bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantifying Geodiversity at the Continental Scale: Limitations and Prospects
Authors: Wolniewicz Paweł
Published: May 2023

Geodiversity, defined as the natural range of geological, geomorphological, hydrological, and soil features, has been explored in a growing number of studies at different spatial scales. However, most research efforts have been directed at analysing geodiversity at a fine-scale. Here, an estimation of the geodiversity of the European subcontinent is performed using six available high-resolution global data sets that describe the diversity of selected features of the abiotic environment. Six maps representing geomorphological, geological, hydrological, hydrogeological, soil, and topographical diversity are compiled using the methods of centroid analysis and kernel density estimation. The present contribution identifies areas with high values of most geodiversity variables which were not previously studied, nor are included in the network of international geoparks. The study also shows that, although remote sensing images and digital elevation models allow one to grasp a significant understanding of geodiversity on the continental scale, fine-resolution and process-oriented geological data sets are required to further enhance the quality of large-scale geodiversity assessments. To ensure interoperability between studies, a consensus is necessary regarding the analytical methods, classification rules, standardised indices and dictionaries. Without this, comparisons of geodiversity evaluations across different scales and between distant study areas remain difficult.
SARAL’s Full Mission Reprocessing: Improvement with the GDR-F Standard
Authors: Jettou Ghita , Rousseau Manon , Piras Fanny , Simeon Mathilde , Tran Ngan
Published: May 2023

Seven years (2013–2019) of the French/Indian mission SARAL altimetry data have been successfully reprocessed within the SALP contract supported by CNES to produce a new data set of GDR (Geophysical Data Record) using an updated, modern set of algorithms and models. The main objective of this article is to assess the quality of the reprocessed dataset and estimate the system’s performance using GDR-F products. To achieve this goal, the new dataset has been validated against the previous one (identified as GDR-T) using mono-mission metrics and comparisons to reference altimetry missions such as Jason-2 and Jason-3. The new data set shows a clear improvement in data quality. The product validation shows a reduction of the standard deviation of crossovers’ Sea Surface Height differences from 5.5 cm (GDR-T) to 5.2 cm (GDR-F). This paper presents the main processing changes and shows some of the results from the validation and quality-assurance processes. The major improvement of the GDR-F data set with respect to the previous one is due to the use of state-of-the-art orbit standards (POE-F) and geophysical corrections, including new tidal models, a new wet troposphere retrieval algorithm, and a new algorithm for sea state estimation. The intent of this paper is to highlight the overall benefit of this new dataset.
Simulation Study on the Effect of Elevated CO2 on Regional Temperature Change on the Loess Plateau
Authors: Shi Zhifang , Cui Yaoping , Wu Liyang , Zhou Yan , Li Mengdi , Zhou Shenghui
Published: May 2023

CO2 undisputedly affects global temperature change, but the specific impact of change in atmospheric CO2 concentration on regional warming remains to be quantified, especially in different climatic backgrounds. Taking the Loess Plateau as the research area, this study quantified the effect of CO2 elevation on regional temperature change based on a single-factor sensitivity experiment of the regional Weather Research and Forecasting (WRF) climatic model, and the results revealed the following: (i) The correlation coefficient between monthly mean values of temperature simulated by the WRF model and the observed values reached 0.96 (p < 0.01), and the overall spatial trends of simulated and observed temperatures increased from the northwest to the southeast. (ii) CO2 concentration increased from 370.70 ppm in 2000 to 414.54 ppm in 2020, and the Loess Plateau region warmed by 0.04 and 0.06 °C under the MODIS land cover of 2000 and 2020, respectively. This indicates that increase in CO2 concentration over the Loess Plateau has greater impact than land cover change on regional temperature change. (iii) As CO2 concentration increased, the maximum fluctuation of temperature in summer exceeded 2.0 °C, while the fluctuations in spring (0.72 °C), autumn (0.77 °C), and winter (0.15 °C) were relatively small, indicating that summer temperature is most sensitive to CO2 concentration change. By emphasizing the marked temperature difference associated with the same CO2 change in different seasons, this study provides an important basis for extending the understanding of the differences in the effect of CO2 on regional temperatures.
Distillation Sparsity Training Algorithm for Accelerating Convolutional Neural Networks in Embedded Systems
Authors: Xiao Penghao , Xu Teng , Xiao Xiayang , Li Weisong , Wang Haipeng
Published: May 2023

