bluuebunny/crossref_metadata_2025 huggingface dataset:
Trend in Satellite-Observed Vegetation Cover and Its Drivers in the Gannan Plateau, Upper Reaches of the Yellow River, from 2000 to 2020
Authors: Liang Yu , Zhang Zhengyang , Lu Lei , Cui Xia , Qian Jikun , Zou Songbing , Ma Xuanlong
Published: August 2022

The Yellow River basin (YRB) has played an important role in the forming of Chinese civilization. Located in the upper reaches of the YRB and the southeastern edge of the Qinghai–Tibet Plateau (QTP), the Gannan Plateau (GP), which consists of mainly alpine and mountain ecosystems, is one of the most important water conservation areas for the Yellow River and recharges 6.59 billion cubic meters of water to the Yellow River each year, accounting for 11.4% of the total runoff of the Yellow River. In the past 30 years, due to climate change and intense human activities, the GP is facing increasing challenges in maintaining its ecosystem integrity and security. Vegetation is a central component of the terrestrial ecosystem and is also key to maintaining ecosystem functioning and services. To form sound ecological restoration projects for the GP and the upper reaches of the YRB in general, this study assesses the trend in FVC (Fractional Vegetation Cover) and its drivers across the GP by integrating high-resolution satellite remote sensing images and meteorological data from 2000 to 2020. Results showed that the mean value of FVC for the entire GP between 2000 and 2020 was 89.26%. Aridity was found to be the main factor that determined the spatial distribution of FVC, while ecosystem type exhibited the secondary effect with forests having the highest FVC within each aridity class. From 2000 to 2020, the FVC in 84.11% of the study area did not exhibit significant change, though 10.32% of the study area still experienced a significant increase in FVC. A multi-factor analysis revealed that precipitation surpassed temperature as the main driver for the FVC trend in semi-arid and semi-humid areas, while this pattern was reversed in humid areas. A further residual analysis indicated that human activities only played a minor role in determining the FVC trend in most naturally vegetated areas of the study area, except for semi-arid crops where a significant positive role of human influences on the FVC trend was observed. The findings highlight the fact that aridity and vegetation types interact to explain the relative sensitivity of alpine and mountain ecosystems to climate trends and human influences. Results from this study provide an observational basis for better understanding and pattern prediction of ecosystem functioning and services in the GP under future climate change, which is key to the success of the national strategy that aims to preserve ecosystem integrity and promote high-quality development over the entire YRB.
Video-Based Nearshore Bathymetric Inversion on a Geologically Constrained Mesotidal Beach during Storm Events
Authors: Rodríguez-Padilla Isaac , Castelle Bruno , Marieu Vincent , Morichon Denis
Published: August 2022

Although geologically constrained sandy beaches are ubiquitous along wave-exposed coasts, there is still a limited understanding of their morphological response, particularly under storm conditions, which is mainly due to a critical lack of nearshore bathymetry observations. This paper examines the potential to derive bathymetries from video imagery under challenging wave conditions in order to investigate headland control on morphological beach response. For this purpose, a video-based linear depth inversion algorithm is applied to three consecutive weeks of frames collected during daylight hours from a single fixed camera located at La Petite Chambre d’Amour beach (Anglet, SW France). Video-derived bathymetries are compared against in situ topo-bathymetric surveys carried out at the beginning and end of the field experiment in order to assess the performance of the bathymetric estimates. The results show that the rates of accretion/erosion within the surf zone are strongly influenced by the headland, whereas the beach morphological response can be classified into three main regimes depending on the angle of wave incidence θp: (1) under deflection configuration (θp>0°), the alongshore sediment transport was trapped at the updrift side of the headland, promoting sand accretion. (2) Under shadowed configuration (θp<0°), the interruption of the longshore current drove a deficit of sand supply at the downdrift side of the headland, leading to an overall erosion in the surf zone. (3) Under shore-normal configuration (θp=0°), rip channels developed, and up-state beach transition was observed. A comparison between video-derived bathymetries and surveys shows an overall root mean square error (RMSE) around 0.49 to 0.57 m with a bias ranging between −0.36 and −0.29 m. The results show that video-derived bathymetries can provide new insight into the morphological change driven by storm events. The combination of such inferred bathymetry with video-derived surface current data is discussed, showing great potential to address the coupled morphodynamics system under time-varying wave conditions.
Regional Yield Estimation for Sugarcane Using MODIS and Weather Data: A Case Study in Florida and Louisiana, United States of America
Authors: Hu Shun , Shi Liangsheng , Zha Yuanyuan , Zeng Linglin
Published: August 2022

