjacent pixels, a Total Variation regularization is further added to the NMF model to improve the smoothness of the abundance map. Finally, the signatures of each end member are modified smoothly in spectral space. Moreover, it is noticed that discontinuities may emerge due to the removal of noisy bands. Therefore, the spectral data are piecewise smooth in spectral space. Then, in this paper, a piecewise smoothness constraint is further applied to each column of the end-member matrix. Experiments are conducted to evaluate the effectiveness of the proposed method based on two different datasets, including a synthetic dataset and the real-life Cuprite dataset, respectively. Experimental results show that the proposed method outperforms several state-of-the-art HU methods. In the Cuprite hyperspectral dataset, the proposed method’s Spectral Angle Distance is 0.1694, compared to the TV-RSNMF method’s 0.1703, L1/2NMF method’s 0.1925, and VCA-FCLS method’s 0.1872.
Unified End-to-End YOLOv5-HR-TCM Framework for Automatic 2D/3D Human Pose Estimation for Real-Time Applications
Authors: Nguyen Hung-Cuong , Nguyen Thi-Hao , Scherer Rafal , Le Van-Hung
Published: July 2022

Three-dimensional human pose estimation is widely applied in sports, robotics, and healthcare. In the past five years, the number of CNN-based studies for 3D human pose estimation has been numerous and has yielded impressive results. However, studies often focus only on improving the accuracy of the estimation results. In this paper, we propose a fast, unified end-to-end model for estimating 3D human pose, called YOLOv5-HR-TCM (YOLOv5-HRet-Temporal Convolution Model). Our proposed model is based on the 2D to 3D lifting approach for 3D human pose estimation while taking care of each step in the estimation process, such as person detection, 2D human pose estimation, and 3D human pose estimation. The proposed model is a combination of best practices at each stage. Our proposed model is evaluated on the Human 3.6M dataset and compared with other methods at each step. The method achieves high accuracy, not sacrificing processing speed. The estimated time of the whole process is 3.146 FPS on a low-end computer. In particular, we propose a sports scoring application based on the deviation angle between the estimated 3D human posture and the standard (reference) origin. The average deviation angle evaluated on the Human 3.6M dataset (Protocol #1–Pro #1) is 8.2 degrees.
Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework
Authors: Shen Zhongxia , Li Gang , Fang Jiaqi , Zhong Hongyang , Wang Jie , Sun Yu , Shen Xinhua
Published: July 2022

Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized anxiety disorder (GAD). To this end, this study intends to develop an analysis framework for automatic GAD detection through incorporating multidimensional EEG feature extraction and machine learning techniques. Specifically, resting-state EEG signals with a duration of 10 min were obtained from 45 patients with GAD and 36 healthy controls (HC). Then, an analysis framework of multidimensional EEG characteristics (including univariate power spectral density (PSD) and fuzzy entropy (FE), and multivariate functional connectivity (FC), which can decode the EEG information from three different dimensions) were introduced for extracting aberrated multidimensional EEG features via statistical inter-group comparisons. These aberrated features were subsequently fused and fed into three previously validated machine learning methods to evaluate classification performance for automatic patient detection. We showed that patients exhibited a significant increase in beta rhythm and decrease in alpha1 rhythm of PSD, together with the reduced long-range FC between frontal and other brain areas in all frequency bands. Moreover, these aberrated features contributed to a very good classification performance with 97.83 ± 0.40% of accuracy, 97.55 ± 0.31% of sensitivity, 97.78 ± 0.36% of specificity, and 97.95 ± 0.17% of F1. These findings corroborate previous hypothesis of disconnectivity in psychiatric disorders and further shed light on distribution patterns of aberrant spatio-spectral EEG characteristics, which may lead to potential application of automatic diagnosis of GAD.
Calibration of Radar RCS Measurement Errors by Observing the Luneburg Lens Onboard the LEO Satellite
Authors: Yang Jie , Li Ning , Ma Pengbin , Liu Bin
Published: July 2022