The rapid development of neural networks has come at the cost of increased computational complexity. Neural networks are both computationally intensive and memory intensive; as such, the minimal energy and computing power of satellites pose a challenge for automatic target recognition (ATR). Knowledge distillation (KD) can distill knowledge from a cumbersome teacher network to a lightweight student network, transferring the essential information learned by the teacher network. Thus, the concept of KD can be used to improve the accuracy of student networks. Even when learning from a teacher network, there is still redundancy in the student network. Traditional networks fix the structure before training, such that training does not improve the situation. This paper proposes a distillation sparsity training (DST) algorithm based on KD and network pruning to address the above limitations. We first improve the accuracy of the student network through KD, and then through network pruning, allowing the student network to learn which connections are essential. DST allows the teacher network to teach the pruned student network directly. The proposed algorithm was tested on the CIFAR-100, MSTAR, and FUSAR-Ship data sets, with a 50% sparsity setting. First, a new loss function for the teacher-pruned student was proposed, and the pruned student network showed a performance close to that of the teacher network. Second, a new sparsity model (uniformity half-pruning UHP) was designed to solve the problem that unstructured pruning does not facilitate the implementation of general-purpose hardware acceleration and storage. Compared with traditional unstructured pruning, UHP can double the speed of neural networks.
Accounting for Non-Detects: Application to Satellite Ammonia Observations
Authors: White Evan , Shephard Mark W. , Cady-Pereira Karen E. , Kharol Shailesh K. , Ford Sean , Dammers Enrico , Chow Evan , Thiessen Nikolai , Tobin David , Quinn Greg , O’Brien Jason , Bash Jesse
Published: May 2023

Presented is a methodology to explicitly identify and account for cloud-free satellite measurements below a sensor’s measurement detection level. These low signals can often be found in satellite observations of minor atmospheric species with weak spectral signals (e.g., ammonia (NH3)). Not accounting for these non-detects can high-bias averaged measurements in locations that exhibit conditions below the detection limit of the sensor. The approach taken here is to utilize the information content from the satellite signal to explicitly identify non-detects and then account for them with a consistent approach. The methodology is applied to the CrIS Fast Physical Retrieval (CFPR) ammonia product and results in a more realistic averaged dataset under conditions where there are a significant number of non-detects. These results show that in larger emission source regions (i.e., surface values > 7.5 ppbv) the non-detects occur less than 5% of the time and have a relatively small impact (decreases by less than 5%) on the gridded averaged values (e.g., annual ammonia source regions). However, in regions that have low ammonia concentration amounts (i.e., surface values < 1 ppbv) the fraction of non-detects can be greater than 70%, and accounting for these values can decrease annual gridded averaged values by over 50% and make the distributions closer to what is expected based on surface station observations.
A Novel Saliency-Based Decomposition Strategy for Infrared and Visible Image Fusion
Authors: Qi Biao , Bai Xiaotian , Wu Wei , Zhang Yu , Lv Hengyi , Li Guoning
Published: May 2023

The image decomposition strategy that extracts salient features from the source image is crucial for image fusion. To this end, we proposed a novel saliency-based decomposition strategy for infrared and visible image fusion. In particular, the latent low-rank representation (LatLRR) and rolling guidance filter (RGF) are together employed to process source images, which is called DLatLRR_RGF. In this method, the source images are first decomposed to salient components and base components based on LatLRR, and the salient components are filtered by RGF. Then, the final base components can be calculated by the difference between the source image and the processed salient components. The fusion rule based on the nuclear-norm and modified spatial frequency is used to fuse the salient components. The base components are fused by the l2-energy minimization model. Finally, the fused image can be obtained by the fused base components and saliency detail components. Multiple groups of experiments on different pairs of infrared and visible images demonstrate that, compared with other state-of-the-art fusion algorithms, our proposed method possesses superior fusion performance from subjective and objective perspectives.
A Class-Incremental Learning Method for SAR Images Based on Self-Sustainment Guidance Representation
Authors: Pan Qidi , Liao Kuo , He Xuesi , Bu Zhichun , Huang Jiyan
Published: May 2023

