bluuebunny/crossref_metadata_2025 huggingface dataset:
Subcritical Water as Pretreatment Technique for Bioethanol Production from Brewer’s Spent Grain within a Biorefinery Concept
Authors: Alonso-Riaño Patricia , Amândio Mariana S. T. , Xavier Ana M. R. B. , Beltrán Sagrario , Sanz Maria Teresa
Published: November 2022

Bioeconomy and environmental issues envisage industrial by-products such as Brewer’s spent grain (BSG) as renewable resources for their recycling and reuse within a biorefinery concept. This study aimed to investigate the production of bioethanol from subcritical water (subW) pretreated BSG, following the conversion of the BSG biopolymers cellulose and hemicelluloses. The subW pretreatment was performed in a batch reactor at 174 °C, during 60 min and 5% (w/v) of dry BSG charge. The behavior of BSG biopolymers under subW pretreatment was monitored by evaluating the chemical composition of the liquid and solid streams and the chemical and structural changes caused in the solid residues by scanning electron microscope (SEM), CHNS elemental analysis and water retention value (WRV). The production of bioethanol from subW-pretreated BSG was assessed by separate hydrolysis and fermentation (SHF) and also by simultaneous saccharification and fermentation (SSF) by using the enzymatic cocktail Celluclast 1.5 L (40 FPU/gsolids) and the yeast Ethanol Red®. The higher bioethanol productivity (1.073 g∙L−1∙h−1) and concentration (32.18 g/L) were achieved by SSF with higher solids’ loading (25%) and following a fed-batch strategy. These results suggest that subcritical water pretreatment is a promising technology for the valorization of BSG as a feedstock for second-generation bioethanol production.
Vapor–Liquid Equilibria of Quaternary Systems of Interest for the Supercritical Antisolvent Process
Authors: Campardelli Roberta , Mottola Stefania , De Marco Iolanda
Published: November 2022

In the Supercritical Antisolvent process (SAS), the thermodynamic behavior of complex multicomponent systems can influence the particles’ morphology. However, due to the limited thermodynamic data for multicomponent systems, the effect of solutes is often neglected, and the system is considered as pseudo-binary. It has been demonstrated that the presence of a solute can significantly influence the thermodynamic behavior of the system. In particular, when the SAS process is adopted for the production of drug/polymer coprecipitated microparticles, the effect of both the drug and the polymer in the solvent/CO2 mixture should be considered. In this work, the effect of polyvinylpyrrolidone (PVP), used as the carrier, and of the liposoluble vitamins menadione (MEN) and α-tocopherol (TOC), as model drugs, was investigated as a deviation from the fundamental thermodynamic behavior of the DMSO/CO2 binary system. Vapor–liquid equilibria (VLE) were evaluated at 313 K, with a PVP concentration in the organic solution equal to 20 mg/mL. The effect of the presence of PVP, MEN, and TOC on DMSO/CO2 VLE at 313 K was studied; furthermore, the effect of PVP/MEN and PVP/TOC, at a polymer/drug ratio of 5/1 and 3/1, was determined. Moreover, SAS precipitation experiments were performed at the same polymer/drug ratios using a pressure of 90 bar. Thermodynamic studies revealed significant changes in phase behavior for DMSO/CO2/PVP/TOC and DMSO/CO2/PVP/MEN systems compared to the binary DMSO/CO2 system. From the analysis of the effect of the presence of a single compound on the binary system VLE, it was noted that PVP slightly affected the thermodynamic behavior of the system. In contrast, these effects were more evident for the DMSO/CO2/TOC and DMSO/CO2/MEN systems. SAS precipitation experiments produced PVP/MEN and PVP/TOC microparticles, and the obtained morphology was justified considering the quaternary systems VLE.
Wearing Effect of Implant Steel Drills and Tappers for the Preparation of the Bone Osteotomies: An Infrared Thermal Analysis and Energy Dispersive Spectroscopy-Scanning Electron Microscopy (EDS-SEM) Study
Authors: Lorusso Felice , Gehrke Sergio Alexandre , Festa Felice , Scarano Antonio
Published: November 2022

