bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Religious Undergraduates’ Career Development: The Salient Roles of Religious Calling, Life Satisfaction, and Quest Religiosity
Authors: Puffer Keith A. , Brooks Reka , Davis Emily
Published: May 2023

Americans, over several decades, have consistently identified themselves as being religious and/or spiritual. Even though religious devotion has been recognized as a relevant diversity facet, therapeutic applications have been problematic. In the career development literature, numerous studies examined the influence of religion/spirituality (R/S) and generally categorized them in three ways. These include R/S as religious calling, as an integral feature of career interests and values, and as a supportive role. Yet, notable gaps exist among the articles. Researchers tended to operationalize R/S in a unidimensional manner, as a stand-alone construct instead of comprising multiple features. Investigations of religious calling in vocational matters were not recent. Further, few authors considered the influence of quest religious orientation on career development. In the present study, the relationships between seven career development and thirteen R/S variables using a sample of religious undergraduates (n = 290) enrolled in a career exploration course at a Christian university in the Midwest region of the United States were investigated. Findings from hierarchical regression analyses of vocational identity, career commitment, and career indecision checking for gender effects revealed three consistent salient predictors – religious calling, life-satisfaction, and quest religiosity. Implications of the results and possible therapeutic applications for career counselors are proposed.
Process of Learning from Demonstration with Paraconsistent Artificial Neural Cells for Application in Linear Cartesian Robots
Authors: Da Silva Filho João Inácio , Fernandes Cláudio Luís Magalhães , Silveira Rodrigo Silvério da , Gomes Paulino Machado , Matos Sérgio Luiz da Conceição , Santo Leonardo do Espirito , Nunes Vander Célio , Côrtes Hyghor Miranda , Lopes William Aparecido Celestino , Mario Mauricio Conceição , Garcia Dorotéa Vilanova , Torres Cláudio Rodrigo , Abe Jair Minoro , Lambert-Torres Germano
Published: May 2023

Paraconsistent Annotated Logic (PAL) is a type of non-classical logic based on concepts that allow, under certain conditions, for one to accept contradictions without invalidating conclusions. The Paraconsistent Artificial Neural Cell of Learning (lPANCell) algorithm was created from PAL-based equations. With its procedures for learning discrete patterns being represented by values contained in the closed interval between 0 and 1, the lPANCell algorithm presents responses similar to those of nonlinear dynamical systems. In this work, several tests were carried out to validate the operation of the lPANCell algorithm in a learning from demonstration (LfD) framework applied to a linear Cartesian robot (gantry robot), which was moving rectangular metallic workpieces. For the LfD process used in the teaching of trajectories in the x and y axes of the linear Cartesian robot, a Paraconsistent Artificial Neural Network (lPANnet) was built, which was composed of eight lPANCells. The results showed that lPANnet has dynamic properties with a robustness to disturbances, both in the learning process by demonstration, as well as in the imitation process. Based on this work, paraconsistent artificial neural networks of a greater complexity, which are composed of lPANCells, can be formed. This study will provide a strong contribution to research regarding learning from demonstration frameworks being applied in robotics.
Activities to Promote the Moon as an Absolute Calibration Reference
Authors: Jing Zhenhua , Hu Xiuqing , Wang Yang , Wu Ronghua , Chen Lin , Zhang Lu , Huang Yu , Wang Shuang , Li Shuang , Zhang Peng
Published: May 2023