Existing deep learning algorithms for synthetic aperture radar (SAR) image recognition are performed with offline data. These methods must use all data to retrain the entire model when new data are added. However, facing the real application environment with growing data, retraining consumes much time and memory space. Class-Incremental Learning (CIL) addresses this problem that deep learning faces in streaming data. The goal of CIL is to enable the model to continuously learn new classes without using all data to retrain the model while maintaining the ability to recognize previous classes. Most of the CIL methods adopt a replay strategy to realize it. However, the number of retained samples is too small to carry enough information. The replay strategy is still trapped by forgetting previous knowledge. For this reason, we propose a CIL method for SAR images based on self-sustainment guidance representation. The method uses the vision transformer (ViT) structure as the basic framework. We add a dynamic query navigation module to enhance the model’s ability to learn the new classes. This module stores special information about classes and uses it to guide the direction of feature extraction in subsequent model learning. In addition, the method also comprises a structural extension module to defend the forgetting of old classes when the model learns new knowledge. It is constructed to maintain the representation of the model in previous classes. The model will learn under the coordinated guidance of old and new information. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our method performs well with remarkable advantages in CIL tasks. This method has a better accuracy rate and performance dropping rate than state-of-the-art methods under the same setting and maintains the ability of incremental learning with fewer replay samples. Additionally, experiments on a popular image dataset (CIFAR100) also demonstrate the scalability of our approach.
Geometric Primitive-Guided UAV Path Planning for High-Quality Image-Based Reconstruction
Authors: Zhou Hao , Ji Zheng , You Xiangyu , Liu Yuchen , Chen Lingfeng , Zhao Kun , Lin Shan , Huang Xiangxiang
Published: May 2023

Image-based refined 3D reconstruction relies on high-resolution and multi-angle images of scenes. The assistance of multi-rotor drones and gimbal provides great convenience for image acquisition. However, capturing images with manual control generally takes a long time. It could easily lead to redundant or insufficient local area coverage, resulting in poor quality of the reconstructed model. We propose a surface geometric primitive-guided UAV path planning method (SGP-G) that aims to automatically and quickly plan a collision-free path to capture fewer images, based on which high-quality models can be obtained. The geometric primitives are extracted by plane segmentation on the proxy, which performs three main functions. First, a more representative evaluation of the reconstructability of the whole scene is realized. Second, two optimization strategies for different geometric primitives are executed to quickly generate a near-global optimized set of viewpoints. Third, regularly arranged viewpoints are generated to improve the efficiency of image acquisition. Experiments on virtual and real scenes demonstrate the remarkable performance of our method. Compared with the state of the art, we accomplish the planning of the photographic path with higher efficiency in a relatively simple way, achieving equivalent and even higher quality of the reconstructed model with fewer images.
Transmission-Based Vertebrae Strength Probe Development: Far Field Probe Property Extraction and Integrated Machine Vision Distance Validation Experiments
Authors: Meaney Paul , Augustine Robin , Welteke Adrian , Pfrommer Bernd , Pearson Adam M. , Brisby Helena
Published: May 2023

We are developing a transmission-based probe for point-of-care assessment of vertebrae strength needed for fabricating the instrumentation used in supporting the spinal column during spinal fusion surgery. The device is based on a transmission probe whereby thin coaxial probes are inserted into the small canals through the pedicles and into the vertebrae, and a broad band signal is transmitted from one probe to the other across the bone tissue. Simultaneously, a machine vision scheme has been developed to measure the separation distance between the probe tips while they are inserted into the vertebrae. The latter technique includes a small camera mounted to the handle of one probe and associated fiducials printed on the other. Machine vision techniques make it possible to track the location of the fiducial-based probe tip and compare it to the fixed coordinate location of the camera-based probe tip. The combination of the two methods allows for straightforward calculation of tissue characteristics by exploiting the antenna far field approximation. Validation tests of the two concepts are presented as a precursor to clinical prototype development.
Reinforcement-Learning-Based Robust Resource Management for Multi-Radio Systems
Authors: Delaney James , Dowey Steve , Cheng Chi-Tsun
Published: May 2023

The advent of the Internet of Things (IoT) has triggered an increased demand for sensing devices with multiple integrated wireless transceivers. These platforms often support the advantageous use of multiple radio technologies to exploit their differing characteristics. Intelligent radio selection techniques allow these systems to become highly adaptive, ensuring more robust and reliable communications under dynamic channel conditions. In this paper, we focus on the wireless links between devices equipped by deployed operating personnel and intermediary access-point infrastructure. We use multi-radio platforms and wireless devices with multiple and diverse transceiver technologies to produce robust and reliable links through the adaptive control of available transceivers. In this work, the term ‘robust’ refers to communications that can be maintained despite changes in the environmental and radio conditions, i.e., during periods of interference caused by non-cooperative actors or multi-path or fading conditions in the physical environment. In this paper, a multi-objective reinforcement learning (MORL) framework is applied to address a multi-radio selection and power control problem. We propose independent reward functions to manage the trade-off between the conflicting objectives of minimised power consumption and maximised bit rate. We also adopt an adaptive exploration strategy for learning a robust behaviour policy and compare its online performance to conventional methods. An extension to the multi-objective state–action–reward–state–action (SARSA) algorithm is proposed to implement this adaptive exploration strategy. When applying adaptive exploration to the extended multi-objective SARSA algorithm, we achieve a 20% increase in the F1 score in comparison to one with decayed exploration policies.
Improved the Impact of SST for HY-2A Scatterometer Measurements by Using Neural Network Model
Authors: Wang Jing , Xie Xuetong , Deng Ruru , Li Jiayi , Tang Yuming , Liang Yeheng , Guo Yu
Published: May 2023