Background: The thermal effect correlated with implant osteotomy could produce significant effects on the healing process and fixture osseointegration. The aim of the present investigation was to assess the heat generation and surface wearing of dental implant drills and manual tappers during simulated osteotomies on animal ribs. Methods: Steel drills (20 units per type) and tappers (20 units per type) were evaluated for a total of 30 osteotomies. The infrared thermal analysis was performed at the first and thirtieth osteotomy. The surface alteration and wearing was assessed by energy dispersive spectroscopy–scanning electron microscopy (EDS-SEM) prior to and after use. Conclusions: The drill material produced a non-significant temperature change during bone osteotomy. Lower heating was reported for manual tappers in favor of a manual osteotomy instead rotary instruments.
Is Anxiety Sensitivity Associated with COVID-19 Related Distress and Adherence among Emerging Adults?
Authors: Yunus Fakir Md. , Livet Audrey , Mahmoud Aram , Moore Mackenzie , Murphy Clayton B. , Nogueira-Arjona Raquel , Thompson Kara , Keough Matthew T. , Krank Marvin D. , Conrod Patricia J. , Stewart Sherry H.
Published: November 2022

We investigated whether anxiety sensitivity (AS) is associated with increased distress and adherence to public health guidelines during the COVID-19 pandemic among undergraduates, and whether increased distress mediates the relationship between AS and increased adherence. An online cross-sectional survey was conducted with 1318 first- and second-year undergraduates (mean age of 19.2 years; 79.5% females) from five Canadian universities. Relevant subscales of the Substance Use Risk Profile Scale (SURPS) and the Big Five Inventory-10 (BFI-10) were used to assess AS and neuroticism. Three measures tapped distress: the Patient Health Questionnaire-9 (PHQ-9) for depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms, and the Brief COVID-19 Stress Scales (CSS-B) for COVID-19-specific distress. The COVID-19 Adherence scale (CAD) assessed adherence to COVID-19 containment measures. AS was significantly independently associated with higher general distress (both anxiety and depressive symptoms) and higher COVID-19-specific distress, after controlling age, sex, study site, and neuroticism. Moreover, AS indirectly predicted greater adherence to COVID-19 preventive measures through higher COVID-19-specific distress. Interventions targeting higher AS might be helpful for decreasing both general and COVID-19-specific distress, whereas interventions targeting lower AS might be helpful for increasing adherence to public health containment strategies, in undergraduates.
Sustainable Bio-Composites Obtained from Recycling Post-Industrial PLA and Fillers Derived from Coffee Production
Authors: Saccani Andrea , Fiorini Maurizio , Burzotta Gianfranco
Published: November 2022

Bio-composites have been formulated by exploiting post-industrial PLA derived from thin film production and silver skin, a by-product of the coffee’s roasting process. The mix design for the compounds exploits the effects of regrading and toughening agents. The mechanical properties of the materials have been investigated as well as the thermal and physical ones. The recycled material mixed with the regrading additive shows good mechanical properties. The filler addition increases the elastic modulus of PLA up to a 20% but decreases the mechanical properties (about 20% on tensile strength), leading to a brittle behavior (minus 35% of impact strength). The use of a toughening agent restores the plastic deformation ability of the matrix.
Hadamard–Viterbi Joint Soft Decoding for MFSK Underwater Acoustic Communications
Authors: Wu Fei-Yun , Tian Tian , Su Ben-Xue , Song Yan-Chong
Published: November 2022

