bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Aircraft Emissions Using Long-Path Differential Optical Absorption Spectroscopy at Hefei Xinqiao International Airport
Authors: Duan Jun , Qin Min , Fang Wu , Liao Zhitang , Gui Huaqiao , Shi Zheng , Yang Haining , Meng Fanhao , Shao Dou , Hu Jiaqi , Han Baobin , Xie Pinhua , Liu Wenqing
Published: August 2022

Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport.
Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer
Authors: Li Xiaohui , Yang Jingsong , Han Guoqi , Ren Lin , Zheng Gang , Chen Peng , Zhang Han
Published: August 2022

Accurate information on tropical cyclone position, intensity, and structure is critical for storm surge prediction. Atmospheric reanalysis datasets can provide gridded, full coverage, long-term and multi-parameter atmospheric fields for the research on the impact of tropical cyclones on the upper ocean, which effectively makes up for the uneven temporal and spatial distribution of satellite remote sensing and in situ data. However, the reanalysis data cannot accurately describe characteristic parameters of tropical cyclones, especially in high wind conditions. In this paper, the performance of the tropical cyclone representation in ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation) is investigated and analyzed with respect to IBTrACS (International Best Track Archive for Climate Stewardship) during the period 2018–2020. Comparisons demonstrate that ERA5 winds significantly underestimate the maximum wind speed during the tropical cyclones (>30 m/s) compared to those provided by IBTrACS. An effective wind reconstruction method is examined to enhance tropical cyclone intensity representation in reanalysis data in 94 cases of 31 tropical cyclones 2018–2020. The reconstructed wind speeds are in good agreement with the SFMR (Stepped Frequency Microwave Radiometer) measured data and SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The proposed wind reconstruction method can effectively improve the accuracy of the tropical cyclone representation in ERA5, and will benefit from the establishment of remote sensing satellite retrieval model and the forcing fields of the ocean model.
Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets
Authors: Gonzalez-Perez Ali , Abd-Elrahman Amr , Wilkinson Benjamin , Johnson Daniel J. , Carthy Raymond R.
Published: August 2022

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.
Reliability of Gridded Precipitation Products for Water Management Studies: The Case of the Ankavia River Basin in Madagascar
Authors: Ramahaimandimby Zonirina , Randriamaherisoa Alain , Jonard François , Vanclooster Marnik , Bielders Charles L.
Published: August 2022

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, gridded precipitation products (GPPs) from merged satellite-/gauge-/model-based rainfall products constitute an attractive alternative. The quality of which must, nevertheless, be verified. The objective of this study was to evaluate, at different time scales, the reliability of 6 GPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The GPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), the European Centre Medium-Range Weather Forecasts ECMWF Reanalysis on global land surface (ERA5-Land), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 Final (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), and the African Rainfall Estimation (RFEv2) products. The results suggest that IMERG (R2 = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other GPPs at the daily scale, followed by RFEv2 (R2 = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R2 = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All GPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0–0.2 mm/day). ARC2, IMERG, PERSIANN, and RFEv2 all underestimate rainfall occurrence in the 0.2–150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (>150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range of 2–100 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R2 = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at an hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R2 = 0.20). Overall, the IMERG product can be regarded as the most reliable gridded precipitation source at monthly, daily, and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall >100 mm/day may, nevertheless, restrict its use.
Bidirectional Flow Decision Tree for Reliable Remote Sensing Image Scene Classification
Authors: Feng Jiangfan , Wang Dini , Gu Zhujun
Published: August 2022

Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however, they lack explainability and trustworthiness. Here, we propose a bidirectional flow decision tree (BFDT) module to create a reliable RS scene classification framework. Our algorithm combines BFDT and Convolutional Neural Networks (CNNs) to make the decision process easily interpretable. First, we extract multilevel feature information from the pretrained CNN model, which provides the basis for constructing the subsequent hierarchical structure. Then the model uses the discriminative nature of scene features at different levels to gradually refine similar subsets and learn the interclass hierarchy. Meanwhile, the last fully connected layer embeds decision rules for the decision tree from the bottom up. Finally, the cascading softmax loss is used to train and learn the depth features based on the hierarchical structure formed by the tree structure that contains rich remote sensing information. We also discovered that superclass results can be obtained well for unseen classes due to its unique tree structure hierarchical property, which results in our model having a good generalization effect. The experimental results align with theoretical predictions using three popular datasets. Our proposed framework provides explainable results, leading to correctable and trustworthy approaches.
Deep Reinforcement Learning-Based Adaptive Modulation for Underwater Acoustic Communication with Outdated Channel State Information
Authors: Zhang Yuzhi , Zhu Jingru , Wang Haiyan , Shen Xiaohong , Wang Bin , Dong Yuan
Published: August 2022