Sugarcane is an important sugar source in America and is mainly planted in the states of Florida and Louisiana. The purpose of this study was to predict the sugarcane yield in these two states from 2008 to 2016. Three statistical sugarcane yield models (i.e., the CNDVI, K–M, and SiPAR models) were applied to predict yield in America, using remote sensing and weather data. To verify the robustness of models, model parameters obtained in places (i.e., Reunion Island and Southwestern China) far away from America were used. The results showed that the SiPAR model outperformed the CDNVI and K–M models for yield prediction. Solar radiation was an important constraint factor to ensure the statistical model’s robustness under different conditions. The CNDVI model had the lowest robustness because of the absence of solar radiation, although it could reflect the yield trend to some extent. The K–M model failed to predict the low sugarcane yield, owing to the lack of consideration of temperature and soil water stresses. Florida had a low sugarcane yield in the west and southwest; however, Louisiana had high sugarcane yield in the same directions. This study demonstrated the robustness of the SiPAR model and investigated the sugarcane yield status in Florida and Louisiana. It can be a reference for similar studies in the future.
Towards a New MAX-DOAS Measurement Site in the Po Valley: NO2 Total VCDs
Authors: Pettinari Paolo , Castelli Elisa , Papandrea Enzo , Busetto Maurizio , Valeri Massimo , Dinelli Bianca Maria
Published: August 2022

Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments are used worldwide to retrieve pollutant information from visible (VIS) and ultra-violet (UV) diffuse solar spectra. A similar instrument, able to meet the Fiducial Reference Measurements for DOAS (FRM4DOAS) standard requirements, is not yet present in the Po Valley (Italy), one of the most polluted regions in Europe. Our purpose is to close this gap exploiting the SkySpec-2D, a FRM4DOAS-compliant MAX-DOAS instrument bought by the Italian research institute CNR-ISAC in May 2021. As a first step, SkySpec-2D was involved in two measurement campaigns to assess its performance: the first one in August 2021 in Bologna where TROPOGAS, a research-grade custom-built MAX-DOAS instrument is located; the second one in September 2021 at the BAQUNIN facility at La Sapienza University (Rome) near the Pandora#117 instrument. Both campaigns revealed a good quality of SkySpec-2D measurements. Indeed, good agreement was found with TROPOGAS (correlation 0.77), Pandora#117 (correlation 0.9) and satellite (TROPOMI and OMI) measurements. Having assessed its performance, the SkySpec-2D was permanently moved to the “Giorgio Fea” observatory in San Petro Capofiume, located in the middle of the Po Valley, where it has been continuously acquiring zenith and off-axis diffuse solar spectra from the 1 October 2021. Nowadays, its MAX-DOAS measurements are routinely provided to the FRM4DOAS team with the purpose to be soon included in the FRM4DOAS validation network.
Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks
Authors: Lakhan Abdullah , Mohammed Mazin Abed , Abdulkareem Karrar Hameed , Jaber Mustafa Musa , Nedoma Jan , Martinek Radek , Zmij Petr
Published: August 2022

Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study’s goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.
Tomographic Absorption Spectroscopy for H2O Transport in a Laminar Jet with Inverse Concentration Gradient
Authors: Cheong Kin-Pang , Shi Dingfeng , Liu Shaotong , Wu Junjun , Duan Kun , Song Yong , Ren Wei
Published: August 2022

We report a tomographic absorption spectroscopy (TAS) study of water vapor transport in a laminar jet issuing into the ambient. The jet was generated using compressed dry air that was straightened by a honeycomb and a smooth contraction nozzle. A TAS scheme using the water vapor in the ambient as absorbing species and the absorption line near 1368.598 nm was proposed to study the H2O transport in the laminar jet with an inverse concentration gradient. One-dimensional tomography was conducted at various heights above the nozzle, and the results were validated by the predictions from computational fluid dynamics (CFD) simulations. Particularly, the variations in the concentration gradient in the shear layer at different heights were captured. The 2D distribution of water concentration in the dry laminar jet was obtained experimentally. The present study shows that TAS has great potential in the research of mass transfer and scalar field of gaseous flows.
Effects of Data Augmentations on Speech Emotion Recognition
Authors: Atmaja Bagus Tris , Sasou Akira
Published: August 2022