The accuracy and consistency of Earth observation (EO) instrument radiometric calibration is a fundamental prerequisite for achieving accurate results and delivering reliable predictions. Frequent calibration and validation (Cal/Val) activities are needed during the instrument’s lifetime, and this procedure is often extended to historical archives. Numerous satellites in orbit and proposed future missions have incorporated lunar observation into their vicarious calibration components over recent years, facilitated by the extreme long-term photometric stability of the Moon. Since the birth of the first lunar calibration reference model, lunar-dependent calibration techniques have developed rapidly, and the application and refinement of the lunar radiometric model have become a welcome research focus in the calibration community. Within the context of the development of lunar observation activities and calibration systems globally, we provide a comprehensive review of the activities and results spawned by treating the Moon as a reference for instrument response and categorize them against the understanding of lunar radiometric reference. In general, this appears to be a process of moving from data to instruments, then back into data, working towards a stated goal. Here we highlight lunar radiometric models developed by different institutions or agencies over the last two decades while reporting on the known limitations of these solutions, with unresolved challenges remaining and multiple lunar observation plans and concepts attempting to address them from various perspectives, presenting a temporal development. We also observe that the methods seeking uncertainty reduction at this stage are rather homogeneous, lacking the combination of approaches or results from lunar surface studies conducted by many spacecraft missions, and joint deep learning methods to extract information. The factors that influence the accuracy of the measurement irradiance may be regulated when practical models arrive. As a central element in lunar calibration, the development of an absolute radiometric datum helps to better understand the Earth system.
An Improved Backward Smoothing Method Based on Label Iterative Processing
Authors: Zhao Jiuchao , Zhan Ronghui , Zhuang Zhaowen , Li Kun , Deng Bing , Peng Huafeng
Published: May 2023

Effective target detection and tracking has always been a research hotspot in the field of radar, and multi-target tracking is the focus of radar target tracking at present. In order to effectively deal with the issue of outlier removal and track initiation determination in the process of multi-target tracking, this paper proposes an improved backward smoothing method based on label iterative processing. This method corrects the loophole in the original backward smoothing method, which can cause estimated target values to be erroneously removed due to missing detection, so that it correctly removes outliers in target tracking. In addition, the proposed method also combines label iterative processing with track initiation determination to effectively eliminate invalid target short-lived tracks. The results of simulation experiments and actual data verification showed that the proposed method correctly removed outliers and invalid short-lived tracks. Compared with the original method, it improved the accuracy of target cardinality estimation and tracking performance to a certain extent. Moreover, without affecting the algorithm performance, the method’s processing efficiency could be improved by increasing the track pruning threshold. Finally, the proposed method was compared with existing methods, verifying that its tracking performance was better than that of existing methods.
Hybrid Domain Efficient Modulation-Based Deceptive Jamming Algorithm for Nonlinear-Trajectory Synthetic Aperture Radar
Authors: Dong Jiaming , Zhang Qunying , Lu Wei , Cheng Wenhai , Liu Xiaojun
Published: May 2023

Deception jamming of synthetic aperture radar (SAR) has attracted extensive attention due to its low power consumption and high fidelity advantages. However, existing SAR deception jamming algorithms assume that SAR operates on a linear trajectory. In practice, SAR trajectories often become nonlinear due to factors such as atmospheric turbulence, which results in the jamming signals lacking the two-dimensional spatial variability of nonlinear-trajectory SAR echo signal and affects the imaging quality of deception jamming. This paper proposes a new algorithm for nonlinear-trajectory airborne SAR deception jamming based on hybrid domain efficient (HDE) modulation. This algorithm derives the jamming frequency response (JFR) with SAR trajectory deviation in the azimuth time–frequency hybrid domain. Based on the hybrid domain modulation, the jammer calculates the JFR of the linear trajectory in the azimuth frequency domain and constructs for the real-time trajectory deviation pulse by pulse at each azimuth moment. The real-time modulation process of the algorithm only involves range domain Fourier transform and complex multiplication, combining computational efficiency and modulation flexibility. The validity constraints of the algorithm have been analyzed to ensure the focusing ability of the jamming signal. Simulation and computational complexity analysis validate the excellent performance of the algorithm in imaging quality and efficiency.
A Scale Conversion Model Based on Deep Learning of UAV Images
Authors: Qiu Xingchen , Gao Hailiang , Wang Yixue , Zhang Wei , Shi Xinda , Lv Fengjun , Yu Yanqiu , Luan Zhuoran , Wang Qianqian , Zhao Xiaofei
Published: May 2023