Underwater acoustic (UWA) adaptive modulation (AM) requires feedback about channel state information (CSI) but the long propagation delays and time-varying features of UWA channels can cause the CSI feedback to be outdated. When the AM mode is selected by outdated CSI, the mismatch between the outdated CSI and the actual CSI during transmission degrades the performance and can even lead to communication failure. Reinforcement learning has the ability to learn the relationships between adaptive systems and the environment. This paper proposes a deep Q-network (DQN)-based AM method for UWA communication that uses a series of outdated CSI as the system input. Our study showed that it could extract channel information and select appropriate modulation modes in the expected channels more effectively than single Q-learning (QL) without needing a deep neural network structure. Furthermore, to mitigate any decision bias that was caused by partial observations of UWA channels, we improved the DQN-based AM by integrating a long short-term memory (LSTM) neural network, named LSTM-DQN-AM. The proposed scheme could enhance the DQN’s ability to remember and process historical input channel information, thus strengthening its relationship mapping ability for state-action pairs and rewards. The pool and sea experimental results demonstrated that the proposed LSTM-DQN-AM outperformed DQN-, QL- and threshold-based AM methods.
Analysis of the Atmospheric Duct Existence Factors in Tropical Cyclones Based on the SHAP Interpretation of Extreme Gradient Boosting Predictions
Authors: Huang Lang , Zhao Xiaofeng , Liu Yudi , Yang Pinglv
Published: August 2022

The atmospheric duct (AD) is an anomalous structure in which electromagnetic waves can make transhorizon propagation. ADs often occur in the formation, development and disappearance of tropical cyclones (TCs). In this work, the eXtreme Gradient Boosting (XGBoost) model is used to predict TC ducts and a relatively high accuracy of 81.3% is obtained. Shapely additional explanations (SHAP) values of the features including TC parameters and local meteorological parameters are employed to interpret XGBoost model predictions of the TC ducts existence. Furthermore, the importance ranking of the features is revealed, among which the distance between dropsondes and TC eyes is the most important. In addition, the detailed relationships between the AD existence and the features are presented. Hence, this work can not only improve the knowledge of the relationship between TC ducts and the features, but also be of great value to the ducts prediction.
Inferring Mass Loss by Measuring Contemporaneous Deformation around the Helheim Glacier, Southeastern Greenland, Using Sentinel-1 InSAR
Authors: Erfani Jazi Zohreh , Motagh Mahdi , Klemann Volker
Published: August 2022

The elastic response of solid earth to glacier and ice sheet melting, the most important consequences of climate change, is a contemporaneous uplift. Here, we use interferometric synthetic aperture radar (InSAR) measurements to detect crustal deformation and mass loss near the Helheim glacier, one of the largest glaciers in southeastern Greenland. The InSAR time series of Sentinel-1 data between April 2016 and July 2020 suggest that there is a maximum cumulative displacement of ~6 cm in the line of sight (LOS) direction from the satellite to the ground near Helheim. We use an exponentially decreasing model of the thinning rate, which assumes that the mass loss starts at the lower-elevation terminal region of the glacier and continues to the higher-elevation interior. A linear inversion of the derived crustal uplift in the vicinity of bedrock using this model for surface loading in an elastic half-space suggests a mass loss of 8.33 Gt/year, which agrees with the results from other studies.
A Factor Analysis Backpropagation Neural Network Model for Vegetation Net Primary Productivity Time Series Estimation in Western Sichuan
Authors: Li Song , Zhang Rui , Xie Lingxiao , Zhan Junyu , Song Yunfan , Zhan Runqing , Shama Age , Wang Ting
Published: August 2022