Data augmentation techniques have recently gained more adoption in speech processing, including speech emotion recognition. Although more data tend to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition in various conditions. The experiments are conducted on the Japanese Twitter-based emotional speech and IEMOCAP datasets. The results show that for speaker-independent data, two data augmentations with glottal source extraction and silence removal exhibited the best performance among others, even with more data augmentation techniques. For the text-independent data (including speaker and text-independent), more data augmentations tend to improve speech emotion recognition performances. The results highlight the trade-off between the number of data augmentations and the performance of speech emotion recognition showing the necessity to choose a proper data augmentation technique for a specific condition.
Process Monitoring Using Synchronized Path Infrared Thermography in PBF-LB/M
Authors: Höfflin Dennis , Sauer Christian , Schiffler Andreas , Hartmann Jürgen
Published: August 2022

Additive manufacturing processes, particularly Laser-Based Powder Bed Fusion of Metals (PBF-LB/M), enable the development of new application possibilities due to their manufacturing-specific freedom of design. These new fields of application require a high degree of component quality, especially in safety-relevant areas. This is currently ensured primarily via a considerable amount of downstream quality control. Suitable process monitoring systems promise to reduce this effort drastically. This paper introduces a novel monitoring method in order to gain process-specific thermal information during the manufacturing process. The Synchronized Path Infrared Thermography (SPIT) method is based on two synchronized galvanometer scanners allowing high-speed and high-resolution observations of the melt pool in the SWIR range. One scanner is used to steer the laser over the building platform, while the second scanner guides the field of view of an IR camera. With this setup, the melting process is observed at different laser powers, scan speeds and at different locations with respect to the laser position, in order to demonstrate the positioning accuracy of the system and to initially gain thermal process data of the melt pool and the heat-affected zone. Therefore, the SPIT system shows a speed independent overall accuracy of ±2 Pixel within the evaluated range. The system further allows detailed thermal observation of the melt pool and the surrounding heat-affected zone.
Study on Quantum Radar Detection Probability Based on Flying-Wing Stealth Aircraft
Authors: Lu Shaoze , Meng Zhijun , Huang Jun , Yi Mingxu , Wang Zeyang
Published: August 2022

The development of quantum radar technology presents a challenge to stealth targets, so it is necessary to study the quantum detection probability. In this study, an analytical expression of the quantum radar cross section (QRCS) for complex targets is presented. Based on this QRCS expression, a calculation method for the detection probability for quantum radar is creatively proposed. Moreover, a self-designed flying-wing stealth aircraft is adopted to obtain the detection probability distributions of the conventional radar and the quantum radar in different directions. As revealed by the result of this study, the detection probabilities of the quantum radar and the conventional radar are significantly different, and the detection probability of the quantum radar has obvious advantages in most regions with a certain distance.
Electromyography Parameter Variations with Electrocardiography Noise
Authors: Chang Kang-Ming , Liu Peng-Ta , Wei Ta-Sen
Published: August 2022

Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG) signals that cannot be easily separated with traditional filters, because both signals have some overlapping spectral components. Therefore, the first challenge encountered in signal processing is to extract the ECG noise from the EMG signal. In this study, the EMG, mixed with different degrees of noise (ECG), is simulated to investigate the variations of the EMG features. Simulated data were derived from the MIT-BIH Noise Stress Test (NSTD) Database. Two EMG and four ECG data were composed with four EMG/ECG SNR to 32 simulated signals. Following Pan-Tompkins R-peak detection, four ECG removal methods were used to remove ECG with different compensation algorithms to obtain the denoised EMG signal. A total of 13 time-domain and four frequency-domain EMG features were calculated from the denoised EMG. In addition, the similarity of denoised EMG features compared to clean EMG was also evaluated. Our results showed that with the ratio EMG/ECG SNR = 10 and 20, the ECG can be almost ignored, and the similarity of EMG features is close to 1. When EMG/ECG SNR = 1 and 2, there is a large variation of EMG features. The results of our simulation study would be beneficial for understanding the variations of EMG features upon the different EMG/ECG SNR.
Nested Bee Hive: A Conceptual Multilayer Architecture for 6G in Futuristic Sustainable Smart Cities
Authors: Farooq Muhammad Shoaib , Nadir Rana Muhammad , Rustam Furqan , Hur Soojung , Park Yongwan , Ashraf Imran
Published: August 2022

