bluuebunny/crossref_metadata_2025 huggingface dataset:
The Epistemic Value of Non-Religious Mystical Experiences
Authors: Bledow Nona
Published: November 2022

This paper examines the epistemic value of non-religious mystical experiences. By taking a non-religious angle, it adds a complementary perspective to the context in which mystical experiences are generally discussed, i.e., the context of theological questions or perspectives informed by the philosophy of religion. While I am pessimistic about the possibilities of such experiences providing propositional knowledge about the external world, this discussion is largely bracketed. Instead, I focus on a different type of knowledge, arguing that what these experiences can provide is a certain type of subjective knowledge, namely experiential knowledge. I further argue that such experiences involve a feeling of concern about a very general object, something such as existence, the world, or reality as a whole. Consciously experiencing this type of feeling or emotion is rare, since it is a background emotion about a very general object relatively far removed from personal flourishing. Nevertheless, in this type of experiences, it is directly experienced. The experiential knowledge obtained through such experiences is what this general concern about existence as a whole feels like. I contend that both the insight on what this feels like as well as the feeling itself can be seen as valuable.
Using a Two-Stage Scheme to Map Toxic Metal Distributions Based on GF-5 Satellite Hyperspectral Images at a Northern Chinese Opencast Coal Mine
Authors: Guo Bin , Guo Xianan , Zhang Bo , Suo Liang , Bai Haorui , Luo Pingping
Published: November 2022

Toxic metals have attracted great concern worldwide due to their toxicity and slow decomposition. Although metal concentrations can be accurately obtained with chemical methods, it is difficult to map metal distributions on a large scale due to their inherently low efficiency and high cost. Moreover, chemical analysis methods easily lead to secondary contamination. To address these issues, 110 topsoil samples were collected using a soil sampler, and positions for each sample were surveyed using a global navigation satellite system (GNSS) receiver from a coal mine in northern China. Then, the metal contents were surveyed in a laboratory via a portable X-ray fluorescence spectroscopy (XRF) device, and GaoFen-5 (GF-5) satellite hyperspectral images were used to retrieve the spectra of the soil samples. Furthermore, a Savitzky–Golay (SG) filter and continuous wavelet transform (CWT) were selected to smooth and enhance the soil reflectance. Competitive adaptive reweighted sampling (CARS) and Boruta algorithms were utilized to identify the feature bands. The optimum two-stage method, consisting of the random forest (RF) and ordinary kriging (OK) methods, was used to infer the metal concentrations. The following outcomes were achieved. Firstly, both zinc (Zn) (68.07 mg/kg) and nickel (Ni) (26.61 mg/kg) surpassed the regional background value (Zn: 48.60 mg/kg, Ni: 19.5 mg/kg). Secondly, the optimum model of RF, combined with the OK (RFOK) method, with a relatively higher coefficient of determination (R2) (R2 = 0.60 for Zn, R2 = 0.30 for Ni), a lower root-mean-square error (RMSE) (RMSE = 12.45 mg/kg for Zn, RMSE = 3.97 mg/kg for Ni), and a lower mean absolute error (MAE) (MAE = 9.47 mg/kg for Zn, MAE = 3.31mg/kg for Ni), outperformed the other four models, including the RF, OK, inverse distance weighted (IDW) method, and the optimum model of RF combined with IDW (RFIDW) method in estimating soil Zn and Ni contents, respectively. Thirdly, the distribution of soil Zn and Ni concentrations obtained from the best-predicted method and the GF-5 satellite hyperspectral images was in line with the actual conditions. This scheme proves that satellite hyperspectral images can be used to directly estimate metal distributions, and the present study provides a scientific base for mapping heavy metal spatial distribution on a relatively large scale.
Laser Scanning Investigation and Geophysical Monitoring to Characterise Cultural Heritage Current State and Threat by Traffic-Induce Vibrations: The Villa Farnesina in Rome
Authors: Costanzo Antonio , Falcone Sergio , La Piana Carmelo , Lapenta Virginia , Musacchio Massimo , Sgamellotti Antonio , Buongiorno Maria Fabrizia
Published: November 2022