Accurate radar RCS measurements are critical to the feature recognition of spatial targets. A calibration method for radar RCS measurement errors is proposed for the first time in the context of special target tracking by observing the Luneburg Lens onboard the LEO satellite. The Luneburg Lens has favorable RCS scattering properties for the radar microwave. Thus, the laboratory RCS measurements of the Luneburg Lens, with some fixed incident frequency and with different incident orientations for the radar microwave, will be implemented in order to build a database. The incident orientation for the radar microwave in the satellite body frame will be calculated by taking advantage of the precise orbit parameters, with errors only at the magnitude of several centimeters and within the actual satellite attitude parameters. According to the incident orientation, the referenced RCS measurements can be effectively obtained by the bilinear interpolation in the database. The errors of actual RCS measurements can thus be calibrated by comparing the referenced and the actual RCS measurements. In the RCS measurement experiment, which lasts less than 400 s, the actual RCS measurement errors of the Luneburg Lens are nearly less than 0 dBsm, which indicates that the RCS measurement errors of the spatial targets can be effectively calculated by the proposed calibration method. After the elaborated calibration, the RCS measurements of the spatial targets can be accurately obtained by radar tracking.
A Transmission Efficiency Evaluation Method of Adaptive Coding Modulation for Ka-Band Data-Transmission of LEO EO Satellites
Authors: Wang Zhongguo , Lu Fan , Wang Dabao , Zhang Xiao , Li Jionghui , Li Jindong
Published: July 2022

Nowadays low Earth orbit (LEO) Earth observation (EO) satellites commonly use constant coding modulation (CCM) or variable coding modulation (VCM) schemes for data transmission to ground stations (G/S). Compared with CCM and VCM, the adaptive coding modulation (ACM) could further improve the data throughput of the link by making full use of link resource and the time-varying characteristics of atmospheric attenuation. In order to comprehensively study the data transmission performance, one new index which could be utilized as a quantitative index for the satellite-to-ground data transmission scheme selection, the transmission efficiency factor (TEF) of LEO satellites is proposed and defined as “the product of the link availability and the average useful data rate”. Then, the transmission efficiency of CCM, VCM and ACM at typical G/S with different weather characteristics at Ka-band is compared and analyzed. The results show that ACM is more suitable for the G/S with moderate and abundant rainfall. Compared with the CCM of MCS 28, for Beijing G/S and Sanya G/S, ACM not only improves the transmission efficiency with the TEF increased by 3.62% and 24.51%, respectively, but also improves the link availability with the outage period reduced by 82.47% and 75.18%, respectively.
ConTra Preference Language: Privacy Preference Unification via Privacy Interfaces
Authors: Becher Stefan , Gerl Armin
Published: July 2022

After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.
Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models
Authors: Othman Esam , Mahmoud Muhammad , Dhahri Habib , Abdulkader Hatem , Mahmood Awais , Ibrahim Mina
Published: July 2022

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.
Regularization Meets Enhanced Multi-Stage Fusion Features: Making CNN More Robust against White-Box Adversarial Attacks
Authors: Zhang Jiahuan , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: July 2022

Regularization has become an important method in adversarial defense. However, the existing regularization-based defense methods do not discuss which features in convolutional neural networks (CNN) are more suitable for regularization. Thus, in this paper, we propose a multi-stage feature fusion network with a feature regularization operation, which is called Enhanced Multi-Stage Feature Fusion Network (EMSF2Net). EMSF2Net mainly combines three parts: multi-stage feature enhancement (MSFE), multi-stage feature fusion (MSF2), and regularization. Specifically, MSFE aims to obtain enhanced and expressive features in each stage by multiplying the features of each channel; MSF2 aims to fuse the enhanced features of different stages to further enrich the information of the feature, and the regularization part can regularize the fused and original features during the training process. EMSF2Net has proved that if the regularization term of the enhanced multi-stage feature is added, the adversarial robustness of CNN will be significantly improved. The experimental results on extensive white-box attacks on the CIFAR-10 dataset illustrate the robustness and effectiveness of the proposed method.
Chatter Monitoring of Machining Center Using Head Stock Structural Vibration Analyzed with a 1D Convolutional Neural Network
Authors: Jeong Kwanghun , Seong Yeonuk , Jeon Jonghoon , Moon Seongjun , Park Junhong
Published: July 2022

Real-time chatter detection is crucial for the milling process to maintain the workpiece surface quality and minimize the generation of defective parts. In this study, we propose a new methodology based on the measurement of machine head stock structural vibration. A short-pass lifter was applied to the cepstrum to effectively remove components resulting from spindle rotations and to extract structural vibration modal components of the machine. The vibration modal components include information about the wave propagation from the cutter impact to the head stock. The force excitation from the interactions between the cutter and workpiece induces structural vibrations of the head stock. The vibration magnitude for the rigid body modes was smaller in the chatter state compared to that in the stable state. The opposite variation was observed for the bending modes. The liftered spectrum was used to obtain this dependence of vibration on the cutting states. The one-dimensional convolutional neural network extracted the required features from the liftered spectrum for pattern recognition. The classified features allowed demarcation between the stable and chatter states. The chatter detection efficiency was demonstrated by application to the machining process using different cutting parameters. The classification performance of the proposed method was verified with comparison between different classifiers.
Lecturers’ Basic Psychological Needs Satisfaction and Its Relationship with Students’ Disposition towards Studying during Online Teaching in the COVID-19 Pandemic
Authors: Abello-Riquelme Rubén , Del Valle Milenko , López-Angulo Yaranay , Sanhueza-Campos Cristian , Contreras-Soto Yasmina
Published: July 2022