The variation of sea surface temperature (SST) can change the backscatter coefficient measured by a scatterometer, resulting in a decrease in the accuracy of the sea surface wind measurement. This study proposed a new approach to correct the effect of SST on the backscatter coefficient. The method focuses on the Ku-band scatterometer HY-2A SCAT, which is more sensitive to SST than C-band scatterometers, can improve the wind measurement accuracy of the scatterometer without relying on reconstructed geophysical model function (GMF), and is more suitable for operational scatterometers. Through comparisons to WindSat wind data, we found that the Ku-band scatterometer HY-2A SCAT wind speeds are systemically lower under low SST and higher under high SST conditions. We trained a neural network model called the temperature neural network (TNNW) using HY-2A data and WindSat data. TNNW-corrected backscatter coefficients retrieved wind speed with a small systematic deviation from WindSat wind speed. In addition, we also carried out a validation of HY-2A wind and TNNW wind using European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data as a reference, and the results showed that the retrieved TNNW-corrected backscatter coefficient wind speed is more consistent with ECMWF wind speed, indicating that the method is effective in correcting SST impact on HY-2A scatterometer measurements.
Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks
Authors: Mapas Jérémi , Lefrançois Alexandre , Aubert Hervé , Comte Sacha , Barbarin Yohan , Lavayssière Maylis , Rougier Benoit , Dore Alexandre
Published: May 2023

In this paper, a neural network approach is applied for solving an electromagnetic inverse problem involving solid dielectric materials subjected to shock impacts and interrogated by a millimeter-wave interferometer. Under mechanical impact, a shock wave is generated in the material and modifies the refractive index. It was recently demonstrated that the shock wavefront velocity and the particle velocity as well as the modified index in a shocked material can be remotely derived from measuring two characteristic Doppler frequencies in the waveform delivered by a millimeter-wave interferometer. We show here that a more accurate estimation of the shock wavefront and particle velocities can be obtained from training an appropriate convolutional neural network, especially in the important case of short-duration waveforms of few microseconds.
Wood Veneer Defect Detection Based on Multiscale DETR with Position Encoder Net
Authors: Ge Yilin , Jiang Dapeng , Sun Liping
Published: May 2023

Wood is one of the main building materials. However, defects on veneers result in substantial waste of wood resources. Traditional veneer defect detection relies on manual experience or photoelectric-based methods, which are either subjective and inefficient or need substantial investment. Computer vision-based object detection methods have been used in many realistic areas. This paper proposes a new deep learning defect detection pipeline. First, an image collection device is constructed and a total of more than 16,380 defect images are collected coupled with a mixed data augmentation method. Then, a detection pipeline is designed based on DEtection TRansformer (DETR). The original DETR needs position encoding functions to be designed and is ineffective for small object detection. To solve these problems, a position encoding net is designed with multiscale feature maps. The loss function is also redefined for much more stable training. The results from the defect dataset show that using a light feature mapping network, the proposed method is much faster with similar accuracy. Using a complex feature mapping network, the proposed method is much more accurate with similar speed.
Repairing Artifacts in Neural Activity Recordings Using Low-Rank Matrix Estimation
Authors: Naik Shruti , Dehaene-Lambertz Ghislaine , Battaglia Demian
Published: May 2023