Multiple Frequency-Shift Keying (MFSK) has been used widely for underwater acoustic communications due to its low complexity and channel robustness. However, the traditional MFSK has the limitation of a low bit rate compared with coherent acoustic communication. To increase the bit rate, this study designs a new MFSK with the concept of orthogonal frequency division multiplexing (OFDM). We also adopt a channel-concatenated coding to resist the multipath interference and design the iterative joint decoding. The channel-concatenated coding consists of a Hadamard code and a convolutional code. Correspondingly, the iterative joint decoding uses the Hadamard–Viterbi joint soft decoding framework with a newly designed branch metric, which uses the Hadamard structure. As an important preprocessing link for a received signal, frame synchronization and Doppler compensation are also described in detail in this study. Simulations and experiments are conducted to show the effectiveness of the proposed MFSK underwater acoustic communications.
HF Noise Characteristics over Cyprus
Authors: Haralambous Haris , Constantinides Antonios , Paul Krishnendu Sekhar
Published: November 2022

Due to the long distance propagation characteristics of the HF spectrum, noise and interference are significant and challenging factors that have to be addressed in the context of HF system performance. Both aspects exhibit systematic diurnal, seasonal, and solar cycle variability that result in a number of corresponding measurement campaigns all over the globe, in an effort to characterise HF noise. Noise at HF consists of man-made and naturally occurring HF noise with atmospheric and galactic components. An understanding of HF noise variation in terms of time, azimuth, and filter bandwidth is crucial for a specific location, as it can provide a background knowledge based on which appropriate values of HF system parameters may be selected. This paper deals with the temporal, azimuthal, and bandwidth characteristics of HF noise based on a one-month dataset of electric field strength measurements recorded by a directional antenna in Cyprus.
Validation of Atmospheric Absorption Models within the 20–60 GHz Band by Simultaneous Radiosonde and Microwave Observations: The Advantage of Using ECS Formalism
Authors: Belikovich Mikhail V. , Makarov Dmitriy S. , Serov Evgeny A. , Kulikov Mikhail Yu. , Feigin Alexander M.
Published: November 2022

The precise calculation of atmospheric absorption in a microwave band is highly important for atmospheric remote-sensing with ground-based and satellite-borne radiometers, as it is a key element in procedures for temperature, humidity or trace gas concentration retrieval. The accuracy of the absorption model directly affects the accuracy of the retrieved information and reliability of the resulting forecasts. In this study, we analyze the difference between observed and simulated microwave spectra obtained from more than four years of microwave and radiosonde observations over Nizhny Novgorod (56.2°N, 44°E). We focus on zenith-measured microwave data in the 20–60 GHz frequency range in clear-sky conditions. The use of a conventional absorption model in simulations leads to a significant difference in frequency channels within the 51–54 GHz range, while calculations employing a more accurate model based on the Energy Corrected Sudden (ECS) formalism for molecular oxygen absorption reduces the difference several-fold.
Investigating the Performance of Carbon Monoxide and Methane Observations from Sentinel-5 Precursor in China
Authors: Tian Yuan , Hong Xinhua , Shan Changgong , Sun Youwen , Wang Wei , Zhou Minqiang , Wang Pucai , Lin Peize , Liu Cheng
Published: November 2022