When examining learning at university, especially in a pandemic context, it is important to analyze the actions taken by lecturers to promote students’ disposition towards studying. The objectives of this research project were to analyze the activities lecturers implement in class to promote their students’ disposition towards studying; examine lecturers’ basic psychological needs satisfaction levels; and determine the relationship between feedback, beliefs, and affect for self-regulation and lecturers’ basic psychological needs satisfaction. A quantitative methodological approach, with a descriptive, correlational, and cross-sectional design was used, with a sample of 97 lecturers from Chilean universities. Data collection took place through a battery of instruments: the promotion of disposition towards studying survey; the feedback scale for self-regulation; the beliefs and affect in self-regulation survey; the satisfaction of basic psychological needs survey; and a sociodemographic questionnaire. The results showed that the promotion of disposition towards studying was a key factor to face social distancing and the loss of self-regulation for learning because of the COVID-19 pandemic. In addition, a positive correlation was found between beliefs and affect in relation to self-regulation and satisfaction levels of basic psychological needs in the participating lecturers.
Evaluation of Resilience Parameters of Soybean Oil-Modified and Unmodified Warm-Mix Asphalts—A Way Forward towards Sustainable Pavements
Authors: Tarar Muhammad Akhtar , Khan Ammad Hassan , Rehman Zia ur , Abbass Wasim , Ahmed Ali , Ali Elimam , Sayed Mohamed Mahmoud , Aziz Mubashir
Published: July 2022

The sustainable design and construction of highways is indispensable for the economic growth and progress of any region. Highway pavements are one of the core transportation infrastructures that require energy efficient materials with durability and an optimized lifecycle. Recent research has proven that warm-mix asphalt pavements prepared with renewable bio-binders are less susceptible to distresses. This study aims to investigate the resilience characteristics (load time, deformation time) of soybean oil modified and unmodified warm-mix asphalts. Aggregates, asphalt binders and asphalt mixes were characterized in accordance with the Superpave Mix Design Criteria. The resilient modulus tests were performed as per ASTM D7369. The test results indicated that the soybean-modified warm asphalt mix samples showed a 20% to 32% reduction in load-carrying capacity than unmodified warm asphalt mixes. The values of the horizontal and vertical recoverable deformations observed in the soybean-modified mixes were found to be 3% to 7% more than in the unmodified mixes. A slight variability (up to 7%) was also observed in the time-response spectra, i.e., peak load, unload and rest periods, in the soybean-modified mixes compared with the unmodified mixes. The Pearson correlation coefficient showed a significant trend between the resilient modulus test parameters for the soybean-modified warm asphalt mixes, i.e., load deformation, load time and deformation time. Soybean oil showed sustainable behavior as a bio-binder, particularly in the deformation-time response for the warm asphalt mixes. However, the effect of soybean in terms of the reduction of the load-carrying capacity from a sustainability perspective needs to be investigated.
Suburbanization within City Limits in Hungary—A Challenge for Environmental and Social Sustainability
Authors: Vasárus Gábor László , Lennert József
Published: July 2022