Electrophysiology recordings are frequently affected by artifacts (e.g., subject motion or eye movements), which reduces the number of available trials and affects the statistical power. When artifacts are unavoidable and data are scarce, signal reconstruction algorithms that allow for the retention of sufficient trials become crucial. Here, we present one such algorithm that makes use of large spatiotemporal correlations in neural signals and solves the low-rank matrix completion problem, to fix artifactual entries. The method uses a gradient descent algorithm in lower dimensions to learn the missing entries and provide faithful reconstruction of signals. We carried out numerical simulations to benchmark the method and estimate optimal hyperparameters for actual EEG data. The fidelity of reconstruction was assessed by detecting event-related potentials (ERP) from a highly artifacted EEG time series from human infants. The proposed method significantly improved the standardized error of the mean in ERP group analysis and a between-trial variability analysis compared to a state-of-the-art interpolation technique. This improvement increased the statistical power and revealed significant effects that would have been deemed insignificant without reconstruction. The method can be applied to any time-continuous neural signal where artifacts are sparse and spread out across epochs and channels, increasing data retention and statistical power.
Effect of Sensor Size, Number and Position under the Foot to Measure the Center of Pressure (CoP) Displacement and Total Center of Pressure (CoPT) Using an Anatomical Foot Model
Authors: Abou Ghaida Hussein , Poffo Luiz , Le Page Ronan , Goujon Jean-Marc
Published: May 2023

Ambulatory instrumented insoles are widely used in real-time monitoring of the plantar pressure in order to calculate balance indicators such as Center of Pressure (CoP) or Pressure Maps. Such insoles include many pressure sensors; the required number and surface area of the sensors used are usually determined experimentally. Additionally, they follow the common plantar pressure zones, and the quality of measurement is usually strongly related to the number of sensors. In this paper, we experimentally investigate the robustness of an anatomical foot model, combined with a specific learning algorithm, to measure the static displacement of the center of pressure (CoP) and the center of total pressure (CoPT), as a function of the number, size, and position of sensors. Application of our algorithm to the pressure maps of nine healthy subjects shows that only three sensors per foot, with an area of about 1.5 × 1.5 cm2, are needed to give a good approximation of the CoP during quiet standing when placed on the main pressure areas.
From SLAM to Situational Awareness: Challenges and Survey
Authors: Bavle Hriday , Sanchez-Lopez Jose Luis , Cimarelli Claudio , Tourani Ali , Voos Holger
Published: May 2023

The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.
Composite Bridge Girders Structure Health Monitoring Based on the Distributed Fiber Sensing Textile
Authors: Wu Rui , Biondi Andres , Cao Lidan , Gandhi Harsh , Abedin Sabrina , Cui Guoqiang , Yu Tzuyang , Wang Xingwei
Published: May 2023

Distributed structure health monitoring has been a hot research topic in recent years, and optic fiber sensors are largely developed for the advantages of high sensitivity, better spatial resolution, and small sensor size. However, the limitation of fibers in installation and reliability has become one of the major drawbacks of this technology. This paper presents a fiber optic sensing textile and a new installation method inside bridge girders to address those shortcomings in fiber sensing systems. The sensing textile was utilized to monitor strain distribution in the Grist Mill Bridge located in Maine based on Brillouin Optical Time Domain Analysis (BOTDA). A modified slider was developed to increase the efficiency of installation in the confined bridge girders. The bridge girder’s strain response was successfully recorded by the sensing textile during the loading tests that involved four trucks on the bridge. The sensing textile demonstrated the capability to differentiate separated loading locations. These results demonstrate a new way of installing fiber optic sensors and the potential applications of fiber optic sensing textiles in structural health monitoring.
Impact of Supersonic Flow in Scintillator Detector Apertures on the Resulting Pumping Effect of the Vacuum Chambers
Authors: Maxa Jiří , Neděla Vilém , Šabacká Pavla , Binar Tomáš
Published: May 2023

The article describes the combination of experimental measurements with mathematical–physics analyses in flow investigation in the chambers of the scintillator detector, which is a part of the environmental scanning electron microscope. The chambers are divided with apertures by small openings that keep the desirable pressure differences between three chambers: The specimen chamber, the differentially pumped intermediate chamber, and the scintillator chamber. There are conflicting demands on these apertures. On the one hand, the diameter of the apertures must be as big as possible so that they incur minimal losses of the passing secondary electrons. On the other hand, it is possible to magnify the apertures only to a certain extent so the rotary and turbomolecular vacuum pump can maintain the required operating pressures in separate chambers. The article describes the combination of experimental measurement using an absolute pressure sensor and mathematical physics analysis to map all the specifics of the emerging critical supersonic flow in apertures between the chambers. Based on the experiments and their tuned analyses, the most effective variant of combining the sizes of each aperture concerning different operating pressures in the detector is determined. The situation is made more difficult by the described fact that each aperture separates a different pressure gradient, so the gas flow through each aperture has its own characteristics with a different type of critical flow, and they influence each other, thereby influencing the final passage of secondary electrons detected by the scintillator and thus affecting the resulting displayed image.
Compressed Imaging Reconstruction Based on Block Compressed Sensing with Conjugate Gradient Smoothed l0 Norm
Authors: Zhang Yongtian , Chen Xiaomei , Zeng Chao , Gao Kun , Li Shuzhong
Published: May 2023