Several smart city ideas are introduced to manage various problems caused by overpopulation, but the futuristic smart city is a concept based on dense and artificial-intelligence-centric cities. Thus, massive device connectivity with huge data traffic is expected in the future where communication networks are expected to provide ubiquity, high quality of service, and on-demand content for a large number of interconnected devices. The sixth-generation (6G) network is considered the problem-solving network of futuristic cities, with huge bandwidth and low latency. The expected 6G of the radio access network is based on terahertz (THz) waves with the capability of carrying up to one terabit per second (Tbps). THz waves have the capability of carrying a large amount of data but these waves have several drawbacks, such as short-range and atmospheric attenuation. Hence, these problems can introduce complications and hamper the performance of the 6G network. This study envisions futuristic smart cities using 6G and proposes a conceptual terrestrial network (TN) architecture for 6G. The nested Bee Hive is a scalable multilayer architecture designed to meet the needs of futuristic smart cities. Moreover, we designed the multilayer network infrastructure while considering the expectations from a network of futuristic smart cities and the complications of THz waves. Extensive simulations are performed using different pathfinding algorithms in the 3D multilayer domain to evaluate the performance of the proposed architecture and set the dynamics of futuristic communication of 6G.
Recovery Strategy for Overturned Wheeled Vehicle Using a Mobile Robot and Experimental Validation
Authors: Ikeda Hidetoshi , Atoji Shinya , Amemiya Manami , Tajima Shingo , Kitada Takayoshi , Fukai Kotaro , Sato Keisuke
Published: August 2022

This paper describes mobile robot tactics for recovering a wheeled vehicle that has overturned. If such a vehicle were to tip over backward off its wheels and be unable to recover itself, especially in areas where it is difficult for humans to enter and work, overall work efficiency could decline significantly, not only because the vehicle is not able to perform its job, but because it becomes an obstacle to other work. Herein, the authors propose a robot-based recovery method that can be used to recover such overturned vehicles, and the authors evaluate its effectiveness. The recovery robot, which uses a mounted manipulator and hand to recover the overturned vehicle, is also equipped with a camera and a personal computer (PC). The ARToolKit software package installed on the PC detects AR markers attached to the overturned vehicle and uses the information they provide to orient itself in order to perform recovery operations. A statics analysis indicates the feasibility of the proposed method. To facilitate these operations, it is also necessary to know the distance between the robotic hand and the target position for grasping of vehicle. Therefore, a theoretical analysis is conducted, and a control system based on the results is implemented. The experimental results obtained in this study demonstrate the effectiveness of the proposed system.
Structure Design of Quadrilateral Overlapped Wireless Power Transmission Coupling Coil
Authors: Wang Xiaotian , Yu Changli , Wu Yuteng , Wang Jingang
Published: August 2022

The application of wireless power transmission technology is becoming more and more extensive. However, in practical applications, the problem of reducing the energy transmission efficiency caused by the offset of the coupling coil needs to be solved urgently. Changing the coil structure is a widely adopted method to deal with this problem. Based on the characteristics of the existing magnetic coupling resonant wireless power transmission system and the principle of the anti-offset coil, this paper innovatively designs a new type of quadrilateral overlapping wireless power transfer coupling coil, which has a strong anti-offset capability. The new type of coil model was built in the simulation and experiment, and the relevant parameters were measured. Experimental results verify that the proposed coil structure has an excellent anti-offset capability.
Machine Learning-Based Predicted Age of the Elderly on the Instrumented Timed Up and Go Test and Six-Minute Walk Test
Authors: Ko Jeong Bae , Hong Jae Soo , Shin Young Sub , Kim Kwang Bok
Published: August 2022

A decrease in dynamic balance ability (DBA) in the elderly is closely associated with aging. Various studies have investigated different methods to quantify the DBA in the elderly through DBA evaluation methods such as the timed up and go test (TUG) and the six-minute walk test (6MWT), applying the G-Walk wearable system. However, these methods have generally been difficult for the elderly to intuitively understand. The goal of this study was thus to generate a regression model based on machine learning (ML) to predict the age of the elderly as a familiar indicator. The model was based on inertial measurement unit (IMU) data as part of the DBA evaluation, and the performance of the model was comparatively analyzed with respect to age prediction based on the IMU data of the TUG test and the 6MWT. The DBA evaluation used the TUG test and the 6MWT performed by 136 elderly participants. When performing the TUG test and the 6MWT, a single IMU was attached to the second lumbar spine of the participant, and the three-dimensional linear acceleration and gyroscope data were collected. The features used in the ML-based regression model included the gait symmetry parameters and the harmonic ratio applied in quantifying the DBA, in addition to the features of description statistics for IMU signals. The feature set was differentiated between the TUG test and the 6MWT, and the performance of the regression model was comparatively analyzed based on the feature sets. The XGBoost algorithm was used to train the regression model. Comparison of the regression model performance according to the TUG test and 6MWT feature sets showed that the performance was best for the model using all features of the TUG test and the 6MWT. This indicated that the evaluation of DBA in the elderly should apply the TUG test and the 6MWT concomitantly for more accurate predictions. The findings in this study provide basic data for the development of a DBA monitoring system for the elderly.
A New Prediction Method of Displacement Errors Caused by Low Stiffness for Industrial Robot
Authors: He Zhenya , Song Mingjing , Zhang Xianmin , Huang Guojian
Published: August 2022