Suburbanization is one of the most prominent processes of post-socialist urban development, leading to the deconcentration of people, capital, as well as productive and non-productive activities within the functional urban area. This phenomenon also has a significant impact on the traditional rural landscape and leads to environmental and social sustainability challenges. Outmigration from the city center to the rural municipalities of the agglomeration ring is already a thoroughly studied topic. However, less attention is given to migration processes not crossing municipal borders. In Hungary, a significant fraction of them is driven by similar motivations as “traditional” suburbanization. Such movements include flows to built-up residential areas that are physically separated from the urban core. Due to their peculiar development path, the inhabited outskirts can also become destinations for within-city migration. This kind of mobility can be considered suburbanization within the city limits. Because of the general lack of data, this phenomenon is seldom researched. However, this study attempts to address this gap. The prevalence of this process in Hungary is explored by analyzing national statistical data sources. Four case studies are selected for detailed examination—Győr, Zalaegerszeg, Kecskemét, and Szeged. GIS methods, field examinations, surveys, and expert interviews are used to get a detailed picture of the demographic and land cover change processes, as well as the distinctions between the destination areas of the case studies. A comparison of the results drawn from the different methods reveals that land use change in the study area is more widespread than what the land cover datasets indicate. The findings indicate that the Corine Land Cover categories describing mixed land use—especially complex cultivation patterns—are not able to capture the drastic function shift caused by intense suburbanization. Different environmental and social sustainability issues are identified depending on the economic status of the outmigrants. This paper also sheds some light on the urban planning considerations of this emerging challenge.
Assessment of Groundwater Suitability for Agricultural Purposes: A Case Study of South Oued Righ Region, Algeria
Authors: Kadri Abdelaziz , Baouia Kais , Kateb Samir , Al-Ansari Nadhir , Kouadri Saber , Najm Hadee Mohammed , Mashaan Nuha S. , Eldirderi Moutaz Mustafa A. , Khedher Khaled Mohamed
Published: July 2022

Groundwater in the Touggourt region—or as its named, Oued Righ—in southeastern Algeria, is the only source of irrigation. To assess its suitability for agricultural purposes, we collected 72 samples from wells at this region, physical and chemical measurements were carried out for each water sample, and calculations of the sodium adsorption ratio (SAR), permeability index (PI), soluble sodium percent (SSP), residual sodium carbonate (RSC), magnesium hazard ratio (MHR) and Kelley’s ratio (KR) were carried out, as these indices are often used to assess the suitability of groundwater for irrigation uses. Based on the irrigation water quality index (IWQI) values, a spatial distribution map for each parameter using the inverse interpolation technique (IDW) was produced by Geographical Information System (GIS). According to the IWQI map, about 35% of the water samples analyzed fall into the Severe Restriction category (SR), making it unsuitable for irrigation under normal circumstance. Again, the remaining 65% of the groundwater has a high restriction (HR) for use. Groundwater in the study area could be used for irrigation in highly permeable soils where salt-tolerant crops are grown. Adequate drainage and continuous monitoring of water quality are recommended.
The LCT Challenge: Defining New Design Objectives to Increase the Sustainability of Building Retrofit Interventions
Authors: Passoni Chiara , Palumbo Elisabetta , Pinho Rui , Marini Alessandra
Published: July 2022

The decarbonization of the construction sector, which is one of the most impactful sectors worldwide, requires a significant paradigm shift from a linear economy to a circular, future-proofed and sustainable economy. In this transition, the role of designers and structural engineers becomes pivotal, and new design objectives and principles inspired by Life Cycle Thinking (LCT) should be defined and included from the early stages of the design process to allow for a truly sustainable renovation of the built environment. In this paper, an overview of LCT-based objectives and principles is provided, critically analyzing the current state of the art of sustainability and circularity in the construction sector. The effectiveness of applying such design principles from the early stages of the design of retrofit interventions is then demonstrated with reference to a case study building. Four seismic retrofit alternatives made of timber, steel and concrete, conceived according to either LCT principles or traditional, were designed and compared to a demolition and reconstruction scenario on the basis of five common environmental impact indicators. The indicators were calculated adopting simplified LCA analyses based on Environmental Product Declarations (EPDs), considering the product and End of Life stages of the building. The results of the comparative analyses confirm that LCT-based retrofit solutions are less impactful than both the traditional seismic retrofit interventions and the demolition and reconstruction scenario.
Climate Change Impacts on the Road Transport Infrastructure: A Systematic Review on Adaptation Measures
Authors: de Abreu Victor Hugo Souza , Santos Andrea Souza , Monteiro Thaís Guedes Máximo
Published: July 2022

Road transport is one of the main contributors to increasing greenhouse gas (GHG) emissions, consequently aggravating global warming, but it is also one of the sectors that most suffer from climate change, which causes extreme weather events. Thus, strategies, also called adaptation measures, have been discussed to minimize the impacts of climate change on transport systems and their infrastructure; however, a knowledge gap is evident in the literature. Therefore, this article develops a systematic review with a bibliometric approach, still scarce in the literature, in renowned databases, focusing on studies developed on adaptation measures for road infrastructure. The results show that, since the development of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), an increasing amount of studies on the theme have been published in recognized journals such as Science of the Total Environment, Energy and Buildings and Urban Climate, analyzing climate threats such as intense precipitations and high temperatures that have led to biophysical impacts such as flooding and urban heat island. In addition, for each type of adverse weather condition, many impacts on road infrastructure can be listed, as well as ways to detect these impacts, and adaptation measures that can be used to minimize these problems.
Effects of Fertilization on Morphological and Physiological Characteristics and Environmental Cost of Maize (Zea mays L.)
Authors: Laskari Maria , Menexes Georgios C. , Kalfas Ilias , Gatzolis Ioannis , Dordas Christos
Published: July 2022