As a critical component of many remote sensing satellites and model validation, pixel-scale surface quantitative parameters are often affected by scale effects in the acquisition process, resulting in deviations in the accuracy of image scale parameters. Consequently, various successive scale conversion methods have been proposed to correct the errors caused by scale effects. In this study, we propose ResTransformer, a deep learning model for scale conversion of surface reflectance using UAV images, which fully extracts and fuses the features of UAV images in the sample area and sample points and establishes a high-dimensional nonlinear spatial correlation between sample points and sample area in the target sample area, so that the scale conversion of surface reflectance at the pixel-scale can be completed quickly and accurately. We collected and created a dataset of 500k samples to verify the accuracy and robustness of the model with other traditional scale conversion methods. The results show that the ResTransformer deep learning model works best, providing average MRE, average MRSE, and correlation coefficient R values of 0.6440%, 0.7460, and 0.99911, respectively, and the baseline improvements compared with the Simple Average method are 92.48%, 92.45%, and 16.59%, respectively. The ResTransformer model also shows the highest robustness and universality and can adapt to surface pixel-scale conversion scenarios with different sizes, heterogeneous sample areas, and arbitrary sampling methods. This method provides a promising, highly accurate, and robust method for converting pixel-scale surface reflectance scale.
Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques
Authors: Shahi Tej Bahadur , Xu Cheng-Yuan , Neupane Arjun , Guo William
Published: May 2023

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution
Authors: Berga David , Gallés Pau , Takáts Katalin , Mohedano Eva , Riordan-Chen Laura , Garcia-Moll Clara , Vilaseca David , Marín Javier
Published: May 2023

The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW, which has been developed for the evaluation of image quality and the detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features such as blur, sharpness, snr, rer and ground sampling distance and obtained validation medRs below 1.0 (out of N = 50) and recall rates above 95%. The overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as a loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.
A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies
Authors: Liberti Gian Luigi , Sabatini Mattia , Wethey David S. , Ciani Daniele
Published: May 2023

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.
Research on the Relationship between the Structure of Forest and Grass Ecological Spaces and Ecological Service Capacity: A Case Study of the Wuding River Basin
Authors: Zeng Yufan , Yu Qiang , Wang Xiaoci , Ma Jun , Xu Chenglong , Qiu Shi , Liu Wei , Wang Fei
Published: May 2023

In recent years, the accelerated pace of urbanization has increased patch fragmentation, which has had a certain impact on the structure and ecological environment of forest–grass ecological networks, and certain protection measures have been taken in various regions. Therefore, studying the spatiotemporal changes and correlations of ecological service functions and forest–grass ecological networks can help to better grasp the changes in landscape ecological structure and function. This paper takes the Wuding River Basin as the research area and uses the windbreak and sand fixation service capacity index, soil conservation capacity, and net primary productivity (NPP) to evaluate the ecological service capacity of the research area from the three dimensions of windbreak and sand fixation, soil conservation, and carbon sequestration. The Regional Sustainability and Environment Index (RSEI) is used to extract ecological source areas, and GIS spatial analysis and the minimum cumulative resistance (MCR) model are used to extract potential ecological corridors. Referring to complex network theory, topology metrics such as degree distribution and clustering coefficient are calculated, and their correlation with ecological service capacity is explored. The results show that the overall ecological service capacity of sand fixation, soil fixation, and carbon sequestration in the research area in 2020 has increased compared to 2000, and the ecological flow at the northern and northwest boundaries of the river basin has been enhanced, but there are still shortcomings such as fragmented ecological nodes, a low degree of clustering, and poor connectivity. In terms of the correlation between topology indicators and ecological service functions, the windbreak and sand fixation service capacity index have the strongest correlation with clustering and the largest grasp, while the correlation between soil conservation capacity and eigencentrality is the strongest and has the largest grasp. The correlation between NPP and other indicators is not obvious, and its correlation with eccentricity and eigencentrality is relatively large.
A Localization and Tracking System Using Single WiFi Link
Authors: Tian Li-Ping , Chen Liang-Qin , Xu Zhi-Meng , Chen Zhizhang (David)
Published: May 2023

Like its outdoor counterpart (e.g., GPS), an indoor tracking system can bring about disruptive changes in how we live and work. This paper proposes a location and tracking system using a single WiFi link based on channel state information. The system can realize real-time, decimeter-level localization and tracking. In this system, phase calibration and static path elimination are realized by multiplying the conjugate signals of different antennas. Then, a three-dimensional MUSIC algorithm is employed to estimate the angle of arrival (AOA), the time of flight (TOF), and the velocity of a target. A scheme is then developed to adjust the MUSIC search range and reduce the computation time from about ten hours to tens of seconds. The Widar2.0 data set from Tsinghua University are used for the experiments; the proposed system is found to have an average tracking error of 0.68 m in the three environments of classroom, office, and corridor, which is better than the existing single link localization and tracking system.
Ocean Eddies in the Drake Passage: Decoding Their Three-Dimensional Structure and Evolution
Authors: Lin Xiayan , Zhao Hui , Liu Yu , Han Guoqing , Zhang Han , Liao Xiaomei
Published: May 2023