A multidisciplinary approach is often the only way to assess the state of the cultural heritage, thus involving different specialist expertise and techniques. The paper shows the paired use of terrestrial laser scanning (TLS) and geophysical monitoring (GM) to detect past effects and analyse the actual vibration levels induced by traffic on cultural heritage. The case study is the Villa Farnesina, one of the most important Renaissance buildings commissioned by the banker Agostino Chigi. The Villa contains frescoes attributed to Raphael and other famous 16th century artists, and it is located a few meters from the Lungotevere, which is one of the busiest roads in the historic centre of Rome. Testimonies report the damages caused by the construction of the embankment of the Tiber River, as well as by the traffic in the second half of the 20th century, so much so as to require requalification of the road artery. The TLS survey allows for detecting cracks and deteriorations of the frescoes, although these were subjected to restoration activities over the time, whereas the (GM) allows analysing actual vibrations induced by traffic at the different floors and outside the Villa. Although the measured vibration limits, as velocity peaks in defined frequency ranges, are below the thresholds established by international codes, the importance of the wall paintings and their already-shown susceptibility to damage suggest keeping the building under constant monitoring.
Spatiotemporal Characteristics of Soil Moisture and Land–Atmosphere Coupling over the Tibetan Plateau Derived from Three Gridded Datasets
Authors: Wang Huimin , Zan Beilei , Wei Jiangfeng , Song Yuanyuan , Mao Qianqian
Published: November 2022

Soil moisture is a crucial component of the water cycle and plays an important role in regional weather and climate. However, owing to the lack of In Situ observations, an accurate understanding of the spatiotemporal variations of soil moisture (SM) on the Tibetan Plateau (TP) is still lacking. In this study, we used three gridded SM products to characterize the spatiotemporal features of SM on the TP during the warm season (May to August). We analyzed the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), Global Land Data Assimilation System (GLDAS), and Soil Moisture Active Passive (SMAP) datasets and used station observation data and triple collocation to quantify product accuracy and consistency. Results of the evaluation based on observation data show that both ERA5 and GLDAS overestimate SM, while the accuracy of SMAP is high. In terms of capturing the temporal variations of SM measured at stations, the performance of ERA5 and that of SMAP are superior to that of GLDAS. According to the evaluation based on triple collocation, SMAP exhibits the smallest random error over the TP and the highest temporal correlation with the unknown true SM in eastern TP. For SMAP, SM variability is the largest in the southern TP. For ERA5 and GLDAS, variability in the western TP is substantially larger than that for SMAP. Low-frequency (30–90 days) variations are the largest contributor to TP SM intraseasonal variability. Relative to SMAP, the contribution of high-frequency variations is low in ERA5 and GLDAS. Land-atmosphere coupling is stronger (weaker) in the western (southeastern) TP, which is relatively dry (wet). Our evaluation of SM product performance over the TP may facilitate the use of these products for disaster monitoring and climate and hydrological studies.
Temporal Subset SBAS InSAR Approach for Tropical Peatland Surface Deformation Monitoring Using Sentinel-1 Data
Authors: Izumi Yuta , Takeuchi Wataru , Widodo Joko , Sulaiman Albertus , Awaluddin Awaluddin , Aditiya Arif , Razi Pakhrur , Anggono Titi , Sumantyo Josaphat Tetuko Sri
Published: November 2022

Tropical peatland in Southeast Asia has undergone rapid degradation and shows large subsidence due to oxidation and peat shrinkage. The measurement of those deformations is thus valuable for evaluating the peat condition and assessing peat restoration. The time series interferometric synthetic aperture radar (TInSAR), especially with the small baseline subsets (SBAS) method, is capable of measuring long-term deformation. However, the dynamic surface scatterers often change in tropical peatland, which degrades the coherent scatterer (CS) distribution density. This article presents a simple and efficient TInSAR approach that enhances the CS density under such dynamic surface scatter variation based on the SBAS method. In the presented approach, a long-time series of single-look complex images is separated into subsets, and deformation estimation is performed for each subset. The effectiveness of this simple solution was investigated by InSAR simulation and validated using SAR observation data. We applied the subset SBAS approach to the three-year Sentinel-1A C-band SAR dataset acquired over tropical peatland in Indonesia. The analyses showed an improved number of CSs for the introduced subset approach. We further introduce the color representation of CS temporal behavior per subset for visual interpretation of scatterer change.
Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images
Authors: Du Xinran , He Shumeng , Yang Houqun , Wang Chunxiao
Published: November 2022