Since its launch on 13 October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) mission has been measuring the solar radiation backscattered by Earth’s atmosphere and surface. In this study, we evaluate the TROPOMI operational methane (CH4) and carbon monoxide (CO) products’ performance results covering about 3 years using the only two global Total Carbon Column Observing Network (TCCON) sites in China, i.e., the Hefei site and the Xianghe site. These two sites have recently joined the TCCON, and this study uses the both sites simultaneously to validate the TROPOMI products over China for the first time. We found that the systematic bias with rescaling between the TROPOMI CO products and the Hefei site is on average 1.78 ± 6.35 ppb or 1.18 ± 5.35%. The systematic bias with rescaling between the TROPOMI CO products and the Xianghe site is on average 5.33 ± 14.24 ppb or 3.85 ± 10.30%. Both the stations show a correlation above 0.9. The TROPOMI CO data are systematically higher than the two TCCON sites measurements in China. We found that the systematic bias with rescaling between the TROPOMI CH4 products and the Hefei site is on average −4.13 ± 11.65 ppb or −0.22 ± 0.62%. The systematic bias between the TROPOMI CH4 products and Xianghe site is on average −7.25 ± 10.72 ppb or −0.39 ± 0.57%. Both the stations show a correlation above 0.9. The TROPOMI CH4 data are systematically lower than the two TCCON sites measurements in China. We found that the bias between the TROPOMI and the two sites’ data as a function of the coincident radius around the two sites is mostly affected by localized emissions for both CO and CH4. We also observe a CO decreasing trend and a CH4 increasing trend in the year-to-year relative changes from 2019 to 2021. Validating against reference from Hefei and Xianghe TCCON site demonstrates the high quality of TROPOMI CO and CH4 data over China.
Outbreak of Moroccan Locust in Sardinia (Italy): A Remote Sensing Perspective
Authors: Klein Igor , Cocco Arturo , Uereyen Soner , Mannu Roberto , Floris Ignazio , Oppelt Natascha , Kuenzer Claudia
Published: November 2022

The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.
WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time
Authors: Feng Xu , Nguyen Khuong An , Luo Zhiyuan
Published: November 2022

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research.
A Hybrid Method for Vibration-Based Bridge Damage Detection
Authors: Gonen Semih , Erduran Emrah
Published: November 2022

Damage detection algorithms employing the conventional acceleration measurements and the associated modal features may underperform due to the limited number of sensors used in the monitoring and the smoothing effect of spline functions used to increase the spatial resolution. The effectiveness of such algorithms could be increased if a more accurate estimate of mode shapes were provided. This study presents a hybrid structural health monitoring method for vibration-based damage detection of bridge-type structures. The proposed method is based on the fusion of data from conventional accelerometers and computer vision-based measurements. The most commonly used mode shape-based damage measures, namely, the mode shape curvature method, the modal strain energy method, and the modal flexibility method, are used for damage detection. The accuracy of these parameters used together with the conventional sparse sensor setups and the proposed hybrid approach is investigated in numerical case studies, with damage scenarios simulated on a simply-supported bridge. The simulations involve measuring the acceleration response of the bridge to ambient vibrations and train crossings and then processing the data to determine the modal frequencies and mode shapes. The efficiency and accuracy of the proposed hybrid health monitoring methodology are demonstrated in case studies involving scenarios in which conventional acceleration measurements fail to detect and locate damage. The robustness of the proposed method against various levels of noise is shown as well. In the studies considered, damage as small as 10% decrease in flexural stiffness of the bridge and localized in less than 1% of the span-length of the bridge is reliably detected even with very high levels of measurement noise. Finally, a modified modal flexibility damage parameter is derived and used to alleviate the shortcomings of the modal flexibility damage parameter.
Improved Understanding of Typhoon-Induced Immediate Chlorophyll-A Response Using Advanced Himawari Imager (AHI) Onboard Himawari-8
Authors: Lin Jia-Yi , Ho Hua , Zheng Zhe-Wen
Published: November 2022

The biological response triggered by a tropical cyclone (TC) passage has attracted much attention due to its possible impacts on regional oceanic, ecological environment, and regional climate balance. However, the detailed progress of TC-induced chlorophyll-a (Chl-a) responses (TICRs) remains unclear due to the inherent limitation of observations in ocean color with polar-orbiting satellites as used in previous studies. The appearance of the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite opens the opportunity of correcting all our understanding of TICRs due to its hyper temporal image acquisition capability. In this study, the more real relationship between Chl-a response and TC is further clarified. Results show an essentially different reacting progress of TICRs given by AHI/Himawari-8. It shows a much quicker response relative to previous understanding. Chl-a concentrations reached the highest value on the first day under the severe influences of typhoons. The averaged Chl-a response (0–3 days behind TC passage) observed by AHI is approximately three (2.95) times stronger than that observed by the Moderate Resolution Imaging Spectrometer onboard the National Aeronautics and Space Administration Terra/Aqua satellites. The spatial characteristics of TICRs by AHI show marked differences. Overall, the rapid and strong response sheds new light on the role of TICRs in influencing the regional oceanic environment, marine ecosystem, and local climate. Whole new estimations for the impacts of TICRs on the aforementioned issues are needed urgently.
Channel Evolution Triggered by Large Flash Flood at an Earthquake-Affected Catchment
Authors: Jin Wen , Wang Hao , Zhang Guotao , Liu Dingzhu , Wang Jiao
Published: November 2022