This paper presents a new method, a fast prediction method based on the Cartesian stiffness model and equivalent spring stiffness (FPM-CSES), to calculate displacement errors of deformation caused by low stiffness for industrial robot. First, the Cartesian stiffness model based on the Jacobian matrix was established for a robot, and then the displacement error model of deformations caused by external force was established based on Cartesian stiffness. Second, the transmission system of the robot’s joint was analyzed, and an equivalent method for joint stiffness was presented based on a series spring system. Meanwhile, the stiffness of the key components including the servo motor, harmonic reducer, and timing belt was deduced in detail. Finally, a compared simulation and a measurement experiment were conducted on a 6-joint series robot. It was found that the FPM-CSES could calculate any configuration among the robot’s workspace. Compared with the finite element analysis (FEA) method, the presented method is feasible and more efficient. The experimental results showed that the prediction accuracy of the FPM-CSES is rather high, with an average rate of more than 83.72%. Hence, the prediction method presented in this study is simple, fast, and reliable, and could be used to predict and analyze the displacement errors caused by the cutting force, and provide the basis for trajectory planning and error compensation, enhancing the robot’s machining performance.
Hybrid Approach for Improving the Performance of Data Reliability in Cloud Storage Management
Authors: Alzahrani Ali , Alyas Tahir , Alissa Khalid , Abbas Qaiser , Alsaawy Yazed , Tabassum Nadia
Published: August 2022

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research
A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data
Authors: Ehrhart Maximilian , Resch Bernd , Havas Clemens , Niederseer David
Published: August 2022

Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure physiological signals and are, therefore, suitable for detecting body reactions to stress. The field of machine learning, or more precisely, deep learning, has been able to produce outstanding results. However, in order to produce these good results, large amounts of labeled data are needed, which, in the context of physiological data related to stress detection, are a great challenge to collect, as they usually require costly experiments or expert knowledge. This usually results in an imbalanced and small dataset, which makes it difficult to train a deep learning algorithm. In recent studies, this problem is tackled with data augmentation via a Generative Adversarial Network (GAN). Conditional GANs (cGAN) are particularly suitable for this as they provide the opportunity to feed auxiliary information such as a class label into the training process to generate labeled data. However, it has been found that during the training process of GANs, different problems usually occur, such as mode collapse or vanishing gradients. To tackle the problems mentioned above, we propose a Long Short-Term Memory (LSTM) network, combined with a Fully Convolutional Network (FCN) cGAN architecture, with an additional diversity term to generate synthetic physiological data, which are used to augment the training dataset to improve the performance of a binary classifier for stress detection. We evaluated the methodology on our collected physiological measurement dataset, and we were able to show that using the method, the performance of an LSTM and an FCN classifier could be improved. Further, we showed that the generated data could not be distinguished from the real data any longer.
Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics
Authors: Pantazi Xanthoula Eirini , Lagopodi Anastasia L. , Tamouridou Afroditi Alexandra , Kamou Nathalie Nephelie , Giannakis Ioannis , Lagiotis Georgios , Stavridou Evangelia , Madesis Panagiotis , Tziotzios Georgios , Dolaptsis Konstantinos , Moshou Dimitrios
Published: August 2022

The aim of this study was to develop three supervised self-organizing map (SOM) models for the automatic recognition of a systemic resistance state in plants after application of a resistance inducer. The pathosystem Fusarium oxysporum f. sp. radicis-lycopersici (FORL) + tomato was used. The inorganic, defense inducer, Acibenzolar-S-methyl (benzo-[1,2,3]-thiadiazole-7-carbothioic acid-S-methyl ester, ASM), reported to induce expression of defense genes in tomato, was applied to activate the defense mechanisms in the plant. A handheld fluorometer, FluorPen FP 100-MAX-LM by SCI, was used to assess the fluorescence kinetics response of the induced resistance in tomato plants. To achieve recognition of resistance induction, three models of supervised SOMs, namely SKN, XY-F, and CPANN, were used to classify fluorescence kinetics data, in order to determine the induced resistance condition in tomato plants. To achieve this, a parameterization of fluorescence kinetics curves was developed corresponding to fluorometer variables of the Kautsky Curves. SKN was the best supervised SOM, achieving 97.22% to 100% accuracy. Gene expression data were used to confirm the accuracy of the supervised SOMs.
Analysis of the Impact of Changes in Echo Signal Parameters on the Uncertainty of Distance Measurements in p-ToF Laser Rangefinders
Authors: Muzal Michał , Zygmunt Marek
Published: August 2022