Compressed imaging reconstruction technology can reconstruct high-resolution images with a small number of observations by applying the theory of block compressed sensing to traditional optical imaging systems, and the reconstruction algorithm mainly determines its reconstruction accuracy. In this work, we design a reconstruction algorithm based on block compressed sensing with a conjugate gradient smoothed l0 norm termed BCS-CGSL0. The algorithm is divided into two parts. The first part, CGSL0, optimizes the SL0 algorithm by constructing a new inverse triangular fraction function to approximate the l0 norm and uses the modified conjugate gradient method to solve the optimization problem. The second part combines the BCS-SPL method under the framework of block compressed sensing to remove the block effect. Research shows that the algorithm can reduce the block effect while improving the accuracy and efficiency of reconstruction. Simulation results also verify that the BCS-CGSL0 algorithm has significant advantages in reconstruction accuracy and efficiency.
Person Recognition Based on Deep Gait: A Survey
Authors: Khaliluzzaman Md. , Uddin Ashraf , Deb Kaushik , Hasan Md Junayed
Published: May 2023

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.
Bridging Disciplinary Divides through Computational Social Sciences and Transdisciplinarity in Tourism Education in Higher Educational Institutions: An Austrian Case Study
Authors: Lampoltshammer Thomas J. , Wallinger Stefanie , Scholz Johannes
Published: May 2023

Grand societal issues such as climate change and technological disruption challenge all industry sectors, including tourism. To cope with these challenges, new sustainable business models that not only rely on data-driven technologies but also require new ways of collaboration beyond disciplines and sectors by facilitating the overall conception of transdisciplinarity are essential. One potential way to combine all these requirements is computational social sciences. As a discipline-crossing approach, it should be anchored within tourism education to train the future workforce and experts necessary to realize the needed transformation. Thus, this study explores the status quo of tourism curricula in higher educational institutions in Austria through the lens of computational social sciences. In doing so, a set of core modules of computational social sciences content was developed as an analytical framework. The results show that there is still a significant gap between the demands of the tourism industry and the offered educational programs in Austria. The article concludes with insights on how to close the existing gap and some suggestions for possible foundational steps to support the transformation.
Future Smart Logistics Technology Based on Patent Analysis Using Temporal Network
Authors: Kwon Koopo , So Jaeryong
Published: May 2023

This study aims to predict new technologies by analyzing patent data and identifying key technology trends using a Temporal Network. We have chosen big data-based smart logistics technology as the scope of our analysis. To accomplish this, we first extract relevant patents by identifying technical keywords from prior literature and industry reports related to smart logistics. We then employ a technology prospect analysis to assess the innovation stage. Our findings indicate that smart logistics technology is in a growth stage characterized by continuous expansion. Moreover, we observe a future-oriented upward trend, which quantitatively confirms its classification as a hot technology domain. To predict future advancements, we establish an IPC Temporal Network to identify core and converging technologies. This approach enables us to forecast six innovative logistics technologies that will shape the industry’s future. Notably, our results align with the logistics technology roadmaps published by various countries worldwide, corroborating our findings’ reliability. The methodology presents in this research provides valuable data for developing R&D strategies and technology roadmaps to advance the smart logistics sector.
A Hybrid Photovoltaic/Diesel System for Off-Grid Applications in Lubumbashi, DR Congo: A HOMER Pro Modeling and Optimization Study
Authors: Rice Ilunga Kajila , Zhu Hanhua , Zhang Cunquan , Tapa Arnauld Robert
Published: May 2023

In Lubumbashi, the capital of Haut Katanga in the Democratic Republic of the Congo (DR Congo), diesel power plants are a common source of electricity. The need to utilize local renewable energy sources in DR Congo has increased due to the unreliability of the state grid and the rising cost of running diesel generators. Solar photovoltaic (PV) panels and batteries, in particular, have recently recorded significant price drops. It is important for operators and suppliers to choose optimal generators together with a renewable energy system to lessen the energy deficit. Diesel generators are still widely used in DRC, but their efficiency pales in contrast to that of more recent power facilities. Consuming fossil fuels results in high expenses for upkeep and operation, in addition to severe environmental damage. This study assessed the feasibility of using local weather and technical data to evaluate the efficiency of a diesel power plant hybridized with a PV system. The Hybrid Optimization Model for Electric Renewable (HOMER) simulations suggest that the hybrid system schedule is preferable due to its many economic and environmental advantages for the local community and its inhabitants. The promotion of such a hybrid system may encourage the sustainable economic development of a stable source of electricity for the Congo Region.
Antimicrobial Activities of Olive Oil Mill Wastewater Extracts against Selected Microorganisms
Authors: Sar Taner , Akbas Meltem Yesilcimen
Published: May 2023