High spatial resolution (HSR) remote sensing images have a wide range of application prospects in the fields of urban planning, agricultural planning and military training. Therefore, the research on the semantic segmentation of remote sensing images becomes extremely important. However, large data volume and the complex background of HSR remote sensing images put great pressure on the algorithm efficiency. Although the pressure on the GPU can be relieved by down-sampling the image or cropping it into small patches for separate processing, the loss of local details or global contextual information can lead to limited segmentation accuracy. In this study, we propose a multi-field context fusion network (MCFNet), which can preserve both global and local information efficiently. The method consists of three modules: a backbone network, a patch selection module (PSM), and a multi-field context fusion module (FM). Specifically, we propose a confidence-based local selection criterion in the PSM, which adaptively selects local locations in the image that are poorly segmented. Subsequently, the FM dynamically aggregates the semantic information of multiple visual fields centered on that local location to enhance the segmentation of these local locations. Since MCFNet only performs segmentation enhancement on local locations in an image, it can improve segmentation accuracy without consuming excessive GPU memory. We implement our method on two high spatial resolution remote sensing image datasets, DeepGlobe and Potsdam, and compare the proposed method with state-of-the-art methods. The results show that the MCFNet method achieves the best balance in terms of segmentation accuracy, memory efficiency, and inference speed.
Mainlobe Deceptive Jammer Suppression Based on Quadratic Phase Coding in FDA-MIMO Radar
Authors: Zhang Yiqun , Liao Guisheng , Xu Jingwei , Lan Lan
Published: November 2022

In this paper, the problem of mainlobe deceptive jammer suppression is solved with the frequency diversity array-multiple-input multiple-output (FDA-MIMO) radar system. At the modeling stage, based on the FDA-MIMO radar, a quadratic phase code (QPC) is applied along the slow time dimension in the transmit array. In the receiver, after decoding and principal range compensation, the true and false targets that are generated in an identical angle, can be discriminated in the joint transmit-receive-Doppler frequency domain. Particularly, the false targets are equivalently moved from the mainlobe to the sidelobes in the transmit spatial frequency domain. Then, by performing the data-dependent transmit-receive-Doppler three-dimensional beamforming, the false targets are suppressed owing to Doppler and range mismatches. Moreover, by moving the jammers to nulls in the Doppler frequency domain, the capability in terms of the maximum number of suppressible jammers can be strengthened with an appropriate coding coefficient and frequency increment. Numerical results can certify the suppression capability of the QPC-FDA-MIMO radar.
Evaluation of BDS-3 B1C/B2b Single/Dual-Frequency PPP Using PPP-B2b and RTS SSR Products in Both Static and Dynamic Applications
Authors: Lan Ruohua , Yang Cheng , Zheng Yanli , Xu Qiaozhuang , Lv Jie , Gao Zhouzheng
Published: November 2022

The BeiDou Global Navigation Satellite System (BDS-3) can provide PPP-B2b satellite-based Precise Point Positioning (PPP) real-time service to the Asia–Pacific region via PPP-B2b signal transmitted from the three Geostationary Earth Orbit (GEO) satellites. This paper provides a comprehensive evaluation of the accuracies of the satellite’s precise real-time orbit and clock products, including BDS-3 PPP-B2b precise products and the precise real-time products provided by four IGS centers (CAS, DLR, GFZ, and WHU). In addition, the influences of these real-time precise satellite products on the PPP positioning accuracy with single-frequency and dual-frequencies are also studied. Furthermore, the accuracies of broadcast ephemeris and IGS ultra-rapid products are studied, as well as their impact on PPP accuracies. Results illustrate that the orbits accuracies of PPP-B2b orbits are 9.42 cm, 21.26 cm, and 28.65 cm in the radial, along-track, and cross-track components, which are slightly lower than those of real-time orbits provided by the four IGS centers. However, the accuracy of PPP-B2b clock biases is 0.18 ns, which is higher than those provided by IGS Real-Time Service (RTS). In the static positioning test, the 3D positioning accuracy of B1I+B3I dual-frequencies PPP and B1C single-frequency PPP are centimeter-level while using PPP-B2b service, which is slightly lower in horizontal components compared to those obtained based on IGS RTS products. The results of the dynamic vehicle test indicate that the positioning accuracies of B1I+B2b dual-frequency PPP are about 50 cm and 120 cm in horizontal and vertical components, which are close to those of B2b single-frequency PPP using PPP-B2b service. Generally, the PPP-B2b orbit and clock accuracies on real-time PPP present similar performance to that based on IGS RTS.
Investigation of Forest Fire Characteristics in North Korea Using Remote Sensing Data and GIS
Authors: Jin Ri , Lee Kyoo-Seock
Published: November 2022