Vegetation net primary productivity (VNPP) is the main factor in ecosystem carbon sink function and regulation of environmental processes. However, NPP data products have data missing in some areas, which affects the availability and overall accuracy level of data. Therefore, we adopted the Factor Analysis Backpropagation neural network model (FA-BP model) to acquire a high-accuracy and high-reliability NPP result without missing or empty areas by using a series of easily accessible datasets, such as meteorological data and remote sensing data. We selected the western Sichuan region as the study area and carried out a VNPP time series estimation from 2000 to 2016. Comparative simulations also verify the accuracy of the time series estimation results: The Pearson correlation r of VNPP prediction results ranged from 0.807 to 0.917, the mean absolute error ranged from 29.1 to 38.9, the root mean square error was between 37.3 and 51.8, and the mean relative error varies from 0.10 to 0.14. Further analysis shows that the spatial pattern of estimated VNPP during the past 17 years in western Sichuan shows a decreasing trend from southeast to northwest. Besides, the VNPP time series is generally on an upward trend in this period. The increasing and decreasing areas of VNPP values in the study area accounted for 81.42% and 18.58%, respectively. Moreover, we find that temperature dominates the change of VNPP in the whole western Sichuan region. The data processing method and experimental results presented in this paper can provide a reference for accurately acquiring VNPP and related studies on natural resources and climate change.
A Novel Imaging Scheme of Squint Multichannel SAR: First Result of GF-3 Satellite
Authors: Lv Yini , Shang Mingyang , Zhong Lihua , Qiu Xiaolan , Ding Chibiao
Published: August 2022

The azimuth multichannel synthetic aperture radar (MC-SAR) is an effective means of earth observation due to its high-resolution and wide-swath (HRWS) imaging capability. Compared with the traditional MC-SAR, the two-dimensional (2D) spectrum of the squint multichannel synthetic aperture radar (SMC-SAR) is skewed, leading to the severe coupling between azimuth signals and range signals. Therefore, the traditional imaging algorithms are inapplicable to SMC-SAR. Currently, the existing imaging algorithms for SMC-SAR need interpolation or sub-aperture processing, which results in low efficiency. This paper proposes a novel imaging scheme for spaceborne SMC-SAR to handle this issue. With the squint angle known, the Doppler centroid can be compensated for by the spectrum shift function. Additionally, the frequency correlation method (FCM) is utilized to estimate the channel phase imbalance. After that, the azimuth multichannel data are reconstructed by the filter banks to eliminate the Doppler spectrum aliasing. Finally, the data are imaged based on the modified equivalent slant range model (MESRM). Both the simulation and Gaofen-3 (GF-3) data experiments demonstrate the effectiveness and efficiency of the proposed method.
Airborne Passive Bistatic Radar Clutter Suppression Algorithm Based on Root Off-Grid Sparse Bayesian Learning
Authors: Wang Jipeng , Wang Jun , Zuo Luo , Guo Shuai , Zhao Dawei
Published: August 2022

When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space–time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space–time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms.
HHI-AttentionNet: An Enhanced Human-Human Interaction Recognition Method Based on a Lightweight Deep Learning Model with Attention Network from CSI
Authors: Shafiqul Islam Md , Jannat Mir Kanon Ara , Kim Jin-Woo , Lee Soo-Wook , Yang Sung-Hyun
Published: August 2022

Nowadays WiFi based human activity recognition (WiFi-HAR) has gained much attraction in an indoor environment due to its various benefits, including privacy and security, device free sensing, and cost-effectiveness. Recognition of human-human interactions (HHIs) using channel state information (CSI) signals is still challenging. Although some deep learning (DL) based architectures have been proposed in this regard, most of them suffer from limited recognition accuracy and are unable to support low computation resource devices due to having a large number of model parameters. To address these issues, we propose a dynamic method using a lightweight DL model (HHI-AttentionNet) to automatically recognize HHIs, which significantly reduces the parameters with increased recognition accuracy. In addition, we present an Antenna-Frame-Subcarrier Attention Mechanism (AFSAM) in our model that enhances the representational capability to recognize HHIs correctly. As a result, the HHI-AttentionNet model focuses on the most significant features, ignoring the irrelevant features, and reduces the impact of the complexity on the CSI signal. We evaluated the performance of the proposed HHI-AttentionNet model on a publicly available CSI-based HHI dataset collected from 40 individual pairs of subjects who performed 13 different HHIs. Its performance is also compared with other existing methods. These proved that the HHI-AttentionNet is the best model providing an average accuracy, F1 score, Cohen’s Kappa, and Matthews correlation coefficient of 95.47%, 95.45%, 0.951%, and 0.950%, respectively, for recognition of 13 HHIs. It outperforms the best existing model’s accuracy by more than 4%.
A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
Authors: Chamran Mohammad Kazem , Yau Kok-Lim Alvin , Ling Mee Hong , Chong Yung-Wey
Published: August 2022