The article presents results of research on the influence of changes in parameters of the digitally recorded echo signals on the uncertainty of pulsed Time-of-Flight (p-ToF) laser distance measurements. The main objective of the study was to evaluate the distance calculation method developed by the authors. This method is based on the acquisition of the full-waveform of the echo pulse signal and approximation of its shape by the second-degree polynomial (we called it SDPA for short). To determine the pulse transit time and measure the distance, the position of the vertex of this parabola is sought. This position represents the maximum intensity of the incoming echo signal and is related to the round-trip propagation time of the laser pulse. In the presented work, measurement uncertainty was evaluated using simulation tests for various parameters of the echo pulse. All obtained results were used to formulate the general relationship between the measurement uncertainty of the SDPA algorithm and the parameters of the received echo signals. This formula extends the base knowledge in the domain of laser p-ToF distance measurements. It can be used to estimate the measurement uncertainty of a FW LiDAR at an early design stage. This greatly improves capabilities of analysis of expected performance of the device. It can also be implemented directly into the rangefinder’s measurement algorithm to estimate the measurement uncertainty based on the emission of a single pulse rather than a series of pulses.
Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise
Authors: Song Jinhao , Li Jie , Wei Xiaokai , Hu Chenjun , Zhang Zeyu , Zhao Lening , Jiao Yubing
Published: August 2022

The accurate noise parameter is essential for the Kalman filter to obtain optimal estimates. However, problems such as variations in the noise environment and measurement anomalies can cause degradation of estimation accuracy or even divergence. The adaptive Kalman filter can simultaneously estimate state and noise parameters, while its performance will also be degraded in complex noise. To address the problem of estimation accuracy degradation and result divergence of the integrated navigation system in a complex time-varying noise environment, an improved multiple-model adaptive estimation (MMAE) that combines the Sage–Husa adaptive unscented Kalman filter with the MMAE is proposed in this paper. The forgetting factor is included as an unknown parameter of MMAE so that the algorithm can adjust the value of the forgetting factor according to different system states. In addition, we improve the hypothesis testing algorithm of classical MMAE to deal with the competition problem of undesirable models that severely impacts the performance of variable-parameter MMAE and enhance the algorithm’s parameter identification capability. Simulation results show that this method enhances the system’s robustness to noises of different statistical properties and improves the estimation accuracy of the filter in time-varying noise environments.
Stick-Slip Vibration Suppression in Drill String Using Observer-Based LQG Controller
Authors: Riane Rami , Doghmane Mohamed Zinelabidine , Kidouche Madjid , Tee Kong Fah , Djezzar Sofiane
Published: August 2022

Hydrocarbon exploration and production activities are guaranteed through various operations including the drilling process, which is realized by using rotary drilling systems. The process involves crushing the rock by rotating the drill bit along a drill string to create a borehole. However, during this operation, violent vibrations can occur at the level of the drill string due to its random interaction with the rocks. According to their axes of occurrence, there are three types of vibrations: axial, lateral, and torsional, where the relentless status of the torsional vibrations is terminologically known as the stick-slip phenomenon. Such a phenomenon can lead to increased fatigue of the drill string and cause its abortive fracture, in addition to reducing the efficiency of the drilling process and consequently making the exploration and production operations relatively expensive. Thus, the main objective of this paper is to eliminate the severe stick-slip vibrations that appear along the drill string of the rotary drilling system according to the LQG observer-based controller approach. The rock–bit interaction term is highly nonlinear, and the bit rotational velocity is unmeasurable; an observer was first designed to estimate the unknown inputs of the model, and then the controller was implemented in the drill string model with 10 degrees of freedom. The estimation process was essentially based on surface measurements, namely, the current and rotational velocity of the top drive. Thereafter, the performance of the proposed observer-based LQG controller was tested for different simulation scenarios in a SimScape/Matlab environment, for which the controller demonstrated good robustness in suppressing the severe stick-slip vibrations. Furthermore, the simulation and experimental results were compared to other controllers designed for the same model; the proposed observer-based LQG controller showed better performance, and it was less sensitive to structured disturbances than H∞. Thence, it is highly recommended to use the proposed approach in smart rotary drilling systems.
In-Flight Alignment of Integrated SINS/GPS/Polarization/Geomagnetic Navigation System Based on Federal UKF
Authors: Cao Songyin , Gao Honglian , You Jie
Published: August 2022