Forest fires cause damage to property and the environment around the world every year. North Korea has suffered from fires every year. Fires may lead to temporary or permanent damage to forest ecosystems, long-term site degradation, and alteration of hydrological regimes, producing detrimental impacts on economies, human health, and safety. In North Korea, fires cause serious damage to the affected mountainous environment. However, it is very difficult to obtain ground information or perform field checks because of the political isolation of North Korea. Thus, there are few studies that have investigated North Korean fires. In this situation, remote sensing techniques and digital topographic data can be used to investigate fire characteristics in North Korea. In this study, fire trends were analyzed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Land Processes Distributed Active Archive Center (LPDAAC) from 2004 to 2015, and Landsat data were processed to estimate burned areas in South Hamgyong Province (SHP) and Gangwon Province (GWP) in North Korea. The burn severity of large fires in elevation, slope, and landform features was also analyzed to investigate large fire-burned areas using 30-m-resolution Global Digital Elevation Model (DEM) data from the United States National Aeronautics and Space Administration (NASA). After the results were compared and discussed, the following conclusions were derived. (1) In terms of location, fires in SHP were relatively concentrated along BaekDu-DaeGan (BDDG), while fires in GWP were scattered throughout the province. (2) In terms of size, the large fire-burned areas with an area greater than 1000 ha are significantly more frequent in SHP than in GWP. In brief, large fires occurred more frequently and were more serious in SHP than in GWP. (3) In terms of forest type, coniferous areas were more susceptible to damage from fires and large fires than deciduous areas in both GWP and SHP. This is attributed to the combustible resin within the coniferous trees. Particularly, when a crown fire occurs, it tends to spread rapidly throughout the coniferous forest. (4) Regarding landforms, most large fires occurred along windward-side open slopes, while there were very few fires in shallow valleys, high ridges, or U-shaped valleys. It is believed that cultivation in high-elevation terrain and a lack of fire-extinguishing equipment and systems allow large fires to spread quickly. North Korea is very susceptible to large fire damage and must develop preparation measures against such situations.
Fast Line Segment Detection and Large Scene Airport Detection for PolSAR
Authors: Wang Daochang , Liu Qi , Yin Qiang , Ma Fei
Published: November 2022

In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distribution. A new filter configuration is applied here to save time. Then, the Statistical Region Merging (SRM) framework is utilized for the generation of line-support regions. As one of our main contributions, we propose a new Statistical Region Merging algorithm based on gradient Strength and Direction (SRMSD). It determines the merging predicate with consideration of both gradient strength and gradient direction. For the merging order, we set it by bucket sort based on the gradient strength. Furthermore, the pixels are restricted to belong to a unique region, making the algorithm linear in time cost. Finally, based on Markov chains and a contrario approach, the false alarm control of line segments is implemented. Moreover, a large scene airport detection method is designed based on the proposed line segment detection algorithm and scattering characteristics. The effectiveness and applicability of the two methods are demonstrated with PolSAR data provided by UAVSAR.
Fast Shape Recognition Method Using Feature Richness Based on the Walking Minimum Bounding Rectangle over an Occluded Remote Sensing Target
Authors: Li Zekun , Guo Baolong , Meng Fanjie
Published: November 2022

Remote sensing target recognition has always been an important topic of image analysis, which has significant practical value in computer vision. However, remote sensing targets may be largely occluded by obstacles due to the long acquisition distance, which greatly increases the difficulty of recognition. Shape, as an important feature of a remote sensing target, plays an important role in remote sensing target recognition. In this paper, an occluded shape recognition method based on the local contour strong feature richness (contour pixel richness, contour orientation richness, and contour distance richness) to the walking minimum bounding rectangle (MBR) is proposed for the occluded remote sensing target (FEW). The method first obtains the local contour feature richness by using the walking MBR; it is a simple constant vector, which greatly reduces the cost of feature matching and increases the speed of recognition. In addition, this paper introduces the new concept of strong feature richness and uses the new strategy of constraint reduction to reduce the complex structure of shape features, which also speeds up the recognition speed. Validation on a self-built remote sensing target shape dataset and three general shape datasets demonstrate the sophisticated performance of the proposed method. FEW in this paper has both higher recognition accuracy and extremely fast recognition speed (less than 1 ms), which lays a more powerful theoretical support for the recognition of occluded remote sensing targets.
Efficient Object Detection Based on Masking Semantic Segmentation Region for Lightweight Embedded Processors
Authors: Yun Heuijee , Park Daejin
Published: November 2022