Maize (Zea mays L.) is one of the most important crops in the world and fertilization is the most important management practice which contributes to high yield. The objective of this study was to determine the effect of different fertilizers on maize crop and their contribution to the carbon footprint. The experiments were conducted in a commercial field in the area of Thessaloniki during the growing seasons of 2019 and 2020. During the experiment a number of physiological and morphological characteristics, and the energy output/input ratio, energy efficiency, and carbon footprint were determined. The results of the experiment showed that the inorganic fertilizers and manure improved the morphological and physiological characteristics that were studied compared to the green manure treatment and the control. In addition, it appeared most of the energy input of maize cultivation is from fertilizers (52%), followed by diesel (25%) and the use of machinery (14%). The treatments with the slow release fertilizers and the manure gave satisfactory results with an average of 42.1 Mg ha−1 in 2019 and 43.6 Mg ha−1 in 2020 for both fertilization treatments. Therefore, it is necessary to use the appropriate fertilizers in order to maintain the productivity of the crop and reduce the environmental costs.
Research on Customer Behavioral Intention of Hot Spring Resorts Based on SOR Model: The Multiple Mediation Effects of Service Climate and Employee Engagement
Authors: Guo Zhengyan , Yao Yao , Chang Yuan-Cheng
Published: July 2022

Based on the stimulus–organism–response (SOR) model, this study explored customer behavioral intentions and influencing factors in the service industry, represented by hotels. It studied the servicescape, customer emotions, and customer behavioral intentions. PROCESS analysis was conducted on 305 valid questionnaires collected from hot spring resorts. The study found that servicescape can predict customer behavioral intentions, that customer emotions have a partial mediating effect in the influence of servicescape on customer behavioral intentions, and that service climate and employee engagement have multiple mediation effects in the SOR model. Therefore, the researchers suggest that hotels can improve customer perceptions through decoration and staff management, and thereby develop the hotel in a sustainable way.
Effects of Combined Application of Salicylic Acid and Proline on the Defense Response of Potato Tubers to Newly Emerging Soft Rot Bacteria (Lelliottia amnigena) Infection
Authors: Osei Richard , Yang Chengde , Wei Lijuan , Jin Mengjun , Boamah Solomon
Published: July 2022

Potato soft rot, caused by the pathogenic bacterium Lelliottia amnigena (Enterobacter amnigenus), is a serious and widespread disease affecting global potato production. Both salicylic acid (SA) and proline (Pro) play important roles in enhancing potato tuber resistance to soft rot. However, the combined effects of SA and Pro on defense responses of potato tubers to L. amnigena infection remain unknown. Hence, the combined effects of SA and Pro in controlling newly emerging potato soft rot bacteria were investigated. Sterilized healthy potato tubers were pretreated with 1.5 mM SA and 2.0 mM Pro 24 h before an inoculation of 0.3 mL of L. amnigena suspension (3.69 × 107 CFU mL−1). Rotting was noticed on the surfaces of the hole where the L. amnigena suspension was inoculated. Application of SA and Pro with L. amnigena lowered the activity of pectinase, protease, pectin lyase, and cellulase by 64.3, 77.8, 66.4 and 84.1%, and decreased malondialdehyde and hydrogen peroxide contents by 77.2% and 83.8%, respectively, compared to the control. The activities of NADPH oxidase, superoxide dismutase, peroxide, catalase, polyphenol oxidase, phenylalanine ammonia-lyase, cinnamyl alcohol dehydrogenase, 4-coumaryl-CoA ligase and cinnamate-4-hydroxylase were increased in the potato tubers with combined treatments by 91.4, 92.4, 91.8, 93.5, 94.9, 91.3, 96.2, 94.7 and 97.7%, respectively, compared to untreated stressed tubers. Six defense-related genes, pathogenesis-related protein, tyrosine-protein kinase, Chitinase-like protein, phenylalanine ammonia-lyase, pathogenesis-related homeodomain protein, and serine protease inhibitor, were induced in SA + Pro treatment when compared with individual application of SA or Pro. This study indicates that the combined treatment of 1.5 mM SA and 2.0 mM Pro had a synergistic effect in controlling potato soft rot caused by a newly emerging bacterium.