Discovering eco-friendly alternatives to synthetic chemicals has become an increasingly popular area of research. Natural products are now in the spotlight for their potential use as replacements for synthetic chemicals. To maximize the benefits of these natural products, it is important to use efficient extraction methods, especially from agroindustrial waste. Olive oil mill wastewater (OOMW) is a byproduct of the olive oil production process and is considered a pollutant; however, OOMW contains a wide range of phenolic compounds that have proven antimicrobial properties. This study investigates the extraction of these compounds from OOMW, with the aim of determining their potential antimicrobial activities against several bacterial strains and fungi, including Bacillus spizizenii, Bacillus cereus, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella aerogenes, Streptococcus uberis, Enterococcus faecalis, and Candida albicans. The OOMW extracts (OEs) were prepared by using three different solvents: ethyl acetate, ethanol, and methanol. The highest total phenolic contents (4.03 g, GAE/L) and the strongest antibacterial activity were obtained with methanol extraction. All OEs showed no antifungal activity against C. albicans. OEs, particularly methanol extracts of OOMW, can be used as bioactive substances in various industries as nutraceuticals and food ingredients, respectively.
A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
Authors: Wang Xuming , Zhou Jiaqi , Yu Xiaobing , Yu Xianrui
Published: May 2023

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients’ suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP; (2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.
Identification of the Variables Involved in People’s Intellectual Understanding of Outdoor Cultural and Natural Spaces with the Support of ICT
Authors: Valencia-Arias Alejandro , Correa Paula Rodríguez , Garcia-Pineda Vanessa , Londoño Wilmer , Quiroz-Fabra Jefferson , García-Arango David
Published: May 2023

The use of educational platforms that allow dynamic learning mediated by information and communication technologies (ICT) has become a common method in different educational institutions. Thus far, these methodologies have been seen mainly within the classroom. Thus, it would be relevant to identify whether using these technologies to recognize cultural and natural exteriors allows complete learning by the users. Based on the above, the objective of this work was to identify the variables involved in people’s intellectual understanding of outdoor cultural and natural spaces with the support of ICT through a systematic literature review methodology based on PRISMA. The study found gamification to be one of the most relevant elements in the learning experience as well as the interdisciplinary approach to learning as a teaching methodology. As the main conclusion, we found that, although different technological tools have been developed for more dynamic learning experiences, the models developed for the evaluation of these tools in learning about outdoor cultural and natural spaces do not yet focus on knowledge consciousness, which still leaves open the field of research in this aspect.
Projection of Meteorological Dryness/Wetness Evolution Based on Multi-Model Scenarios in Poyang Lake Basin, China
Authors: Deng Yueping , Jiang Wenyu , Zhang Tianyu , Chen Jing , Wu Zhi , Liu Yuanqing , Tao Xinyue , Liu Bo
Published: May 2023

Based on the projections of three shared socioeconomic pathways (SSPs) scenarios of three climate models of CMIP6, this study analyzed the standardized precipitation evapotranspiration index (SPEI) to understand the future meteorological dryness/wetness changes in the Poyang Lake basin (PLB) from 2021 to 2100. The effect of temperature change on the dryness and wetness variation was explored by comparing the trends of SPEI and standardized precipitation index (SPI) at multiple-time scales and different SSPs scenarios. The results indicate that the frequency of drought events may increase by 1.1~3.8% than the historical period in the three scenarios, and they may be higher than that of wetness events in the future of this century. Cumulative months of drought events are higher in most decades than the wetness events, and especially in the 2090s. A total of 43 months may suffer drought events in the 2090s under the SSP585 scenario, which is more than twice the wetness events. With the enhanced concentration of greenhouse gas (GHG) emissions, both the frequency of droughts and the proportion of extreme droughts show a significant increasing trend at 99% confidence in PLB. The spatial distribution of net precipitation is generally in the southwest–northeast pattern, yet it is still in different values in most scenarios; thus, the uncertainty of dryness/wetness spatial conditions should be considered. The SPI detects more wetness events and a more intensive wetting trend, while the SPEI shows the opposite. The difference between SPI and SPEI gradually increases with GHG emission concentration, and may even lead to contrary conclusion in the last two decades at a 48-month scale under the SSP245 and 585 scenarios, indicating the unneglectable impact of increasing temperature and evapotranspiration on the dryness/wetness conditions in the future. The research results can help to predict the evolution pattern of dry and wet occurrence in the PLB in the future and promote flood/drought control and disaster mitigation.
Which Is More Environmentally Friendly? A Comparative Analysis of the Environmental Benefits of Two Waste-to-Energy Technologies for Plastics Based on an LCA Model
Authors: Zhao Yumeng , Ren Kai , Huang Wenfang
Published: May 2023