Earthquakes–induced landslides generally provide abundant loose materials at hillslopes, possibly triggering morphological reshaping processes at river channels and riverbeds during the large flash flood hydrograph and bringing huge risk downstream. Therefore, in a Wenchuan earthquake-affected catchment, the collected hydro-meteorological data and high-precision small Unmanned Aerial Vehicle (sUAV) data were used to quantitatively analyze channel evolution by a large flash flood event on 25 and 26 June 2018. It was found that the stable riverbed structure formed by the armour layer appeared in the tenth year after the Wenchuan earthquake. In a confined channel, the layer can protect the channel and resist the drastic change after the flash flood event with only a small bed elevation from 0.2 m to 2 m. Without the protection of the armour, the change could reach 6 m in the unconfined channel. Meanwhile, more materials with a deposition volume of about 7450 m3 from tributaries were generally taken to the main channel, and more intense erosion with a volume of 105 m3 mostly occurred downstream of tributaries. It was noted that, in the cross-section, the increased channel width could lead to a significant change with the large volume of 35 m3. Additionally, a conceptual diagram of the generalized channel response to large flash floods was provided during multi-stage periods after the Wenchuan earthquake. It determined the rebalance processes of channel evolution in the tenth year after the earthquake. This study will contribute to understanding the post-earthquake long-term channel evolutions and could provide decision-makers of assessing the mitigation strategies for higher-magnitude flood disasters triggered by channel change in earthquake-affected watersheds.
How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production
Authors: Huang Xiaojuan , Lin Shangrong , Li Xiangqian , Ma Mingguo , Wu Chaoyang , Yuan Wenping
Published: November 2022

Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks.
Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain
Authors: Fernández-Alonso José María , Llorens Rafael , Sobrino José Antonio , Ruiz-González Ana Daría , Alvarez-González Juan Gabriel , Vega José Antonio , Fernández Cristina
Published: November 2022

The characterization of aboveground biomass is important in forest management planning, with various objectives ranging from prevention of forest fires to restoration of burned areas, especially in fire-prone regions such as NW Spain. Although remotely sensed data have often been used to assess the recovery of standing aboveground biomass after perturbations, the data have seldom been validated in the field, and different shrub fractions have not been modelled. The main objective of the present study was to assess different vegetation parameters (cover, height, standing AGB and their fractions) in field plots established in five areas affected by wildfires between 2009 and 2016 by using Sentinel-2 spectral indices and LiDAR metrics. For this purpose, 22 sampling plots were established in 2019, and vegetation variables were measured by a combination of non-destructive measurement (cover and height) and destructive sampling (total biomass and fine samples of live and dead fractions of biomass).The structural characterization of gorse shrublands was addressed, and models of shrub cover—height, total biomass, and biomass by fraction and physiological condition—were constructed, with adjusted coefficients of determination ranging from 0.6 to 0.9. The addition of LiDAR data to optical remote sensing images improved the models. Further research should be conducted to calibrate the models in other vegetation communities.
Assessing the Optimal Stage-Cam Target for Continuous Water Level Monitoring in Ephemeral Streams: Experimental Evidence
Authors: Tauro Flavia , Noto Simone , Botter Gianluca , Grimaldi Salvatore
Published: November 2022