As a common integrated navigation system, the strapdown inertial navigation system (SINS)/global positioning system (GPS) can estimate velocity and position errors well. Many auxiliary attitude measurement systems can be used to improve the accuracy of attitude angle errors. In this paper, the in-flight alignment problem of the integrated SINS/GPS/Polarization/Geomagnetic navigation system is discussed. Firstly, the SINS/Geomagnetic subsystem is constructed to improve the estimation accuracy of horizontal attitude angles. Secondly, the polarization sensor is used to improve the estimation accuracy of heading angle. Then, a federal unscented Kalman filter (FUKF) with non-reset structure is applied to fuse the navigation data. Finally, simulation results for the integrated navigation system are provided based on experimental data. It can be shown that the proposed approach can improve not only the speed and position, but also the attitude error effectively.
Laurus azorica: Valorization through Its Phytochemical Study and Biological Activities
Authors: Viveiros Mariana M. , Barreto Maria Carmo , Seca Ana M. L.
Published: August 2022

Laurus azorica (Seub.) Franco is an endemic species from the Azores, traditionally used in all the islands as a seasoning in cooking. The studies carried out with this species refer mainly to its essential oils. The study that was developed here allowed, for the first time, to determine the chemical composition and biological activities of the ethanol extract, fractions, and pure compounds from L. azorica. The hexane fraction was analyzed by GC–MS and revealed the presence of 48 compounds, comprising mainly fatty acids, fatty alcohols and terpenes, the family of fatty alcohols identified here for the first time in the genus Laurus. Three sesquiterpene lactones—costunolide, 11,13-dehydrosantonin and reynosin—were isolated for the first time in L. azorica from the same fraction, and structurally characterized using spectroscopic techniques. The compounds identified belong to families known to have relevant medicinal and nutritional properties. Regarding antioxidant activities, the results obtained showed a moderate radical scavenging effect of extracts and fractions, while in the β-carotene bleaching assay, costunolide was shown to be the most active (IC50 = 4.08 ± 0.76 μg/mL), about 3.6 times more active than the standard, gallic acid, which presented IC50 = 14.56 ± 0.13 μg/mL. Although the inhibition of extracellular matrix-degrading enzymes was not detected, the ethanol extract showed good inhibitory activity of tyrosinase, with an IC50 of 12.04 ± 0.23 μg/mL, only 6.6-fold lower than the control kojic acid. The results presented deepen the knowledge about a little studied species, opening new perspectives for the development of value-added applications in the food and cosmeceutical fields.
Going Deeper into the S of ESG: A Relational Approach to the Definition of Social Responsibility
Authors: Becchetti Leonardo , Bobbio Emanuele , Prizia Federico , Semplici Lorenzo
Published: August 2022

ESG frameworks have progressively become central in economic and policy choices. This is why it is of utmost importance to build a shared and accepted framework to define what we really mean by ESG overcoming the “minimalist” Do Not Significantly Harm (DNSH) principle and moving toward the full achievement of the more ambitious substantial contribution (SC) principle, oriented to the maximization of the social and environmental impact of value creation. To move forward in this direction, our work proposes a relational approach for the assessment of ESG factors focusing in particular on the social pillar. Our conceptual and theoretical proposal argues that, in order to increase the value of that pillar, it is necessary to assess both the internal and external relationships of the firm from an impact perspective, improving at the same time the multidimensional well-being of workers and the capacity to create sustainable development in the local community. The main factors companies should consider to achieve these goals are related to the domains of sense of community, empowerment, good practices of mutual aid and degree of participation at individual, team, organization, and territorial levels that can trigger gift giving, reciprocity and trust, overcoming standard social dilemmas and producing superadditive outcomes together with high social and environmental impact. Starting from these elements, this work proposes a set of indicators and metrics, based on an original methodology to measure and assess the commitment of a firm to increasing social factors. This methodology is particularly suitable for SMEs and start-up companies.
Background on the Sustainability of Knowledge
Authors: Bencsik Andrea
Published: August 2022

For years, sustainability has been on the minds of professionals, organisational leaders and everyone involved in everyday life. There has been a lot of research on different areas and processes of corporate operations, and more and more initiatives are emerging to address nature conservation, environmental protection and climate change issues. However, little research addresses the potential for sustainability of organisational knowledge, a factor that fundamentally influences the functioning of organisations. Beyond the steps of the knowledge management process, organisational culture, working conditions, the organisational environment and the organisational leadership that manages them are also at the forefront. The aim of this study is to highlight the supporting role of sustainable management for the sustainability of knowledge and to show the context of further supporting conditions. Previous research has identified sustainable management as an alternative management style that can significantly change organisations and society by deepening understanding of natural and economic systems and their interdependencies. Accordingly, it ensures market performance in a holistic approach based on the concept of sustainable knowledge and with a view to the efficient use of the company’s internal resources. The cornerstones of these interrelationships and the conditions of the relationships are presented here in a theoretical approach.
Effect of Sputtering Pressure on the Nanostructure and Residual Stress of Thin-Film YSZ Electrolyte
Authors: Teng Yue , Lee Ho Yeon , Lee Haesu , Lee Yoon Ho
Published: August 2022