The Drake Passage is known for its abundant mesoscale eddies, but little is known about their three-dimensional characteristics, which hinders our understanding of their impact on eddy-induced transport and deep-sea circulation. A 10-year study was conducted using GLORYS12 Mercator Ocean reanalysis data from 2009 to 2018. The study analyzed the statistical characteristics of eddies in the Drake Passage, spanning from the surface down to a depth of 2000 m in three dimensions. The findings indicate that the mean radius of the eddies is 35.5 km, with a mean lifespan of 12.3 weeks and mean vorticity of 2.2 × 10−5 s−1. The eddies are most active and energetic near the three main fronts and propagate north-eastward at an average distance of 97.8 km. The eddy parameters vary with water depth, with more anticyclones detected from the surface to 400 m, displaying a larger radius and longer propagation distance. Cyclones have longer lifespans and greater vorticity. However, beyond 400 m, there is not much difference between anticyclones and cyclones. Approximately 23.3% of the eddies reach a depth of 2000 m, with larger eddies tending to penetrate deeper. The eddies come in three different shapes, bowl-shaped (52.7%), lens-shaped (27.1%) and cone-shaped (20.2%). They exhibit annual and monthly distribution patterns. Due to its high latitude location, the Drake Passage has strong rotation and weak stratification, resulting in the generation of small and deep-reaching eddies. These eddies contribute to the formation of Antarctic intermediate water and lead to modulation of turbulent dissipation.
High-Resolution and Wide-Swath SAR Imaging with Space–Time Coding Array
Authors: Yu Kun , Zhu Shengqi , Lan Lan , Yang Biao
Published: May 2023

To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR).
Adapting Static and Contextual Representations for Policy Gradient-Based Summarization
Authors: Lin Ching-Sheng , Jwo Jung-Sing , Lee Cheng-Hsiung
Published: May 2023

Considering the ever-growing volume of electronic documents made available in our daily lives, the need for an efficient tool to capture their gist increases as well. Automatic text summarization, which is a process of shortening long text and extracting valuable information, has been of great interest for decades. Due to the difficulties of semantic understanding and the requirement of large training data, the development of this research field is still challenging and worth investigating. In this paper, we propose an automated text summarization approach with the adaptation of static and contextual representations based on an extractive approach to address the research gaps. To better obtain the semantic expression of the given text, we explore the combination of static embeddings from GloVe (Global Vectors) and the contextual embeddings from BERT (Bidirectional Encoder Representations from Transformer) and GPT (Generative Pre-trained Transformer) based models. In order to reduce human annotation costs, we employ policy gradient reinforcement learning to perform unsupervised training. We conduct empirical studies on the public dataset, Gigaword. The experimental results show that our approach achieves promising performance and is competitive with various state-of-the-art approaches.
Uneven Terrain Recognition Using Neuromorphic Haptic Feedback
Authors: Prasanna Sahana , D’Abbraccio Jessica , Filosa Mariangela , Ferraro Davide , Cesini Ilaria , Spigler Giacomo , Aliperta Andrea , Dell’Agnello Filippo , Davalli Angelo , Gruppioni Emanuele , Crea Simona , Vitiello Nicola , Mazzoni Alberto , Oddo Calogero Maria
Published: May 2023