Recently, increased attention has been devoted to intermittent and ephemeral streams (IRES) due to the recognition of their importance for ecology, hydrology, and biogeochemistry. However, IRES dynamics still demand further research, and traditional monitoring approaches present several limitations in continuously and accurately capturing river network expansion/contraction. Optical-based approaches have shown promise in noninvasively estimating the water level in intermittent streams: a simple setup made up of a wildlife camera and a reference white pole led to estimations within 2cm of accuracy in severe hydrometeorological conditions. In this work, we investigate whether the shortcomings imposed by adverse illumination can be partially mitigated by modifying this simple stage-cam setup. Namely, we estimate the image-based water level by using both the pole and a larger white bar. Further, we compare such results to those obtained with larger bars painted in the red, green, and blue primary colors. Our findings show that using larger white bars also increases reflections and, therefore, the accuracy in the estimation of the water level is not necessarily enhanced. Likewise, experimenting with colored bars does not significantly improve image-based estimations of the stage. Therefore, this work confirms that a simple stage-cam setup may be sufficient to monitor IRES dynamics, suggesting that future efforts may be rather focused on including filters and polarizers in the camera as well as on improving the performance of the image processing algorithm.
Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China
Authors: He Yifei , Zhang Yaonan
Published: November 2022

Generating an unbiased inventory of mass movements is challenging, particularly in a large region such as China. However, due to the enormous threat to human life and property caused by the increasing number of mass movements, it is imperative to develop a reliable nationwide mass movement susceptibility model to identify mass movement-prone regions and formulate appropriate disaster prevention strategies. In recent years, the mixed-effects models have shown their unique advantages in dealing with the biased mass movement inventory, yet there are no relevant studies to compare different mixed-effects models. This research compared three mixed-effects models to explore the most plausible and robust susceptibility mapping model, considering the inherently heterogeneously complete mass movement information. Based on a preliminary data analysis, eight critical factors influencing mass movements were selected as basis predictors: the slope, aspect, profile curvature, plan curvature, road density, river density, soil moisture, and lithology. Two additional factors, namely, the land use and geological environment division, representing the inventory bias were selected as random intercepts. Subsequently, three mixed-effects models—Statistical-based generalized linear mixed-effects model (GLMM), generalized additive mixed-effects model (GAMM), and machine learning-based tree-boosted mixed-effects model (TBMM)—were adopted. These models were used to evaluate the susceptibility of three distinct types of mass movements (i.e., 28,814 debris flows, 54,586 rockfalls and 108,432 landslides), respectively. The results were compared both from quantitative and qualitative perspectives. The results showed that TBMM performed best in all three cases with AUROCs (Area Under the Receiver Operating Characteristic curve) of cross-validation, spatial cross-validation, and predictions on simulated highly biased inventory, all exceeding 0.8. In addition, the spatial prediction patterns of TBMM were more in line with the natural geomorphological underlying process, indicating that TBMM can better reduce the impact of inventory bias than GLMM and GAMM. Finally, factor contribution analysis showed the key role of topographic factors in predicting the occurrence of mass movements, followed by road density and soil moisture. This study contributes to assessing China’s overall mass movement susceptibility situation and assisting policymakers in master planning for risk mitigation. Further, it demonstrates the tremendous potential of TBMM for mass movement susceptibility assessment, despite inherent biases in the inventory.
Vision-Based Structural Modal Identification Using Hybrid Motion Magnification
Authors: Zhang Dashan , Zhu Andong , Hou Wenhui , Liu Lu , Wang Yuwei
Published: November 2022

As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.
Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review
Authors: Yang Bin , Yang Shuang , Lv Zhihan , Wang Faming , Olofsson Thomas
Published: November 2022

Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification.
Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review
Authors: Sun Pengpeng , Sun Chenghao , Wang Runmin , Zhao Xiangmo
Published: November 2022

Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.
A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection
Authors: Sheikhi Saeid , Kostakos Panos
Published: November 2022

Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional datasets that simulate real-world network data increase the complexity and processing time of IDS system training and testing, while irrelevant features waste resources and reduce the detection rate. In this paper, a new intrusion detection model is presented which uses a genetic algorithm (GA) for feature selection and optimization algorithms for gradient descent. First, the GA-based method is used to select a set of highly correlated features from the NSL-KDD dataset that can significantly improve the detection ability of the proposed model. A Back-Propagation Neural Network (BPNN) is then trained using the HPSOGWO method, a hybrid combination of the Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Finally, the hybrid HPSOGWO-BPNN algorithm is used to solve binary and multi-class classification problems on the NSL-KDD dataset. The experimental outcomes demonstrate that the proposed model achieves better performance than other techniques in terms of accuracy, with a lower error rate and better ability to detect different types of attacks.
RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System
Authors: Padhi Bharat Kumar , Chakravarty Sujata , Naik Bighnaraj , Pattanayak Radha Mohan , Das Himansu
Published: November 2022

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model.
A Methodology for Abstracting the Physical Layer of Direct V2X Communications Technologies
Authors: Wu Zhuofei , Bartoletti Stefania , Martinez Vincent , Bazzi Alessandro
Published: November 2022

Recent advancements in vehicle-to-everything (V2X) communications have greatly increased the flexibility of the physical (PHY) and medium access control (MAC) layers. This increases the complexity when investigating the system from a network perspective to evaluate the performance of the supported applications. Such flexibility, in fact, needs to be taken into account through a cross-layer approach, which might lead to challenging evaluation processes. As an accurate simulation of the signals appears unfeasible, a typical solution is to rely on simple models for incorporating the PHY layer of the supported technologies based on off-line measurements or accurate link-level simulations. Such data are, however, limited to a subset of possible configurations, and extending them to others is costly when not even impossible. The goal of this paper is to develop a new approach for modeling the PHY layer of V2X communications that can be extended to a wide range of configurations without leading to extensive measurement or simulation campaigns at the link layer. In particular, given a scenario and starting from results in terms of the packet error rate (PER) vs. signal-to-interference-plus-noise ratio (SINR) related to a subset of possible configurations, we first approximated the curves with step functions characterized by a given SINR threshold, and we then derived one parameter, called implementation loss, that was used to obtain the SINR threshold and evaluate the network performance under any configuration in the same scenario. The proposed methodology, leading to a good trade-off among the complexity, generality, and accuracy of the performance evaluation process, was validated through extensive simulations with both IEEE 802.11p and LTE-V2X sidelink technologies in various scenarios. The results first show that the curves can be effectively approximated by using an SINR threshold, with a value corresponding to 0.5 PER, and then demonstrate that the network-level outputs derived from the proposed approach are very close to those obtained with complete curves, despite not being restricted to a few possible configurations.
SF-Partition-Based Clustering and Relaying Scheme for Resolving Near–Far Unfairness in IoT Multihop LoRa Networks
Authors: Mugerwa Dick , Nam Youngju , Choi Hyunseok , Shin Yongje , Lee Euisin
Published: November 2022