Solid oxide fuel cells are energy conversion devices that contribute to carbon neutrality, with the advantages of high efficiency, clean emissions production, and distributed power generation. However, the high operating temperature of the solid oxide fuel cells causes system stability and material selection problems. In this study, we aimed to lower the operating temperature of a solid oxide fuel cell by reducing the thickness of the electrolyte via sputtering. The deposition process was conducted under various pressure conditions to find the optimal sputtering process for a gas-tight YSZ thin-film electrolyte. The gas-tightness of the YSZ electrolytes was evaluated by observing the nanostructure and cell performance. As a result, the YSZ thin-film deposited at 3 mTorr showed the best gas-tightness and cell performance. At 500 °C, 1.043 V of OCV and a maximum power density of 1593 mW/cm2 were observed. Then, X-ray diffraction was used to calculate the residual stress of the YSZ films. As a result, it was confirmed that the gas-tight film showed compressive residual stress. Through this study, we were successful in developing a room-temperature YSZ electrolyte fabrication process with excellent gas-tightness and performance. It was also proven that there is a strong relationship between the gas-tightness and residual stress. This study is expected to contribute to cost reductions and the mass production of solid oxide fuel cells.
Promoting Sustainability within the Nordic-Arctic Region’s Food System: Challenges and Trends
Authors: Raheem Dele , Holopainen Anu , Koegst Jana , Tulimaa Maija , Benkherouf Moaadh
Published: August 2022

The food system has direct impacts on our health both at individual and planetary levels; however, there is a need to make it more sustainable. Many communities in the Nordic-Arctic region are faced with challenges that arise from the need to protect natural resources due to increased activities that are fostered by tourism in the region. In this paper, we explore the interrelated factors that can sustainably support the food system in the region. A better understanding of these associated challenges in a complex food system from production to consumption is required. In order to ensure sustainability in the future, it will be necessary to explore the impacts of food tourism while responding to megatrends in the society. It is important that the natural resources in the communities of this region are better preserved for the next generation. Traditional knowledge and digital solutions can be harnessed to support food security and sovereignty that can empower local communities of the Nordic-Arctic region without compromising their heritage and sustainability.
Optimization of Ramp Locations along Freeways: A Dynamic Programming Approach
Authors: Chen Dawei , Mo Fangxu , Chen Ye , Zhang Jun , You Xinyu
Published: August 2022

Ramps provide entrances and exits for residents to conveniently use the freeway service. Due to the high construction cost and geometric design requirements, the decision of ramp locations involves a trade-off between multiple influencing factors, such as accessibility, safety, efficiency, construction costs, etc. This study proposed a methodology for optimizing freeway ramp placement in an effort to improve freeway accessibility. The freeway ramp locating problem was formulated as a bi-objective optimization model. Two objectives were pertinent to the reduction of total social costs: the minimization of total travel cost and minimization of total construction cost. To reflect the safety concern of ramp locations, the frequency of lane changes around the ramps and the minimum spacing between ramps were constrained. We developed an exact solution method based upon dynamic programming to solve the proposed model. Finally, a case study of the Beijing–Hong Kong–Macau Expressway within Henan Province, China, was conducted to verify the effectiveness of the proposed model and solution method.
Influence of Gender on Thermal, Air-Movement, Humidity and Air-Quality Perception in Mixed-Mode and Fully Air-Conditioned Offices
Authors: Maykot Jéssica Kuntz , Oliveira Candi Citadini de , Ghisi Enedir , Rupp Ricardo Forgiarini
Published: August 2022

As gender may influence thermal and air quality perception in indoor environments, the aim of this study was to analyse gender influence on air movement, air humidity, air quality and thermal perception in office buildings in Southern Brazil. Statistical descriptions, regression analyses and hypothesis tests were performed using data collected from field studies conducted in a fully air-conditioned building and in three mixed-mode buildings. In addition, comfort temperatures were estimated through the Griffiths method. Results showed that females tend to feel colder compared to males. Men and women tended to present higher thermal acceptability and thermal comfort in mixed-mode buildings and in fully air-conditioned buildings, respectively. Weak but significant correlations were obtained between some environmental and subjective variables. In general, comfort temperatures were statistically higher for females (24.2 °C) than for males (23.5 °C). Significant gender differences for thermal perceptions of indoor environments were detected.