Because of the development of image processing using cameras and the subsequent development of artificial intelligence technology, various fields have begun to develop. However, it is difficult to implement an image processing algorithm that requires a lot of calculations on a light board. This paper proposes a method using real-time deep learning object recognition algorithms in lightweight embedded boards. We have developed an algorithm suitable for lightweight embedded boards by appropriately using two deep neural network architectures. The first architecture requires small computational volumes, although it provides low accuracy. The second architecture uses large computational volumes and provides high accuracy. The area is determined using the first architecture, which processes semantic segmentation with relatively little computation. After masking the area using the more accurate deep learning architecture, object detection is implemented with improved accuracy, as the image is filtered by segmentation and the cases that have not been recognized by various variables, such as differentiation from the background, are excluded. OpenCV (Open source Computer Vision) is used to process input images in Python, and images are processed using an efficient neural network (ENet) and You Only Look Once (YOLO). By running this algorithm, the average error can be reduced by approximately 2.4 times, allowing for more accurate object detection. In addition, object recognition can be performed in real time for lightweight embedded boards, as a rate of about 4 FPS (frames per second) is achieved.
Robotic Platform for Horticulture: Assessment Methodology and Increasing the Level of Autonomy
Authors: Kutyrev Alexey , Kiktev Nikolay , Jewiarz Marcin , Khort Dmitriy , Smirnov Igor , Zubina Valeria , Hutsol Taras , Tomasik Marcin , Biliuk Mykola
Published: November 2022

The relevance of the study is confirmed by the rapid development of automation in agriculture, in particular, horticulture; the lack of methodological developments to assess the effectiveness of the introduction of robotic technologies; and the need to expand the functionality of mobile robots. The purpose of the study was to increase the level of autonomy of a robotic platform for picking apple fruits based on a new method, develop a system of factors to determine the effectiveness of the introduction of robots in horticulture, and develop a control system using integrated processing of onboard data. The article discussed the efficiency factors for the introduction of robotic systems and technologies in agricultural enterprises specializing in horticulture within the framework of projects with different budgets. The study sample consisted of 30 experts—enterprises that have implemented robotic platforms and scientists specializing in this field. Based on an expert survey of enterprise specialists, a ranked list of 18 efficiency factors was obtained. To select an evaluation factor that determines the effectiveness of robotization and the developed control system, a method for calculating the concordance coefficient (method of expert analysis) was applied as a measure of the consistency of a group of experts for each group of factors. An analysis of the results of the expert evaluation showed that three factors are the most significant: the degree of autonomy of work; positioning accuracy; and recognition accuracy. The generalized indicator of local autonomy of task performance was estimated based on the analysis of a set of single indicators. A system for controlling the movement of an autonomous robotic wheeled platform based on inertial and satellite navigation and calculation of the path to be overcome was developed. The developed software allows for the design of a route for the robotic platform in apple horticulture to automatically perform various technological operations, such as fertilization, growth and disease control, and fruit harvesting. With the help of the software module, the X, Y coordinates, speed and azimuth of movement were given, and the movement of the platform along the given typical turn trajectories in an intensive horticulture environment was visualized.
Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation
Authors: Kour Kanwalpreet , Gupta Deepali , Gupta Kamali , Anand Divya , Elkamchouchi Dalia H. , Pérez-Oleaga Cristina Mazas , Ibrahim Muhammad , Goyal Nitin
Published: November 2022

The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation.
Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis
Authors: You Keshun , Qiu Guangqi , Gu Yingkui
Published: November 2022

With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of the models cannot be truly verified under complex extreme variable loading conditions. In this study, an end-to-end rolling bearing fault diagnosis model of a hybrid deep neural network with principal component analysis is proposed. Firstly, in order to reduce the complexity of deep learning computation, data pre-processing is performed by principal component analysis (PCA) with feature dimensionality reduction. The preprocessed data is imported into the hybrid deep learning model. The first layer of the model uses a CNN algorithm for denoising and simple feature extraction, the second layer makes use of bi-directional long and short memory (BiLSTM) for greater in-depth extraction of the data with time series features, and the last layer uses an attention mechanism for optimal weight assignment, which can further improve the diagnostic precision. The test accuracy of this model is fully comparable to existing deep learning fault diagnosis models, especially under low load; the test accuracy is 100% at constant load and nearly 90% for variable load, and the test accuracy is 72.8% at extreme variable load (2.205 N·m/s–0.735 N·m/s and 0.735 N·m/s–2.205 N·m/s), which are the worst possible load conditions. The experimental results fully prove that the model has reliable robustness and generality.
Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning
Authors: Lee Saebom , Choi Gyuho , Park Hyun-Cheol , Choi Chang
Published: November 2022

Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are easily affected by various factors, such as climate change, pests, and diseases, resulting in reduced yield and quality. Advances in computer vision in recent years have been widely used for plant disease detection and classification, providing opportunities for early disease detection, and resulting in improvements in agriculture. Particularly, the early and accurate detection of citrus diseases, which are vulnerable to pests, is very important to prevent the spread of pests and reduce crop damage. Research on citrus pest disease is ongoing, but it is difficult to apply research results to cultivation owing to a lack of datasets for research and limited types of pests. In this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites. The constructed dataset was trained, verified, and tested using a model that had undergone five transfer learning steps. All models used in the experiment had an average accuracy of 97% or more and an average f1 score of 96% or more. We built a web application server using the EfficientNet-b0 model, which exhibited the best performance among the five learning models. The built web application tested citrus pest disease using image samples collected from websites other than the self-collected image samples and prepared data, and both samples correctly classified the disease. The citrus pest automatic diagnosis web system using the model proposed in this study plays a useful auxiliary role in recognizing and classifying citrus diseases. This can, in turn, help improve the overall quality of citrus fruits.
Low-Cost UVBot Using SLAM to Mitigate the Spread of Noroviruses in Occupational Spaces
Authors: Wang Fanxin , Nisar Harris Junaid , Li Yao , Araud Elbashir , Nguyen Thanh H. , Kesavadas Thenkurussi
Published: November 2022

Noroviruses (NoVs) cause over 90% of non-bacterial gastroenteritis outbreaks in adults and children in developed countries. Therefore, there is a need for approaches to mitigate the transmission of noroviruses in workplaces to reduce their substantial health burden. We developed and validated a low-cost, autonomous robot called the UVBot to disinfect occupational spaces using ultraviolet (UV) lamps. The total cost of the UVBOT is less than USD 1000, which is much lower than existing commercial robots that cost as much as USD 35,000. The user-friendly desktop application allows users to control the robot remotely, check the disinfection map, and add virtual walls to the map. A 2D LiDAR and a simultaneous localization and mapping (SLAM) algorithm was used to generate a map of the space being disinfected. Tulane virus (TV), a human norovirus surrogate, was used to validate the UVBot’s effectiveness. TV was deposited on a painted drywall and exposed to UV radiation at different doses. A 3-log (99.9%) reduction of TV infectivity was achieved at a UV dose of 45 mJ/cm2. We further calculated the sanitizing speed as 3.5 cm/s and the efficient sanitizing distance reached up to 40 cm from the UV bulb. The design, software, and environment test data are available to the public so that any organization with minimal engineering capabilities can reproduce the UVBot system.
mm-DSF: A Method for Identifying Dangerous Driving Behaviors Based on the Lateral Fusion of Micro-Doppler Features Combined
Authors: Hao Zhanjun , Li Zepei , Dang Xiaochao , Ma Zhongyu , Wang Yue
Published: November 2022

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver’s driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method.
Correction: Zhang et al. A 7.4-Bit ENOB 600 MS/s FPGA-Based Online Calibrated Slope ADC without External Components. Sensors 2022, 22, 5852
Authors: Zhang Mengdi , Zhao Ye , Chen Yong , Crovetti Paolo , Wang Yanji , Ning Xinshun , Qiao Shushan
Published: November 2022

The authors wish to correct the following errors in the original paper [...]
Developing Smartness in Emerging Environments and Applications with a Focus on the Internet of Things
Authors: Mehmood Rashid , Corchado Juan M. , Yigitcanlar Tan
Published: November 2022

The smartness that underpins smart cities and societies is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner [...]
Promotion of Soil Microbial Community Restoration in the Mu Us Desert (China) by Aerial Seeding
Authors: Ma Yina , Zu Lei , Long Fayu , Yang Xiaofan , Wang Shixiong , Zhang Qing , He Yuejun , Chen Danmei , Sui Mingzhen , Zhang Guangqi , Zang Lipeng , Liu Qingfu
Published: November 2022