Recent years have witnessed relevant advancements in the quality of life of persons with lower limb amputations thanks to the technological developments in prosthetics. However, prostheses that provide information about the foot–ground interaction, and in particular about terrain irregularities, are still missing on the market. The lack of tactile feedback from the foot sole might lead subjects to step on uneven terrains, causing an increase in the risk of falling. To address this issue, a biomimetic vibrotactile feedback system that conveys information about gait and terrain features sensed by a dedicated insole has been assessed with intact subjects. After having shortly experienced both even and uneven terrains, the recruited subjects discriminated them with an accuracy of 87.5%, solely relying on the replay of the vibrotactile feedback. With the objective of exploring the human decoding mechanism of the feedback startegy, a KNN classifier was trained to recognize the uneven terrains. The outcome suggested that the subjects achieved such performance with a temporal dynamics of 45 ms. This work is a leap forward to assist lower-limb amputees to appreciate the floor conditions while walking, adapt their gait and promote a more confident use of their artificial limb.
Magnetoelectric Effect in Amorphous Ferromagnetic FeCoSiB/Langatate Monolithic Heterostructure for Magnetic Field Sensing
Authors: Fetisov L. Y. , Dzhaparidze M. V. , Savelev D. V. , Burdin D. A. , Turutin A. V. , Kuts V. V. , Milovich F. O. , Temirov A. A. , Parkhomenko Y. N. , Fetisov Y. K.
Published: May 2023

This paper investigates the possibilities of creating magnetic field sensors using the direct magnetoelectric (ME) effect in a monolithic heterostructure of amorphous ferromagnetic material/langatate. Layers of 1.5 μm-thick FeCoSiB amorphous ferromagnetic material were deposited on the surface of the langatate single crystal using magnetron sputtering. At the resonance frequency of the structure, 107 kHz, the ME coefficient of linear conversion of 76.6 V/(Oe∙cm) was obtained. Furthermore, the nonlinear ME effect of voltage harmonic generation was observed with an increasing excitation magnetic field. The efficiency of generating the second and third harmonics was about 6.3 V/(Oe2∙cm) and 1.8 V/(Oe3∙cm), respectively. A hysteresis dependence of ME voltage on a permanent magnetic field was observed due to the presence of α-Fe iron crystalline phases in the magnetic layer. At the resonance frequency, the monolithic heterostructure had a sensitivity to the AC magnetic field of 4.6 V/Oe, a minimum detectable magnetic field of ~70 pT, and a low level of magnetic noise of 0.36 pT/Hz1/2, which allows it to be used in ME magnetic field sensors.
Design and Implementation of Three-Channel Drainage Pipeline Ground Penetrating Radar Device
Authors: Xu Maoxuan , Yang Feng , Yan Rui
Published: May 2023

In order to solve the current problems that conventional video inspection can only detect, as an internal pipeline defect and drainage pipeline radar inspection device detects in a single direction and at radar frequency in water pipeline defect detection, a three-channel drainage pipeline ground penetrating radar (GPR) inspection device was designed and developed, the assembly and commissioning of the device prototype were completed, and an actual engineering test application was carried out. Focusing on the problem that the detection direction and depth of the single-channel detection device are limited, a three-channel drainage pipeline GPR inspection device is designed to realize the synchronous detection of the inside of the pipeline, the pipeline body, and the external environment of the pipeline, improving the detection depth and efficiency. According to the design scheme of the three-channel drainage pipeline GPR inspection device, the assembly of the device prototype was completed. The device contains three radar channels, the top of the main frequency of the antenna is 1.4 GHz, the two sides are 750 MHz, the video camera has a pixel count of 4 million, and the positioning accuracy is less than 1 mm, the waterproof grade is IP68, the detection accuracy of pipe deformation (slope) is 0.1°, the detection depth outside the pipe is 1.2 m, and the detection accuracy of corrosion thickness is 15 mm. In a practical application of the device, the Jianguomenqiao sewage pipeline in Beijing, China, was tested, resulting in the discovery of 87 defects, including 39 loose soil areas at the bottom of the pipe exterior, 40 void areas, and 8 cavities.
Stereoscopic UWB Yagi–Uda Antenna with Stable Gain by Metamaterial for Vehicular 5G Communication
Authors: Fu Yuanxu , Shen Tao , Dou Jiangling , Chen Zhe
Published: May 2023