Waste plastics are low-value waste; thus, achieving high-value recycling is the most desirable goal. Scientific methods are required to evaluate the environmental benefits and support the technology optimization and market selection of waste-to-energy technology for plastics. This study selected catalytic cracking and incineration power generation of two typical examples of waste plastics energy technologies as the research objects, established a full life cycle assessment model, and used the mid-point method to analyze and compare the environmental benefits of the two technologies. The results showed that catalytic cracking technology is more environmentally friendly. The sensitivity analysis showed that the treatment units had a high impact on the results; process optimization predicted the efficiency of technology optimization and advocated future technological developments to explore the direction of using clean energy, upgrading equipment, and updating catalysts. The results of this study can provide ideas for the optimization of two kinds of waste plastics energy technology. In addition, the comparative data analysis intuitively demonstrated the advantages and disadvantages of waste-to-energy technologies and provided a practical path for the future development of high-value treatments for waste plastics.
Transition to a Circular Economy in Europe through New Business Models: Barriers, Drivers, and Policy Making
Authors: Försterling Gabi , Orth Ronald , Gellert Benjamin
Published: May 2023

In recent years, because of global challenges resulting from increased resource shortages and the climate crisis, interest in and the commitment to transition to a more sustainable economic system, especially a circular economy, has increased among scientists, politicians, and practitioners in Europe. To create a system that maintains the value of products as long as possible and minimizes waste, new business models, so-called circular business models (CBMs), are required. So far, as a result of far-reaching barriers, no breakthrough regarding CBMs has been observed and there are a lack of comprehensive analyses on the barriers and drivers of CBMs. Using a systematic literature analysis, this gap was filled and 637 barriers and 394 drivers were extracted from 76 publications, which were categorized into eight areas and synthesized in a comprehensive framework. The results show that an undifferentiated analysis of CBMs could result in incorrect assumptions, as the barriers between them differ. Overall, however, the most significant effect on all CBMs is from external barriers at a macro level. In this paper, drivers, in the form of success factors and political measures, were assigned to concrete barriers, indicating that policy interventions are needed in Europe in order to overcome these barriers and accelerate systematic change. The article provides research, policy, and practice with a theoretically grounded basis for analyzing these barriers and overcoming them.
Study on Multi-Objective Optimization of Power System Parameters of Battery Electric Vehicles
Authors: Hu Jie , Cao Wentong , Jiang Feng , Hu Lingling , Chen Qian , Zheng Weiguang , Zhou Junming
Published: May 2023

The optimization of power parameters is the key to the design of pure electric vehicles. Reasonable matching of the relationship between various parameters can effectively reduce energy consumption and achieve energy sustainability. In this paper, several vehicle performance indexes such as maximum vehicle speed, acceleration time and power consumption per 100 km were used as optimization target vectors, and transmission ratio was used as optimization variable to establish the optimization problem of parameter matching. Then, the feasible domain of the transmission ratio was obtained by taking the lowest performance index of the vehicle as the constraint condition. In the feasible domain, the multi-objective genetic algorithm is used to solve the optimization problem. The Pareto optimal solution set is obtained for fixed ratio transmission and two-gear transmission, which is used as an alternative solution set. The final parameter-matching scheme is determined by comparing the alternative scheme set of different motors comprehensively. The results show that the competition relationship between multiple optimizable indexes can be described effectively by solving the Pareto front. Specifically, the Pareto optimal solution set for the motor A + fixed transmission scheme is 1.33~1.85; the Pareto optimal solution set for the motor A + 2 transmission scheme is [1.72, 0.98]~[2.99, 1.57], and the Pareto optimal solution set for the motor B + 2 transmission scheme is [2.99, 1.40]~[2.99, 1.57]. The motor A + fixed transmission scheme does not require A clutch and does not require designing a shift algorithm. Therefore, after comprehensive consideration, the motor A + fixed transmission ratio transmission scheme is set as the final scheme.