Soil microbial communities link soil and plants and play a key role in connecting above-ground and below-ground communities in terrestrial ecosystems. Currently, how artificial revegetation promotes the restoration of soil microbial community diversity in degraded ecosystems attracts extensive attention. In this study, soil samples were collected from long-term artificially restored mobile sandy lands (aerial seeding sample plots) from 1983 to 2015 in the Mu Us Desert. The second-generation high-throughput sequencing technology was adopted to identify soil microorganisms and analyze the changes in their community structure and diversity. The relationships between changes in microbial diversity and soil nutrients were explored by Pearson correlation analysis and canonical correspondence analysis. In addition, the restoration of subsurface soil microbial communities in this area was evaluated. The results are as follows: (1) The alpha diversity of the soil microorganisms increased significantly with the restoration period, and the composition and diversity of the soil microbial communities in the sample plots in different restoration years varied significantly. (2) Soil nutrient indexes, such as total carbon, total nitrogen and nitrate nitrogen, significantly increased with the restoration period and were significantly positively correlated with soil fungal and bacterial diversity. (3) Key soil fungal and bacterial phyla contributed to nutrient cycling in degraded ecosystems. It can be concluded that afforestation by aerial seeding facilitates the change in community structure and increases the diversity of soil microorganisms in the Mu Us Desert. This paper provides a basis for future measures and policies for restoring degraded lands and ecosystems.
Vortex-Pressure Fluctuation Interaction in the Outlet Duct of Centrifugal Pump as Turbines (PATs)
Authors: Lin Tong , Li Jian , Xie Baofei , Zhang Jianrong , Zhu Zuchao , Yang Hui , Wen Xiaoming
Published: November 2022

The outlet duct is the only outlet flow component of the pump as turbines (PATs). The flow state within it not only affects its operation stability but also influences the safe operation of downstream process equipment. The undesirable flow phenomenon of the vortex is the main reason for pressure pulsations and vibrations; therefore, it is important to adopt simulation and experimental methods to reveal the vortex-pressure fluctuation interaction characteristics in the outlet duct of PATs. Firstly, the spatial and temporal evolution of the vortex in the outlet duct under different operation conditions were compared based on the Q-vortex identification criterion. Subsequently, the frequency components corresponding to local high amplitude vorticity and pressure pulsations were clarified. Finally, the relationship between vortex evolution and the pressure pulsations in the outlet duct was established. The results showed that the flow rates of the turbine significantly affected the spatial and temporal evolution of the vortex rope in the outlet duct. The front chamber leakage flow and vortex shedding from the blade trailing edge also influenced the vortex distribution characteristics in the outlet duct. The dominant frequency of the pressure pulsation in the outlet duct was 6 fn under different operating conditions, and the amplitude of the pressure pulsation increased with the flow rates. The effect of vortex evolution on the local pressure pulsation characteristics decreased with increasing flow rates. The results can be used to improve and stabilize the operation and further optimization of PATs.
Does Central Environmental Protection Inspector Improve Corporate Social Responsibility? Evidence from Chinese Listed Companies
Authors: Feng Mei , Chen Chu , Liu Jia , Jia Wei
Published: November 2022

As an essential innovation in China’s environmental governance, the central environmental protection inspector policy (CEPI) plays a crucial role in driving the development of the corporate responsibility system. Using the difference-in-differences model with multiple time periods, samples of A-share listed companies in Shanghai and Shenzhen from 2013 to 2020 are chosen to examine the impact of the CEPI on corporate social responsibility within the context of China’s modern governance system. The results indicate the following: first, the CEPI can significantly promote enterprises to fulfil social responsibility; second, the local government environmental regulation and public participation are the dual guarantees for the effect of the CEPI’s policy, and market-based environmental regulation of the local government is more effective than command-and-control environmental regulation; and third, a high propensity for technological innovation is a critical incentive for businesses to fulfil their social responsibilities under the CEPI. For enterprises to actively respond to society, the compensation effect of innovation must be greater than its cost effect. By examining the interaction between the Central Government, the local government, companies and the public, this research aims to provide theoretical support for accelerating the creation of China’s contemporary environmental governance system.
Risk Management of Fuel Hedging Strategy Based on CVaR and Markov Switching GARCH in Airline Company
Authors: Lin Shuang , Wang Minke , Cheng Zhihong , He Fan , Chen Jiuhao , Liao Chuanhui , Zhang Shengda
Published: November 2022

Using a hedging strategy to stabilize fuel price is very important for airline companies in order to reduce the cost of their main business. In this paper, we construct models for managing the risk of the hedging strategy. First, we use conditional value at risk (CVaR) to measure the risk of an airline company’s hedging strategy. Compared with the value at risk (VaR), CVaR satisfies subadditivity, positive homogeneity, monotonicity, and transfer invariance. Therefore, CVaR is a consistent method of risk measurement. Second, time-varying state transition probability is introduced into our model in order to build a Markov Switching-GARCH (MS-GARCH). MS-GARCH takes dynamic changes of market state into account, a feature which has obvious advantages over the traditional constant state model. Additionally, we use a Markov chain Monte Carlo (MCMC) algorithm to estimate the parameters of MS-GARCH based on Gibbs sampling. We use fuel oil futures data from the Shanghai Futures Stock Exchange to implement and evaluate our model. In this paper, we empirically estimate the risk of airlines’ hedging strategy and draw the conclusion that our model is obviously effective in terms of the risk management of hedging, a use which has a certain guiding significance for reality.
The Impact of Digital Technologies on Company Restoration Time Following the COVID-19 Pandemic
Authors: Sammarco Giorgia , Ruzza Daniel , Maleki Vishkaei Behzad , De Giovanni Pietro
Published: November 2022