In this paper, a stereoscopic ultra-wideband (UWB) Yagi–Uda (SUY) antenna with stable gain by near-zero-index metamaterial (NZIM) has been proposed for vehicular 5G communication. The proposed antenna consists of magneto-electric (ME) dipole structure and coaxial feed patch antenna. The combination of patch antenna and ME structure allows the proposed antenna can work as a Yagi–Uda antenna, which enhances its gain and bandwidth. NZIM removes a pair of C-notches on the surface of the ME structure to make it absorb energy, which results in two radiation nulls on both sides of the gain passband. At the same time, the bandwidth can be enhanced effectively. In order to further improve the stable gain, impedance matching is achieved by removing the patch diagonally; thus, it is able to tune the antenna gain of the suppression boundary and open the possibility to reach the most important characteristic: a very stable gain in a wide frequency range. The SUY antenna is fabricated and measured, which has a measured −10 dBi impedance bandwidth of approximately 40% (3.5–5.5 GHz). Within it, the peak gain of the antenna reaches 8.5 dBi, and the flat in-band gain has a ripple lower than 0.5 dBi.
1-D and 2-D Direction of Arrival Estimation in a Conical Conformal Array: Design and Implementation
Authors: Zhang Hongyun , Li Ping , Zhang Guangwei , Li Guolin , Gao Xiaofeng
Published: May 2023

Direction of arrival (DOA) estimation for conformal arrays is challenging due to non-omnidirectional element patterns and shadow effects. Conical conformal array (CCA) can avoid the shadow effect at small elevation angles. So CCA is suitable for DOA estimation on both azimuth and elevation angles at small elevation angles. However, the element pattern in CCA cannot be obtained by conventional directional element coordinate transformation. Its local element pattern also has connection with the cone angle. The paper establishes the CCA radiation pattern in local coordinate system using 2-D coordinate transformation. In addition, in the case of large elevation angle, only half elements of the CCA can receive signal due to the shadow effect. The array degrees of freedom (DOF) are reduced by halves. We introduce the difference coarray method, which increases the DOF. Moreover, we propose a more accurate propagator method for 2-D cases. This method constructs a new propagation matrix and reduces the estimation error. In addition, this method reduces computational complexity by using linear computations instead of eigenvalue decomposition (EVD) and avoids spectral search. Simulation and experiment verify the estimation performance of the CCA. Both demonstrate the CCA model established in this paper is corresponding to the designed CCA antenna, and the proposed algorithms meet the needs of CCA angle detection. When the number of array elements is 12, the estimation accuracy is about 5 degrees.
Off-Screen Sound Separation Based on Audio-visual Pre-training Using Binaural Audio
Authors: Yoshida Masaki , Togo Ren , Ogawa Takahiro , Haseyama Miki
Published: May 2023

This study proposes a novel off-screen sound separation method based on audio-visual pre-training. In the field of audio-visual analysis, researchers have leveraged visual information for audio manipulation tasks, such as sound source separation. Although such audio manipulation tasks are based on correspondences between audio and video, these correspondences are not always established. Specifically, sounds coming from outside a screen have no audio-visual correspondences and thus interfere with conventional audio-visual learning. The proposed method separates such off-screen sounds based on their arrival directions using binaural audio, which provides us with three-dimensional sensation. Furthermore, we propose a new pre-training method that can consider the off-screen space and use the obtained representation to improve off-screen sound separation. Consequently, the proposed method can separate off-screen sounds irrespective of the direction from which they arrive. We conducted our evaluation using generated video data to circumvent the problem of difficulty in collecting ground truth for off-screen sounds. We confirmed the effectiveness of our methods through off-screen sound detection and separation tasks.
Saliency Map and Deep Learning in Binary Classification of Brain Tumours
Authors: Chmiel Wojciech , Kwiecień Joanna , Motyka Kacper
Published: May 2023

The paper was devoted to the application of saliency analysis methods in the performance analysis of deep neural networks used for the binary classification of brain tumours. We have presented the basic issues related to deep learning techniques. A significant challenge in using deep learning methods is the ability to explain the decision-making process of the network. To ensure accurate results, the deep network being used must undergo extensive training to produce high-quality predictions. There are various network architectures that differ in their properties and number of parameters. Consequently, an intriguing question is how these different networks arrive at similar or distinct decisions based on the same set of prerequisites. Therefore, three widely used deep convolutional networks have been discussed, such as VGG16, ResNet50 and EfficientNetB7, which were used as backbone models. We have customized the output layer of these pre-trained models with a softmax layer. In addition, an additional network has been described that was used to assess the saliency areas obtained. For each of the above networks, many tests have been performed using key metrics, including statistical evaluation of the impact of class activation mapping (CAM) and gradient-weighted class activation mapping (Grad-CAM) on network performance on a publicly available dataset of brain tumour X-ray images.
Validity and Reliability of the activPAL4TM for Measurement of Body Postures and Stepping Activity in 6–12-Year-Old Children
Authors: Burahmah Esraa , Shanmugam Sivaramkumar , Williams Daniel , Stansfield Ben
Published: May 2023