Long range (LoRa) is one of the most successful low-power wide-area networking technologies because it is ideally suited for long-distance, low-bit rate, and low-power communications in the unlicensed sub-GHz spectrum utilized for Internet of things (IoT) networks. The effectiveness of LoRa depends on the link budget (i.e., spreading factor (SF), bandwidth (BW), and transmission power (TX)). Due to the near–far effect, the allocation of a link budget to LoRa devices (LDs) in large coverage regions is unfair between them depending on their distance to the GW. Thus, more transmission opportunities are given to some LDs to the detriment of other LD’s opportunities. Numerous studies have been conducted to address the prevalent near–far fairness problem. Due to the absence of a tractable analytical model for fairness in the LoRa network, however, it is still difficult to solve this problem completely. Thus, we propose an SF-partition-based clustering and relaying (SFPCR) scheme to achieve enormous LD connectivity with fairness in IoT multihop LoRa networks. For the SF partition, the SFPCR scheme determines the suitable partitioning threshold point for bridging packet delivery success probability gaps between SF regions, namely, the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions to the GW, the HSFZ constructs a density-based subspace clustering that generates clusters of arbitrary shape for adjacent LDs and selects cluster headers by using a binary score representation. To support reliable data transmissions to the GW by multihop communications, the LSFZ offers a relay LD selection that ideally chooses the best relay LD to extend uplink transmissions from LDs in the HSFZ. Through simulations, we show that the proposed SFPCR scheme exhibits the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%, and it conserves the energy of LDs compared with the existing schemes.
Cultural Competence in Healthcare and Healthcare Education
Authors: Constantinou Costas S. , Andreou Panayiota , Nikitara Monica , Papageorgiou Alexia
Published: November 2022

Cultural competence in healthcare has been defined in many ways; however, it generally refers to knowledge of social and cultural factors that influence illness and related behaviour, and actions taken to provide the best of quality care considering each patient’s background [...]
Economic Impact on Health and Well-Being: Comparative Study of Israeli and Japanese University “Help” Profession Students
Authors: Isralowitz Richard , Yehudai Mor , Sugawara Daichi , Masuyama Akihiro , Romem Porat Shai-li , Dagan Adi , Reznik Alexander
Published: November 2022

Background: Deteriorating economic conditions caused by rising inflation and living expenses can have negative consequences for university students. This comparative study examined Israeli and Japanese “help” profession (e.g., medicine, nursing, social work, and psychology) students’ fear of such conditions and its impact on their health and well-being. Methods: Data were collected from a cross-sectional sample of 848 university students from Israel and Japan (78.9% female, 20.4% male, and 0.7% other) during a 3-month period of economic decline in 2022. Reliable data-collection instruments and SPSS (version 25) were used for the study. Results: Overall, Japanese students evidenced a higher level of economic well-being than their Israeli counterparts. This finding may have been a result of the lower inflation and living costs in Japan. However, most survey respondents evidenced a fear of deteriorating economic conditions that was significantly associated with psycho-emotional behavior, including increased burnout, substance use, unhealthy food intake, weight gain, and resilience regardless of gender and religiosity. Conclusions: The study findings showed the impact of deteriorating economic conditions on the health and well-being of “help” profession students. These results are preliminary; however, they do serve as an early warning of the key challenges that may need to be considered and addressed for prevention and intervention purposes. Further research should be conducted in other countries and over different time periods to substantiate present findings.
Queer Positionality and Researching University Lad Culture
Authors: Stenson Annis Elizabeth
Published: November 2022

This paper reflects on my experiences as a queer researcher investigating the relationship between university lad culture and gender-related violence. Gender-related violence is analysed as a useful conceptual tool for considering lad culture, owing to the relationship between lad culture and sexual violence, LGBT-phobia and the privileging of white, young, heterosexual men within lad culture. Using reflections from my doctoral case study research, in which I collected data from self-identified ‘lads’ (5 in-depth interviews), I will consider the challenges and benefits of my researcher position in relation the research methodology. Then, using a re-analysis of interviews, I will argue that my researcher position led to certain presentations of lad culture from my participants. Self-Identified Lad (SIL) participants presented themselves as distant from lad culture, showed queerness/hid homophobia within lad culture and were willing to discuss sexual violence. While the case study yielded only a small sample of SILs, a benefit of my researcher position is that this project was the first to conduct interviews with LGB lads and one female lad. My queer feminist position has therefore produced a unique insight into lads who identify with lad culture but discursively position themselves as fringe members. This contributes to theorisations of a laddish continuum, and allows us to consider why some self-identified lads are on the fringes, and what this tells us about lad culture.