With the significant rise in demand for network utilization, such as data transmission and device-to-device (D2D) communication, fifth-generation (5G) networks have been proposed to fill the demand. Deploying 5G enhances the utilization of network channels and allows users to exploit licensed channels in the absence of primary users (PUs). In this paper, a hybrid route selection mechanism is proposed, and it allows the central controller (CC) to evaluate the route map proactively in a centralized manner for source nodes. In contrast, source nodes are enabled to make their own decisions reactively and select a route in a distributed manner. D2D communication is preferred, which helps networks to offload traffic from the control plane to the data plane. In addition to the theoretical analysis, a real testbed was set up for the proof of concept; it was composed of eleven nodes with independent processing units. Experiment results showed improvements in traffic offloading, higher utilization of network channels, and a lower interference level between primary and secondary users. Packet delivery ratio and end-to-end delay were affected due to a higher number of intermediate nodes and the dynamicity of PU activities.
Dielectric Permittivity Measurement Using Open-Ended Coaxial Probe—Modeling and Simulation Based on the Simple Capacitive-Load Model
Authors: Šarolić Antonio , Matković Anđela
Published: August 2022

The study aim was to validate that dielectric permittivity measurement using the open-ended coaxial probe can be reliably modeled using electromagnetic modeling and simulations, followed by the postprocessing calculations based on the simple capacitive-load model. Saline solutions with various NaCl concentrations were used as materials under test (MUTs) to investigate how ionic conductivity affects the model validity. Two different solvers and simulation methods were used: FEKO for the frequency domain and CST for the time domain. Furthermore, we performed physical experiments with the same probe and MUTs, again implementing the capacitive-load model on the measurement data to observe the model validity. Relative error of the capacitive-load model with respect to the reference permittivity values, both in measurements and simulations, was within 10% for all cases except for the measured εr′ of 1M solution at the lowest frequencies. The model yielded average relative errors well below 1% for the physiological saline, which is relevant for biological materials. The error increased for higher concentrations and for the lowest simulated frequencies but was within the declared measurement accuracy of the probe itself. This makes the simple capacitive-load model valid for all analyzed concentrations in the microwave frequency range from 0.5 to 18 GHz.
Automatic Ergonomic Risk Assessment Using a Variational Deep Network Architecture
Authors: Chatzis Theocharis , Konstantinidis Dimitrios , Dimitropoulos Kosmas
Published: August 2022

Ergonomic risk assessment is vital for identifying work-related human postures that can be detrimental to the health of a worker. Traditionally, ergonomic risks are reported by human experts through time-consuming and error-prone procedures; however, automatic algorithmic methods have recently started to emerge. To further facilitate the automatic ergonomic risk assessment, this paper proposes a novel variational deep learning architecture to estimate the ergonomic risk of any work-related task by utilizing the Rapid Entire Body Assessment (REBA) framework. The proposed method relies on the processing of RGB images and the extraction of 3D skeletal information that is then fed to a novel deep network for accurate and robust estimation of REBA scores for both individual body parts and the entire body. Through a variational approach, the proposed method processes the skeletal information to construct a descriptive skeletal latent space that can accurately model human postures. Moreover, the proposed method distills knowledge from ground truth ergonomic risk scores and leverages it to further enhance the discrimination ability of the skeletal latent space, leading to improved accuracy. Experiments on two well-known datasets (i.e., University of Washington Indoor Object Manipulation (UW-IOM) and Technische Universität München (TUM) Kitchen) validate the ability of the proposed method to achieve accurate results, overcoming current state-of-the-art methods.
Research on Safety Interlock System Design and Control Experiment of Combined Support and Anchor Equipment
Authors: Wang Pengyu , Su Guoyong , Yang Wenlong , Jing Peng
Published: August 2022