A link between inappropriate physical behaviour patterns (low physical activity and high sedentary behaviour) and poor health outcomes has been observed. To provide evidence to quantify this link, it is important to have valid and reliable assessment tools. This study aimed to assess the validity and reliability of the activPAL4TM monitor for distinguishing postures and measuring stepping activity of 6–12-year-old children. Thirteen children (8.5 ± 1.8 years) engaged in pre-determined standardised (12 min) and non-standardised (6 min) activities. Agreement, specificity and positive predictive value were assessed between the activPAL4TM and direct observation (DO) (nearest 0.1 s). Between-activPAL4TM (inter-device) and between-observer (inter-rater) reliability were determined. Detection of sitting and stepping time and forward purposeful step count were all within 5% of DO. Standing time was slightly overestimated (+10%) and fast walking/jogging steps underestimated (−20%). For non-standardised activities, activPAL4TM step count matched most closely to combined backward and forward purposeful steps; however, agreement varied widely. The activPAL4TM demonstrated high levels of reliability (ICC(1, 1) > 0.976), which were higher in some instances than could be achieved through direct observation (ICC(2, 1) > 0.851 for non-standardised activities). Overall, the activPAL4TM recorded standardised activities well. However, further work is required to establish the exact nature of steps counted by the activPAL4TM.
Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application
Authors: Shih Chi-Huang , Lin Cheng-Jian , Jhang Jyun-Yu
Published: May 2023

With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.
Blood Flow Restriction Is Not Useful as Soccer Competition Recovery in Youth Male National-Level Soccer Players: A Crossover Randomised Controlled Trial
Authors: Castilla-López Christian , Romero-Franco Natalia
Published: May 2023

In soccer, blood flow restriction (BFR) is used to optimise between-match recovery. However, the benefits are unclear. This study evaluated the effects of BFR as a recovery strategy after a competition on countermovement jump (CMJ) height, rating of perceived exertion (RPE) and the wellness of soccer players. Forty national-level soccer players were allocated into two conditions: BFR (an active recovery session wearing a BFR device, 24 h after a competition) or NoBFR (the same recovery without BFR). CMJ, RPE and wellness were evaluated the day (CMJ and RPE) or the morning (wellness) before the competition; just after the competition (CMJ and RPE); and 24, 48 (wellness) and 72 h later. After 4 weeks, the players changed conditions. All players showed impaired CMJ (p = 0.013), RPE (p < 0.001) and wellness (p < 0.001) after the match compared with the baseline. The CMJ returned to the baseline 24 h later and wellness returned 48 h later. Only in the BFR condition did the RPE remain impaired 24 h after the match, which was also the moment after finishing the BFR recovery session (p < 0.001). BFR during active recovery does not provide any additional benefits compared with traditional exercise modalities to recover CMJ, RPE and wellness in youth national-level soccer players. BFR could even induce an immediate higher RPE.
Research on Dynamic Optimization and Coordination Strategy of Value Co-Creation in Digital Innovation Ecosystems
Authors: Ji Huimin , Zou Hua , Liu Baotong
Published: May 2023

Based on the theory of differential games and guided by the realization of value co-creation, this paper discusses the value co-creation of a technology innovation platform, scientific innovation layer, and support layer in the digital innovation ecosystem. Given the dynamic change characteristics of digital technology innovation and resource integration, this paper constructs a differential game decision model. The conclusions are as follows: (1) The Stackelberg master–slave game and collaborative model have incentive effects. The returns in both methods increase over time and finally reach a stable value. In the collaborative game model, the effort level of participants is the highest and realizes the Pareto optimality. (2) The digital technology innovation capacity coefficient, digital technology assimilation capacity and absorption capacity coefficient, and resource integration cost coefficient are the key factors affecting the optimal return of the innovation ecosystem. (3) The two-way cost-sharing path can balance the innovation ecosystem, in which the technology innovation platform shares the cost and provides incentives to the scientific innovation layer and the support layer. The sharing ratio and incentive degree are positively correlated with the benefit of value co-creation. However, if the income distribution coefficient is not appropriately set, the participants’ income will decrease.
Solving Bi-Objective Vehicle Routing Problems with Driving Risk Consideration for Hazardous Materials Transportation
Authors: Chai Huo , He Ruichun , Kang Ronggui , Jia Xiaoyan , Dai Cunjie
Published: May 2023