The global spread of COVID-19 affected societies and economies at large with simultaneous disruptions to both supply and demand. To investigate the implications of COVID-19, this study seeks to inquire into how companies can achieve resilience through performance robustness and how this affects the restoration time (i.e., the time period from the problem occurrence to the time when the company performance returns to the previous operational level). Moreover, it studies how digital technology adoption allowed firms to become more resilient during the COVID-19 outbreak by exploiting high robustness and improving restoration time. Our findings reveal that, among the several performance indicators investigated, only the robustness of Sales is of particular significance in contributing to restoration time. As regards the technologies deployed, Blockchain, 3D Printing, and Artificial Intelligence had a positive impact on firms’ resilience during the outbreak.
Assessing Rail Station Accessibility Based on Improved Two-Step Floating Catchment Area Method and Map Service API
Authors: Li Daoyong , Zang Hengyi , He Qilin
Published: November 2022

Accessibility is an important index for evaluating the efficiency of rail stations. In view of the imbalance between the supply and demand of rail station settings and population distribution, this paper takes the Shijingshan District in Beijing as an example. Based on the Gaussian two-step floating catchment area method and Gaode map’s service interface, the accessibility of rail stations is simulated and analyzed in terms of both walking and riding. Combined with the calculation results, supply and demand relationship and trip time, the current characteristics and causes are analyzed, and the corresponding optimization suggestions are put forward. The main conclusions are as follows: (1) The accessibility distribution of rail stations in the Shijingshan District is relatively coordinated with the population distribution. The effectiveness of the accessibility assessment of rail stations can be further improved by improving the causal evaluation model with traditional calculation data; (2) The change of trip mode has a small impact on the accessibility of large stations, while small stations and areas with uneven station distribution can be improved by riding; (3) According to the K-value clustering method, the results of the two calculation methods are divided into five categories, and each category of demand units has different accessibility characteristics and causes; (4) Comprehensive accessibility is positively correlated with road density and population density.
Developing a Framework for Fake News Diffusion Control (FNDC) on Digital Media (DM): A Systematic Review 2010–2022
Authors: Khan Shakeel Ahmad , Shahzad Khurram , Shabbir Omer , Iqbal Abid
Published: November 2022

This study sought to investigate factors causing the spread of fake news on digital media (DM) and to explore the sometimes disastrous consequences of fake news on social media. The study also aimed to construct a framework for fake news disaster management to control the dangers of false news on DM. The study applied PRISMA guidelines and techniques for exploring, devising, and inclusion and exclusion criteria. The search was carried out through 15 of the world’s leading digital databases. As a result, 31 peer-reviewed studies published in impact-factor journals of leading databases were included. Findings showed that several factors influenced the sharing of fake news on digital media (DM) platforms. Six major trending factors were the rise of technologies, social connections, political reasons, the absence of a controlling center, online business and marketing, and quick dissemination of information. The study identified the disadvantages of fake news (FN) on digital media (DM). A framework was constructed for managing fake news disasters to control the spread of fake news on digital media. This paper offers important theoretical contributions through the development of a framework for controlling fake news spread on digital media and by providing a valuable addition to the existing body of knowledge. The study offers practical assistance to top management, decision makers, and policymakers to devise policies to effectively manage problems caused by fake news dissemination. It provides practical strategies to address fake news disasters on digital media for redefining social values. This research also assists digital media managers in utilizing the proposed framework and controlling the harmful impact of fake news on social media.
Time Series Data Modeling Using Advanced Machine Learning and AutoML
Authors: Alsharef Ahmad , Sonia Sonia , Kumar Karan , Iwendi Celestine
Published: November 2022

A prominent area of data analytics is “timeseries modeling” where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods. The datasets this empirical research utilized were secondary quantitative data of the real prices of the currently most used cryptocurrencies. We found that AutoML for timeseries is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline for predicting timeseries data.