In view of the risk of collision with humans or equipment arising from a lack of protection in the operation process of combined support and anchor equipment on the heading face, this paper designs a safety interlock system for combined support and anchor equipment. Firstly, a mathematical model of hydraulic power system control and a valve control system based on feedforward–feedback optimization were established according to the power demand of the combined support and anchor equipment. Secondly, according to the reliability indexes of the safety interlock system, corresponding sensor, logic control and execution modules were designed. Ultrasonic sensor groups were arranged at the key positions of the combined support and anchor equipment to capture the position information in real time when the equipment was moving. Thus, the pump-valve hydraulic system was controlled through closed-loop feedback. The experimental results show that the safety interlock system of the combined support and anchor equipment can adjust the revolving speed of the permanent magnet synchronous motor (PMSM) in real time according to the distance from the obstacle, so as to control the pump outlet flow, and then perform interlocking safety control of the hydraulic cylinder’s movement speed. The system can effectively prevent damage to the surrounding equipment or personnel arising from equipment malfunction.
Research Progress in Distributed Acoustic Sensing Techniques
Authors: Shang Ying , Sun Maocheng , Wang Chen , Yang Jian , Du Yuankai , Yi Jichao , Zhao Wenan , Wang Yingying , Zhao Yanjie , Ni Jiasheng
Published: August 2022

Distributed acoustic sensing techniques based on Rayleigh scattering have been widely used in many applications due to their unique advantages, such as long-distance detection, high spatial resolution, and wide sensing bandwidth. In this paper, we provide a review of the recent advancements in distributed acoustic sensing techniques. The research progress and operation principles are systematically reviewed. The pivotal technologies and solutions applied to distributed acoustic sensing are introduced in terms of polarization fading, coherent fading, spatial resolution, frequency response, signal-to-noise ratio, and sensing distance. The applications of the distributed acoustic sensing are covered, including perimeter security, earthquake monitoring, energy exploration, underwater positioning, and railway monitoring. The potential developments of the distributed acoustic sensing techniques are also discussed.
Extended Reality in Neurosurgical Education: A Systematic Review
Authors: Iop Alessandro , El-Hajj Victor Gabriel , Gharios Maria , de Giorgio Andrea , Monetti Fabio Marco , Edström Erik , Elmi-Terander Adrian , Romero Mario
Published: August 2022

Surgical simulation practices have witnessed a rapid expansion as an invaluable approach to resident training in recent years. One emerging way of implementing simulation is the adoption of extended reality (XR) technologies, which enable trainees to hone their skills by allowing interaction with virtual 3D objects placed in either real-world imagery or virtual environments. The goal of the present systematic review is to survey and broach the topic of XR in neurosurgery, with a focus on education. Five databases were investigated, leading to the inclusion of 31 studies after a thorough reviewing process. Focusing on user performance (UP) and user experience (UX), the body of evidence provided by these 31 studies showed that this technology has, in fact, the potential of enhancing neurosurgical education through the use of a wide array of both objective and subjective metrics. Recent research on the topic has so far produced solid results, particularly showing improvements in young residents, compared to other groups and over time. In conclusion, this review not only aids to a better understanding of the use of XR in neurosurgical education, but also highlights the areas where further research is entailed while also providing valuable insight into future applications.
Optimization of a Simulated Annealing Algorithm for S-Boxes Generating
Authors: Kuznetsov Alexandr , Wieclaw Lukasz , Poluyanenko Nikolay , Hamera Lukasz , Kandiy Sergey , Lohachova Yelyzaveta
Published: August 2022

Cryptographic algorithms are used to ensure confidentiality, integrity and authenticity of data in information systems. One of the important areas of modern cryptography is that of symmetric key ciphers. They convert the input plaintext into ciphertext, representing it as a random sequence of characters. S-boxes are designed to complicate the input–output relationship of the cipher. In other words, S-boxes introduce nonlinearity into the encryption process, complicating the use of different methods of cryptanalysis (linear, differential, statistical, correlation, etc.). In addition, S-boxes must be random. This property means that nonlinear substitution cannot be represented as simple algebraic constructions. Random S-boxes are designed to protect against algebraic methods of cryptanalysis. Thus, generation of random S-boxes is an important area of research directly related to the design of modern cryptographically strong symmetric ciphers. This problem has been solved in many related works, including some using the simulated annealing (SA) algorithm. Some works managed to generate 8-bit bijective S-boxes with a nonlinearity index of 104. However, this required enormous computational resources. This paper presents the results of our optimization of SA via various parameters. We were able to significantly reduce the computational complexity of substitution generation with SA. In addition, we also significantly increased the probability of generating the target S-boxes with a nonlinearity score of 104.
Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks
Authors: Lomelin-Ibarra Vicente A. , Gutierrez-Rodriguez Andres E. , Cantoral-Ceballos Jose A.
Published: August 2022

Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. Since the mental task has similar behavior to that of the motor execution process, it can be used to create rehabilitation routines for patients with some motor skill impairment. However, due to the nature of this mental task, its execution is complicated. Hence, the classification of these signals in scenarios such as brain–computer interface systems tends to have a poor performance. In this work, we study in depth different forms of data representation of motor imagery EEG signals for distinct CNN-based models as well as novel EEG data representations including spectrograms and multidimensional raw data. With the aid of transfer learning, we achieve results up to 93% accuracy, exceeding the current state of the art. However, although these results are strong, they entail the use of high computational resources to generate the samples, since they are based on spectrograms. Thus, we searched further for alternative forms of EEG representations, based on 1D, 2D, and 3D variations of the raw data, leading to promising results for motor imagery classification that still exceed the state of the art. Hence, in this work, we focus on exploring alternative methods to process and improve the classification of motor imagery features with few preprocessing techniques.
What Can Machine Learning Teach Us about Australian Climate Risk Disclosures?
Authors: Harker Callan , Hassall Maureen , Lant Paul , Rybak Nikodem , Dargusch Paul
Published: August 2022

There seems to be no agreed taxonomy for climate-related risks. The information in firms’ climate risk disclosures represents a new resource for identifying the priorities and strategies of Australian companies’ management of climate risk. This research surveys 839 companies listed on the Australian Stock Exchange for the presence of climate risk disclosures, identifying 201 disclosures on climate risk. The types of climate risks and the risk management strategies were extracted and evaluated using machine learning. The analysis revealed that Australian firms are focused on acute physical climate risks, followed by market and regulatory risks. The predominant management strategy for these risks was to use a risk reduction approach, rather than avoiding or transferring risk. The analysis showed that key Australian industry sectors, such as materials, banking, insurance, and energy are focusing on different mixtures of risk types, but they are all primarily managing risks through risk-reduction strategies. An underlying driver of climate risk disclosure was composed of the financial implications of climate risk, particularly with respect to acute physical risks. The research showed that emission reductions represent a primary consideration for Australian firms in their disclosures identifying how they are responding to climate risk. Further research using machine learning to evaluate climate risk disclosure should focus on analysing entire climate risk reports for key topics and trends over time.
Two-Step Pricing Decision Models for Manufacturer-Led Dual-Channel Supply Chains
Authors: Zhang Xuelong , Wu Doudou , Zhao Xiangzhong
Published: August 2022

In this paper, we study the optimal profit change in a manufacturer-led dual-channel supply chain under centralized decision-making and decentralized decision-making scenarios. The supply chain is composed of only a single supplier, a manufacturer, and a retailer, and the manufacturer plays a leading role in the supply chain. Meanwhile, the following conditions exist simultaneously: the promotion levels of retailers in offline physical stores, the promotion compensation given by manufacturers to retailers, and channel competition. In order to coordinate the profits of channel members, a two-step pricing decision model is established. This research shows that using a two-step pricing decision model can make the retailer’s optimal promotion fees and the manufacturer’s optimal sales compensation fees the same as the centralized decision-making’s optimal values; however, the results are not good, because after the coordination, the retailer’s profits increase and the manufacturer’s profits decrease, and, as a consequence, the retailer needs to pay a fixed fee to the manufacturer within the validity of the contract, which is signed by both sides, to achieve a win–win situation for the channel members.
Estimation of Forest Ecosystem Climate Regulation Service Based on Actual Evapotranspiration of New Urban Areas in Guanshanhu District, Guiyang, Guizhou Province, China
Authors: Deng Ou , Li Yiqiu , Li Ruoshuang , Yang Guangbin
Published: August 2022

Suburban forest ecosystems have a great influence on local climate, especially for mitigating urban heat island effects and dry island effects. To quantify the climate regulation value of forest ecosystem, and provide a reference for regional ecosystem accounting and scientific land management, a new estimation method based on actual evapotranspiration (AET) is proposed and applied in this work. Based on remote sensing, meteorological, and soil data in the years 2000, 2010, and 2020, the annual AET of the forest ecosystem and its dynamic changes were calculated in the new urban area, Guanshanhu District, Guiyang, Guizhou Province, SW China. The climate regulation value is derived from differences in the annual AET of forest ecosystems relative to impervious surfaces. The results showed that: (1) the area of forest ecosystem in Guanshanhu District increased from 2000 to 2010 as a result of ecological engineering but decreased from 2010 to 2020 due to the establishment and expansion of the new urban area, while the area of the impervious surface increased rapidly; (2) the differences in annual AET of forest ecosystem relative to the impervious surface were calculated and subdivided according to different forest types. In 2000, 2010, and 2020, coniferous forests contributed the most to the annual AET difference, followed by coniferous and broad-leaved mixed forests, broad-leaved forests, shrubs, and other forests, respectively; (3) the total climate regulation value of forest ecosystem showed an increasing trend, on the whole, the estimation results were ¥8.78 × 108 in 2000, ¥12.62 × 108 in 2010, and ¥14.75 × 108 in 2020; (4) The average per unit area climate regulation value of all types of forests in the area, based on electricity price in the year 2000, was ¥8.06 × 104/ha in 2000, ¥8.11 × 104/ha in 2010, and ¥10.58 × 104/ha in 2020, the highest portion of per unit area climate regulation value was of coniferous forest, as ¥8.59 × 104/ha in 2000, ¥9.28 × 104/ha in 2010, and ¥11.05 × 104/ha in 2020. This study is a beneficial exploration of forest ecosystem climate regulation value estimation in Guanshanhu District, and the results can provide references for ecological construction in new urban areas.
Avoidance of Supermarket Food Waste—Employees’ Perspective on Causes and Measures to Reduce Fruit and Vegetables Waste
Authors: Mattsson Lisa , Williams Helén
Published: August 2022

Identifying causes of food waste at grocery retail level is crucial for the development of effective measures to reduce waste. Frontline employees manage food waste in their day-to-day operations; however, there is a paucity of research that draws attention to their knowledge of and approach to causes and measures to reduce food waste. In this empirical study, a mixed methods approach is adopted, using multiple interviews and participatory observations with employees, and primary quantitative data on fruit and vegetable waste for one year from the supermarkets. The results illuminate the fact that the role of employees is central for reducing food waste, and from their perspective, the causes and measures can be divided into four different main themes covering policy, practice, people and product. The analysis involves 73 different fruit and vegetables categories, and the fruit and vegetables waste at the three supermarkets is 60 tonnes. The results also reveal different causes for different fruit and vegetables categories, implying that generic descriptions of causes are not enough to use as bases for planning reduction measures. The paper provides a base for planning and implementing reduction measures for the grocery retail sector, which contribute to a sustainable food supply chain.
An Advanced Multi-Agent Reinforcement Learning Framework of Bridge Maintenance Policy Formulation
Authors: Zhou Qi-Neng , Yuan Ye , Yang Dong , Zhang Jing
Published: August 2022

In its long service life, bridge structure will inevitably deteriorate due to coupling effects; thus, bridge maintenance has become a research hotspot. The existing algorithms are mostly based on linear programming and dynamic programming, which have low efficiency and high economic cost and cannot meet the actual needs of maintenance. In this paper, a multi-agent reinforcement learning framework was proposed to predict the deterioration process reasonably and achieve the optimal maintenance policy. Using the regression-based optimization method, the Markov transition matrix can better describe the uncertain transition process of bridge components in the maintenance year and the real-time updating of the matrix can be realized by monitoring and evaluating the performance deterioration of components. Aiming at bridges with a large number of components, the maintenance decision-making framework of multi-agent reinforcement learning can adjust the maintenance policy according to the updated Markov matrix in time, which can better adapt to the dynamic change of bridge performance in service life. Finally, the effectiveness of the framework was verified by taking the simulation data of a simply supported beam bridge and a cable-stayed bridge as examples.
Mitigating Risks for Effective Personnel Management in the Organization of the Energy Sector due to the COVID-19 Pandemic
Authors: Podolchak Nazar , Martyniuk Volodymyr , Tsygylyk Natalia , Skowron Stanislaw , Wołowiec Tomasz
Published: August 2022

One of the three most important components of the effective work of an organization or enterprise in the energy sector is properly organized staff work. Thus, all the risks arising in this process should be given proper consideration. Conventional methods can hardly be of any assistance, which is why this paper proposes the innovative rapidly adaptive model for the evaluation of existing risks and the forecasting of emerging ones due to the COVID-19 pandemic in modern personnel management. The model employs a probability theory and applies it to four major risk groups, which are related to many factors. It also describes the methods of mitigating these risks. This made it possible to improve personnel management efficiency, thus offsetting the adverse consequences of the COVID-19 pandemic.
Study on the Spatial-Temporal Evolution of Land Use Ecosystem Service Value and Its Zoning Management and Control in the Typical Alpine Valley Area of Southeast Tibet—Empirical Analysis Based on Panel Data of 97 Villages in Chayu County
Authors: Fan Shuping , Li Peng , He Qi , Cheng Jiaru , Zhang Mingfeng , Wu Nan , Yang Song , Pan Shidong
Published: August 2022

Under the background of ecological civilization construction and the overall planning of land and space, it is particularly important to explore the land use ecosystem service value and its zoning control. This paper, taking Chayu County, a typical alpine valley area of southeast Tibet as an example and based on the remote sensing interpretation data of three periods in 2000, 2010 and 2020, employs the three-level spatial scale from the village level, the township level to the county level to converge step by step, and uses a series of model algorithms to analyze and calculate the regional ecosystem service value and their dynamic changes, as well as spatial agglomeration and regional type division. The research shows that the land use types mainly consist of forest land, grassland and unused land, whose overall change range is small during the study period. The conversion of land use types is mainly between forest land, grassland and unused land and the land use index generally presents a spatial pattern of “high in the southwest and low in the northeast”, showing a decreasing trend to some degree. ESVI generally presents a differentiation pattern of “high in the west and low in the east”, with obvious spatial differentiation characteristics of kernel density, significant clustering and distribution characteristics and stable variation range, displaying an overall spatial pattern with characteristics of “dense in the west and sparse in the east, high in the north and low in the south”. Based on the administrative village scale, the study area is divided into three different types of land use ecological function areas: habitat maintenance function area, biological protection function area and production support function area. Differentiated approaches to appropriate development and construction and the corresponding optimization paths of ecological protection will be put forward.
The Role of Digital-Media-Based Pedagogical Aids in Elementary Entomology: An Innovative and Sustainable Approach
Authors: Lu Su-Ju , Chen Ya-Hui , Huang Hazel , Liu Ying-Chieh
Published: August 2022

It has been challenging for city school pupils to learn about insects, given the lack of live insects. To overcome this challenge, the objective of this study is to propose the use of emerging digital media, namely three-dimensional printing (3DP) technologies, to create reusable, fake, “live” insects. In this study, we designed two 3D-printed pedagogical aids to support elementary entomology. The first aid was a set of cards showing photos of the 3D-printed insect prototype, which could help the learners in close observation of the insect body’s regions and parts, and the second 3D-printed aid was a construction kit mimicking physical insects, which were made of an enlarged 3D-printed insect prototype. The two aids were used in our field experiment to examine the effectiveness in learning and motivation. A total of 153 pupils were grouped into three groups (one using the existing multimedia e-book, one using the first 3DP aid, and one using the second aid). The results confirm that the digital media application—in this case, 3DP technologies—were able to compensate for the lack of the live insects. These results raised our confidence in using a customized size 3D-printed insect prototype to enhance rudimentary entomology inside the classroom. If the 3DP technologies are used properly, they could offer an innovative and sustainable solution.