Driving behavior is an important factor affecting the risk of hazardous materials transportation. In this paper, we propose a transport risk evaluation method that considers driving risk. We consider driving risk and establish a model of vehicle routing problems with a soft time window for the transportation of hazardous materials and design a non-dominated genetic algorithm to solve the bi-objective optimization model. Taking a network of 23 nodes and 38 road segments as an example, 59 pareto-optimal solutions were obtained for six drivers on nine different paths. Comparing different solutions, it was found that driving risk, road population density, and transportation distance have different impacts on transport cost and risk. Choosing drivers and routes can adjust the propensity of cost and risk, allowing the decision-maker to select a solution for allocating drivers and routing vehicles according to their risk preference.
Optimization of Green Containerized Grain Supply Chain Transportation Problem in Ukraine Considering Disruption Scenarios
Authors: Li Jingwen , Jing Ke , Khimich Myroslav , Shen Lixin
Published: May 2023

Grain supply chain transportation problem is a nontrivial and intractable issue for many developing countries. Grain as a bulk commodity is usually transported by bulk carriers. By taking into account the special condition of Ukraine, we proposed the containerized grain supply chain transportation optimization problem. In this problem, the sustainable supply chain system delivers grains in containers among primary elevators, intermodal yards, and port container terminals. Then, a containerized grain transportation model was developed to minimize the total cost of the sustainable supply chain system. Specifically, 20-foot containers were proven as more reasonable to be used in this paper. We also considered different transportation tools: trucks that can load one 20-foot container, trailers that can load two 20-foot containers, and wagons that can hold two 20-foot containers. Additionally, a disruption model was proposed by considering different disruption scenarios. Based on an analysis of the simulation results, some cost minimization strategies were proposed. Finally, a sensitivity analysis that aimed to analyze the effect of the proposed strategies on the minimal total cost and sustainability of the supply chain was conducted. The main conclusions drawn from the simulation are that the established food supply chain model is meaningful and accurate, and the incorporation of the disruption model aligns with practical requirements. Additionally, an increase in intermodal yard capacity, truck park size, and wagon park size decreases the total cost of the supply chain. The first two have a positive effect on the sustainability of the supply chain, while the latter increases the disruption risk of the supply chain.
Bio-Remediation of Heavy Metal-Contaminated Soil by Microbial-Induced Carbonate Precipitation (MICP)—A Critical Review
Authors: Wang Sheng , Fang Longyang , Dapaah Malcom Frimpong , Niu Qijian , Cheng Liang
Published: May 2023

Biomineralization processes utilizing microbial-induced carbonate precipitation (MICP) have recently shown promise as an effective approach for remediating heavy metal contamination. This article offers a comprehensive review of the latest research on MICP-mediated heavy metal remediation, with a focus on the characteristics of heavy metals in the treated environment, such as copper, cadmium, lead, nickel, zinc, chromium, and mixed heavy metals. The review summarizes experimental results from various heavy metals treated by MICP, including the enrichment and screening of new urease-positive bacteria, the mineral structure of different heavy metal precipitates, and the efficiency of the MICP technology. Recent advancements in the MICP technology regarding heavy metal removal, long-term stability, and practical applications are also discussed. Additionally, the limitations of the technique and existing solutions are reviewed. In addition, it provides insights on future directions for further research and development of the MICP approach for heavy metal remediation, in order to optimize the technique and improve its efficiency. Overall, the review highlights the potential of MICP as a viable method for heavy metal remediation, offering promising results for the removal of a variety of heavy metal contaminants from contaminated environments.
A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction
Authors: Li Pengfei , Zhang Tong , Jin Yantao
Published: May 2023

Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards.