bluuebunny/crossref_metadata_2025 huggingface dataset:
Antifungal Activity of Chitosan/Poly(Ethylene Oxide) Blend Electrospun Polymeric Fiber Mat Doped with Metallic Silver Nanoparticles
Authors: Murillo Leire , Rivero Pedro J. , Sandúa Xabier , Pérez Gumer , Palacio José F. , Rodríguez Rafael J.
Published: September 2023

In this work, the implementation of advanced functional coatings based on the combination of two compatible nanofabrication techniques such as electrospinning and dip-coating technology have been successfully obtained for the design of antifungal surfaces. In a first step, uniform and beadless electrospun nanofibers of both polyethylene oxide (PEO) and polyethylene (PEO)/chitosan (CS) blend samples have been obtained. In a second step, the dip-coating process has been gradually performed in order to ensure an adequate distribution of silver nanoparticles (AgNPs) within the electrospun polymeric matrix (PEO/CS/AgNPs) by using a chemical reduction synthetic process, denoted as in situ synthesis (ISS). Scanning electron microscopy (SEM) has been used to evaluate the surface morphology of the samples, showing an evolution in average fiber diameter from 157 ± 43 nm (PEO), 124 ± 36 nm (PEO/CS) and 330 ± 106 nm (PEO/CS/AgNPs). Atomic force microscopy (AFM) has been used to evaluate the roughness profile of the samples, indicating that the ISS process induced a smooth roughness surface because a change in the average roughness Ra from 84.5 nm (PEO/CS) up to 38.9 nm (PEO/CS/AgNPs) was observed. The presence of AgNPs within the electrospun fiber mat has been corroborated by UV-Vis spectroscopy thanks to their characteristic optical properties (orange film coloration) associated to the Localized Surface Plasmon Resonance (LSPR) phenomenon by showing an intense absorption band in the visible region at 436 nm. Energy dispersive X-ray (EDX) profile also indicates the existence of a peak located at 3 keV associated to silver. In addition, after doping the electrospun nanofibers with AgNPs, an important change in the wettability with an intrinsic hydrophobic behavior was observed by showing an evolution in the water contact angle value from 23.4° ± 1.3 (PEO/CS) up to 97.7° ± 5.3 (PEO/CS/AgNPs). The evaluation of the antifungal activity of the nanofibrous mats against Pleurotus ostreatus clearly indicates that the presence of AgNPs in the outer surface of the nanofibers produced an important enhancement in the inhibition zone during mycelium growth as well as a better antifungal efficacy after a longer exposure time. Finally, these fabricated electrospun nanofibrous membranes can offer a wide range of potential uses in fields as diverse as biomedicine (antimicrobial against human or plant pathogen fungi) or even in the design of innovative packaging materials for food preservation.
Vinylene-Linked Emissive Covalent Organic Frameworks for White-Light-Emitting Diodes
Authors: Li Yan , Wu Xiaohan , Zhang Jinyi , Han Congcong , Cao Mengmeng , Li Xiangrong , Wan Jieqiong
Published: September 2023

Covalent organic frameworks (COFs) have gained considerable attention due to their highly conjugated π-skeletons, rendering them promising candidates for the design of light-emitting materials. In this study, we present two vinylene-linked COFs, namely, VL-COF-1 and VL-COF-2, which were synthesized through the Knoevenagel condensation of 2,4,6-trimethyl-1,3,5-triazine with terephthalaldehyde or 4,4′-biphenyldicarboxaldehyde. Both VL-COF-1 and VL-COF-2 exhibited excellent chemical and thermal stability. The presence of vinylene linkages between the constituent building blocks in these COFs resulted in broad excitation and emission properties. Remarkably, the designed VL-COFs demonstrated bright emission, fast fluorescence decay, and high stability, making them highly attractive for optoelectronic applications. To assess the potential of these VL-COFs in practical devices, we fabricated white-light-emitting diodes (WLEDs) coated with VL-COF-1 and VL-COF-2. Notably, the WLEDs coated with VL-COF-1 achieved high-quality white light emission, closely approximating standard white light. The promising performance of VL-COF-coated WLEDs suggests the feasibility of utilizing COF materials for stable and efficient lighting applications.
Numerical Simulation of Three-Dimensional Free Surface Flows Using the K–BKZ–PSM Integral Constitutive Equation
Authors: Bertoco Juliana , Castelo Antonio , Ferrás Luís L. , Fernandes Célio
Published: September 2023

This work introduces a novel numerical method designed to address three-dimensional unsteady free surface flows incorporating integral viscoelastic constitutive equations, specifically the K–BKZ–PSM (Kaye–Bernstein, Kearsley, Zapas–Papanastasiou, Scriven, Macosko) model. The new proposed methodology employs a second-order finite difference approach along with the deformation fields method to solve the integral constitutive equation and the marker particle method (known as marker-and-cell) to accurately capture the evolution of the fluid’s free surface. The newly developed numerical method has proven its effectiveness in handling complex fluid flow scenarios, including confined flows and extrudate swell simulations of Boger fluids. Furthermore, a new semi-analytical solution for velocity and stress fields is derived, considering fully developed flows of a K–BKZ–PSM fluid in a pipe.
Flow Visualisation and Evaluation Studies on Metalworking Fluid Applications in Manufacturing Processes—Methods and Results
Authors: Fritsching Udo , Buss Lizoel , Tonn Teresa , Schumski Lukas , Gakovi Jurgen , Hatscher Johnson David , Sölter Jens , Avila Kerstin , Karpuschewski Bernhard , Gerken Julian Frederic , Wolf Tobias , Biermann Dirk , Menze Christian , Möhring Hans-Christian , Tchoupe Elio , Heidemanns Lukas , Herrig Tim , Klink Andreas , Nabbout Kaissar , Sommerfeld Martin , Luther Fabian , Schaarschmidt Ingo , Schubert Andreas , Richter Markus
Published: September 2023

Metalworking operations rely on the successful application of metalworking fluids (MWFs) for effective and efficient operation. Processes such as grinding or drilling often require the use of MWFs for cooling, lubrication, and chip removal. Electrochemical machining processes require electrolyte flow to operate. However, in those machining operations, a fundamental understanding of the mode of action of MWF is lacking due to the unknown flow dynamics and its interaction with the material removal during the process. Important information on the behaviour of MWFs during machining can be obtained from specific experimental flow visualisation studies. In this paper, promising flow visualisation analysis techniques applied to exemplary machining processes (grinding, sawing, drilling, and electrochemical machining) are presented and discussed. Shadowgraph imaging and flow measurements, e.g., particle image velocimetry, allow the identification of typical flow and MWF operating regimes in the different machining processes. Based on the identification of these regimes, efficient machining parameters and MWF applications can be derived. In addition, detailed experimental analyses of MWFs provide essential data for the input and validation of model development and numerical simulations within the Priority Programme SPP 2231 FluSimPro.
Synergies between Lean and Industry 4.0 for Enhanced Maintenance Management in Sustainable Operations: A Model Proposal
Authors: Mendes David , Gaspar Pedro D. , Charrua-Santos Fernando , Navas Helena
Published: September 2023

Companies actively seek innovative tools and methodologies to enhance operations and meet customer demands. Maintenance plays a crucial role in achieving such objectives. This study identifies existing models that combine Lean Philosophy and Industry 4.0 principles to enhance decision-making and activities related to maintenance management. A comprehensive literature review on key concepts of Lean Philosophy and Industry 4.0, as well as an in-depth analysis of existing models that integrate these principles, is performed. An innovative model based on the synergies between Lean Philosophy and Industry 4.0, named the Maintenance Management in Sustainable Operations (MMSO) model, is proposed. A pilot test of the application of the MMSO model on a conveyor belt led to an operational time increase from 82.3% to 87.7%, indicating a notable 6.6% improvement. The MMSO model significantly enhanced maintenance management, facilitating the collection, processing, and visualization of data via internet-connected devices. Through this integration, various benefits are achieved, including improved flexibility, efficiency, and effectiveness in addressing market needs. This study highlights the value of integrating Lean Philosophy and Industry 4.0 principles to improve maintenance management practices. The proposed MMSO model effectively leverages these principles, fostering agility, optimized resource utilization, heightened productivity and quality, and reduced energy consumption. The model not only serves as a tool for operational optimization and customer demand enhancement but also aligns with sustainability principles within the energy transition. Its successful application in the pilot test phase further reinforces its potential as a reliable approach for maintenance management and sustainable operations in both production and decision-making processes.
Catalytic Hydrogenation of Nitrate over Immobilized Nanocatalysts in a Multi-Phase Continuous Reaction System: System Performance, Characterization and Optimization
Authors: Santos Ana Sofia G. G. , Restivo João , Orge Carla Alexandra , Pereira Manuel Fernando R. , Soares Olívia Salomé G. P.
Published: September 2023

Nitrate catalytic reduction in a continuous system was studied in the presence of Pd-Cu macrostructured catalysts synthesized through a novel washcoating methodology of the pre-formed bimetallic powder catalyst. The present work aims to understand the behavior of the macrostructured bimetallic catalyst in the presence of different reaction conditions in order to achieve the design of an optimized facility that can produce the best catalytic results: maximum NO3− conversion with enhanced N2 selectivity. The residence time of the inlet solution and the catalyst concentration in the reactor proved to be the parameters that most influenced the conversion and selectivity due to the important role that these parameters play in the hydrodynamic conditions of the reactor. A higher loading of catalyst and lower inlet flow rates allow promoting a higher contact time between the three phases that participate in the reaction (G-L-S). The most efficient reaction conditions (three pieces of the macrostructured catalyst, liquid flow rate of 10 mL min−1, and a total gas flow rate of 200 Ncm3 min−1 (1:1 H2:CO2)) allowed obtaining an NO3− conversion of 51% with a corresponding N2 selectivity of 23%. Also, the conversion results strongly depended on the total gas flow rate used during the reaction since this assists the mixing between the three phases and promotes a greater contact that will contribute to enhanced catalytic results.
The Influence of Oxidizing and Non-Oxidizing Biocides on Enzymatic and Microbial Activity in Sugarcane Processing
Authors: Terrell Evan , Qi Yunci , Bruni Gillian O. , Heck Emily
Published: September 2023

Processing aids are utilized during raw sugar manufacturing at sugarcane processing facilities to mitigate unwanted contamination from microorganisms and their associated exopolysaccharides (EPS). Microorganisms in processing facilities contribute to sugar losses through sucrose inversion and consumption, with many bacteria strains subsequently producing dextran and fructan EPS that can cause downstream issues related to viscosity and crystallization. Similar issues also result from the presence of unwanted starches from plant material in cane juices. Processing aids include biocides for bacterial inhibition, and enzymes (e.g., dextranase, amylase) to break down polysaccharides in juices. However, oxidizing biocide processing aids (e.g., sodium hypochlorite) may inhibit enzymatic processing aid activity. In this study, biocides (sodium hypochlorite, carbamate, and hop extract) and enzymes (dextranase and amylase) were simultaneously added to sugarcane juice to measure residual enzymatic activity for dextranase and amylase. The same biocides were also tested to estimate minimum inhibitory concentrations against bacterial strains isolated from Louisiana sugarcane processing facilities. These experiments provide evidence to suggest that sodium hypochlorite may interfere with enzymatic processing aid activity, with lesser/limited enzymatic inhibition from carbamates and hop extracts. Biocide susceptibility assays suggest that sodium hypochlorite has limited effectiveness against tested bacterial strains. Hop extract biocide was only effective against Gram-positive Leuconostoc while carbamate biocide showed more broad-spectrum activity against all tested strains.
A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use
Authors: Kyberd Peter , Popa Alexandru Florin , Cojean Théo
Published: September 2023

Gaze-tracking, where the point of regard of a subject is mapped onto the image of the scene the subject sees, can be employed to study the visual attention of the users of prosthetic hands. It can show whether the user pays greater attention to the actions of their prosthetic hand as they use it to perform manipulation tasks, compared with the general population. Conventional analysis of the video data requires a human operator to identify the key areas of interest in every frame of the video data. Computer vision techniques can assist with this process, but fully automatic systems require large training sets. Prosthetic investigations tend to be limited in numbers. However, if the assessment task is well-controlled, it is possible to make a much simpler system that uses the initial input from an operator to identify the areas of interest and then the computer tracks the objects throughout the task. The tool described here employs colour separation and edge detection on images of the visual field to identify the objects to be tracked. To simplify the computer’s task further, this test uses the Southampton Hand Assessment Procedure (SHAP) to define the activity spatially and temporarily, reducing the search space for the computer. The work reported here concerns the development of a software tool capable of identifying and tracking the points of regard and areas of interest throughout an activity with minimum human operator input. Gaze was successfully tracked for fourteen unimpaired subjects and was compared with the gaze of four users of myoelectric hands. The SHAP cutting task is described and the differences in attention observed with a greater number of shorter fixations by the prosthesis users compared to unimpaired subjects. There was less looking ahead to the next phase of the task by the prosthesis users.
Improving the Spatial Prediction of Sand Content in Forest Soils Using a Multivariate Geostatistical Analysis of LiDAR and Hyperspectral Data
Authors: Castrignanò Annamaria , Buttafuoco Gabriele , Conforti Massimo , Maesano Mauro , Moresi Federico Valerio , Mugnozza Giuseppe Scarascia
Published: September 2023

Soil sand particles play a crucial role in soil erosion because they are more susceptible to being detached and transported by erosive forces than silt and clay particles. Therefore, in soil erosion assessment and mitigation, it is crucial to model and predict soil sand particles at unsampled locations using appropriate methods. The study was aimed to evaluate the ability of a multivariate approach based on non-stationary geostatistics to merge LiDAR and visible-near infrared (Vis-NIR) diffuse reflectance data with laboratory analyses to produce high-resolution maps of soil sand content. Remotely sensed, high-resolution LiDAR-derived topographic attributes can be used as auxiliary variables to estimate soil textural particle-size fractions. The proposed approach was compared with the commonly used univariate approach of ordinary kriging to evaluate the contribution of auxiliary variables. Soil samples (0–0.20 m depth) were collected at 135 locations within a 139 ha forest catchment with granitic parent material and subordinately alluvial deposits, where soils classified as Typic Xerumbrepts and Ultic Haploxeralf crop out. A number of linear trend models coupled with different auxiliary variables were compared. The best model for predicting sand content was the one with elevation derived from LIDAR data as the only auxiliary variable. Although the improvement in estimation over the univariate model was rather marginal, the proposed approach proved very flexible and scalable to include any type of auxiliary variable. The application of LiDAR data is expected to expand as it allows the high-resolution prediction of soil properties, generally insufficiently sampled, at different spatial scales.
Deep Spatial Graph Convolution Network with Adaptive Spectral Aggregated Residuals for Multispectral Point Cloud Classification
Authors: Wang Qingwang , Zhang Zifeng , Chen Xueqian , Wang Zhifeng , Song Jian , Shen Tao
Published: September 2023

Over an extended period, considerable research has focused on elaborated mapping in navigation systems. Multispectral point clouds containing both spatial and spectral information play a crucial role in remote sensing by enabling more accurate land cover classification and the creation of more accurate maps. However, existing graph-based methods often overlook the individual characteristics and information patterns in these graphs, leading to a convoluted pattern of information aggregation and a failure to fully exploit the spatial–spectral information to classify multispectral point clouds. To address these limitations, this paper proposes a deep spatial graph convolution network with adaptive spectral aggregated residuals (DSGCN-ASR). Specifically, the proposed DSGCN-ASR employs spatial graphs for deep convolution, using spectral graph aggregated information as residuals. This method effectively overcomes the limitations of shallow networks in capturing the nonlinear characteristics of multispectral point clouds. Furthermore, the incorporation of adaptive residual weights enhances the use of spatial–spectral information, resulting in improved overall model performance. Experimental validation was conducted on two datasets containing real scenes, comparing the proposed DSGCN-ASR with several state-of-the-art graph-based methods. The results demonstrate that DSGCN-ASR better uses the spatial–spectral information and produces superior classification results. This study provides new insights and ideas for the joint use of spatial and spectral information in the context of multispectral point clouds.
Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020
Authors: Zhang Ji , Zhou Xu , Yang Shengtian , Ao Yang
Published: September 2023

The widespread distribution of karst landforms has led to a shortage of water resources in Southwest China. Understanding the spatiotemporal variations in and driving factors of evapotranspiration (ET) in this area is crucial for understanding and predicting severe water resource shortage. This study conducted trend analysis using meteorological data from 2003 to 2020 as well as remote sensing products such as Penman–Monteith–Leuning equation version 2 (PML-V2) ET. The factors influencing the spatial distribution pattern of average ET were identified using a geographical detector. Partial correlation analysis was performed to characterize the relationships between ET and the factors governing its variations, determined using the random forest model. The results demonstrated the following: (1) The average ET decreased with increasing latitude and altitude, primarily affected by the landform type in terms of longitude and displaying “W”-shaped fluctuations. Overall, the annual ET exhibited a significant (p < 0.05) increasing trend, with 72.63% of its area under the increasing trend. (2) The results of the geographic detector indicated sunshine duration as the strongest explanatory factor of the spatial distribution of ET, followed by enhanced vegetation index (EVI), landform type, precipitation, elevation, slope, and aspect. Instead of an individual factor, the interplay between multiple factors more considerably influenced the spatial distribution pattern of ET. (3) The EVI exhibited an overall increasing trend, with a significant increase over 73.59% of the study area and a positive correlation with ET. Thus, the increase in EVI had the strongest impact on ET in the study area, which was further confirmed by the results of the random forest model for 42.92% of the study area. Thus, the present findings clarify the spatiotemporal variations in and driving factors of ET in Southwest China and can serve as a benchmark for policies aiming to develop and manage water resources in this region.
Impact of a Hyperspectral Satellite Cross-Calibration Radiometer’s Spatial and Noise Characteristics on Cross-Calibration
Authors: Ryan Robert E. , Pagnutti Mary , Huggins Max , Burch Kara , Sitton David , Manriquez Kimberly , Ryan Hannah
Published: September 2023

The satellite cross-calibration radiometer (SCR) is a conceptual on-orbit hyperspectral imaging radiometer that transfers the radiometric calibration from a “gold-standard” reference instrument such as the Landsat 8/9 Operational Land Imager (OLI) to other civil, international, or commercial “client” multispectral satellite systems via near-simultaneous cross-calibration acquisitions. The spectral resolution, spectral range, spatial resolution, and signal-to-noise ratio (SNR) all significantly impact the complexity and cost of hyperspectral SCRs, so it is important to understand their effect on cross-calibration quality. This paper discusses the results of a trade study to quantify the effects of varying ground sample distance (GSD), number of independent samples, and instrument/scene noise on cross-calibration gain uncertainties. The trade study used a simulated SCR cross-calibration with near-simultaneous nadir overpasses (SNOs) of the Landsat 8 OLI acting as the reference instrument and the DLR Earth Sensing Imaging Spectrometer (DESIS) acting as a surrogate SCR hyperspectral instrument. Results demonstrate that cross-calibration uncertainty is only minimally affected by spatial resolution and SNR, which may allow SCR instruments to be developed at a lower cost.
A Deep Learning Network for Individual Tree Segmentation in UAV Images with a Coupled CSPNet and Attention Mechanism
Authors: Lv Lujin , Li Xuejian , Mao Fangjie , Zhou Lv , Xuan Jie , Zhao Yinyin , Yu Jiacong , Song Meixuan , Huang Lei , Du Huaqiang
Published: September 2023

Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting individual trees in complicated urban forest landscapes, as well as poor mask segmentation quality. This study proposes a novel Mask-CSP-attention-coupled network (MCAN) based on the Mask R-CNN algorithm. MCAN uses the Cross Stage Partial Net (CSPNet) framework with the Sigmoid Linear Unit (SiLU) activation function in the backbone network to form a new Cross Stage Partial Residual Net (CSPResNet) and employs a convolutional block attention module (CBAM) mechanism to the feature pyramid network (FPN) for feature fusion and multiscale segmentation to further improve the feature extraction ability of the model, enhance its detail information detection ability, and improve its individual tree detection accuracy. In this study, aerial photography of the study area was conducted by UAVs, and the acquired images were used to produce a dataset for training and validation. The method was compared with the Mask Region-based Convolutional Neural Network (Mask R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Only Look Once v5 (YOLOv5) on the test set. In addition, four scenes—namely, a dense forest distribution, building forest intersection, street trees, and active plaza vegetation—were set up, and the improved segmentation network was used to perform individual tree segmentation on these scenes to test the large-scale segmentation ability of the model. MCAN’s average precision (AP) value for individual tree identification is 92.40%, which is 3.7%, 3.84%, and 12.53% better than that of Mask R-CNN, Faster R-CNN, and YOLOv5, respectively. In comparison to Mask R-CNN, the segmentation AP value is 97.70%, an increase of 8.9%. The segmentation network’s precision for the four scenes in multi-scene segmentation ranges from 95.55% to 92.33%, showing that the proposed network performs high-precision segmentation in many contexts.
Remote Sensing Classification of Offshore Seaweed Aquaculture Farms on Sample Dataset Amplification and Semantic Segmentation Model
Authors: Zhu Hongchun , Lu Zhiwei , Zhang Chao , Yang Yanrui , Zhu Guocan , Zhang Yining , Liu Haiying
Published: September 2023

Satellite remote sensing provides an effective technical means for the precise extraction of information on aquacultural areas, which is of great significance in realizing the scientific supervision of the aquaculture industry. Existing optical remote sensing methods for the extraction of aquacultural area information mostly focus on the use of image spatial features and research on classification methods of single aquaculture patterns. Accordingly, the comprehensive utilization of a combination of spectral information and deep learning automatic recognition technology in the feature expression and discriminant extraction of aquaculture areas needs to be further explored. In this study, using Sentinel-2 remote sensing images, a method for the accurate extraction of different algae aquaculture zones combined with spectral information and deep learning technology was proposed for the characteristics of small samples, multidimensions, and complex water components in marine aquacultural areas. First, the feature expression ability of the aquaculture area target was enhanced through the calculation of the normalized difference aquaculture water index (NDAWI). Second, on this basis, the improved deep convolution generative adversarial network (DCGAN) algorithm was used to amplify the samples and create the NDAWI dataset. Finally, three semantic segmentation methods (UNet, DeepLabv3, and SegNet) were used to design models for classifying the algal aquaculture zones based on the sample amplified time series dataset and comprehensively compare the accuracy of the model classifications for achieving accurate extraction of different algal aquaculture information within the seawater aquaculture zones. The results show that the improved DCGAN amplification exhibited a better effect than the generative adversarial networks (GANs) and DCGAN under the indexes of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The UNet classification model constructed on the basis of the improved DCGAN-amplified NDAWI dataset achieved better classification results (Lvshunkou: OA = 94.56%, kappa = 0.905; Jinzhou: OA = 94.68%, kappa = 0.913). The algorithmic model in this study provides a new method for the fine classification of marine aquaculture area information under small sample conditions.
Factors of Subsidence in Katy, Texas, USA
Authors: Tirmizi Osman , Khan Shuhab D.
Published: September 2023

Coastal communities are susceptible to the damaging effects of land subsidence caused by both natural and anthropogenic processes. The Greater Houston area, situated along the Gulf Coast of Texas, has experienced some of the highest rates of subsidence in the United States. Previous work has extensively analyzed the role of groundwater levels and oil and gas extraction in land subsidence of the Greater Houston area, but has failed to adequately incorporate other significant contributing factors. In this research, we aim to fill that information gap by analyzing the individual effects of subsidence from multiple different processes including groundwater and hydrocarbon extraction rates with the addition of population growth, total annual precipitation, and total developed area in terms of impervious surfaces. We perform a full resolution InSAR analysis of the Katy area using Sentinel-1 data from 2017 to 2022 and compare contributors of subsidence to vertical displacement rates calculated by GNSS stations through a generalized linear regression analysis. The InSAR results show up to 1.4 cm/yr of subsidence in multiple areas of Katy, and the generalized linear regression results suggest that population growth and total developed area are two of the highest contributors to subsidence in the area.
Assessment of Variability and Attribution of Drought Based on GRACE in China from Three Perspectives: Water Storage Component, Climate Change, Water Balance
Authors: Wu Rong , Zhang Chengyuan , Li Yuli , Zhu Chenrui , Lu Liang , Cui Chenfeng , Zhang Zhitao , Wang Shuo , Chu Jiangdong , Li Yongxiang
Published: September 2023

Understanding how drought is impacted by both natural and human influences is crucial to the sustainable utilization and protection of water resources. We established a drought severity index (DSI) based on the terrestrial water storage anomaly (TWSA) derived from the GRACE satellite to detect drought characteristics and trends over ten major river basins in China from 2002 to 2017. The influence of natural factors (terrestrial water storage components, precipitation, evapotranspiration, runoff, NDVI, and teleconnection factors (ENSO, PDO, NAO, and AO)) and a human factor (LULC) on drought were investigated and quantified from the perspective of water storage components based on the Theil–Sen trend and Mann–Kendall test method, the perspective of climate change based on cross wavelet transforms, and the perspective of water balance based on Random Forest. The results indicated that (1) almost all humid and arid basins experienced major drought periods during 2002–2006 and 2014–2017, respectively. The southern IRB and central YZRB regions exhibited notable declines in DSI trends, while the majority of the HLRB, IRB, LRB, YRB, HRB, and SWRB experienced significant increases in DSI trends; (2) abnormal groundwater decreases were the main cause of drought triggered by insufficient terrestrial water storage in most basins; (3) ENSO was the strongest teleconnection factor in most humid basins, and NAO, PDO, and AO were the strongest teleconnection factors in the arid basins and PRB. Most significant resonance cycles lasted 12–64 months in 2005–2014; and (4) the influence of an anthropogenic driver (LULC) has become as important as, or more important than, natural factors (runoff and teleconnection factors) on hydrological drought.
An Efficient Spectrum Reconstruction Algorithm for Non-Uniformly Sampled Signals and Its Application in Terahertz SAR
Authors: Zhang Guohua , Li Chao , Wang Zeyu , Hu Jianmin , Zheng Shen , Liu Xiaojun , Fang Guangyou
Published: September 2023

An efficient spectrum reconstruction algorithm based on the Tikhonov regularization for terahertz (THz) synthetic aperture radar (SAR) azimuth non-uniform sampling is proposed in this article. The high bandwidth, high azimuth resolution, and high frame rate characteristics of THz SAR contribute to its wide application prospects in both military and civilian remote sensing fields. However, the higher azimuth sampling rate also leads to the more severe non-uniform sampling issues of THz SAR. Traditional methods based on the hardware adjustment of pulse repetition frequency (PRF) and simple interpolation for azimuth resampling struggle to meet the higher imaging quality requirements. The back projection algorithm (BPA) can accurately focus non-uniformly sampled data but requires significant computational resources. The algorithm proposed in this paper, which can reconstruct the wavenumber spectrum of SAR azimuth non-uniformly sampled signals, transforms the spectrum reconstruction problem into a linear equation system and solves it using Tikhonov regularization, thereby exhibiting higher computational efficiency compared to BPA. Furthermore, the proposed algorithm is derived from precise theoretical formulations and controls the solution error by utilizing a regularization parameter, leading to a superior imaging quality compared to the azimuth resampling algorithm. In this paper, an accurate spectrum reconstruction formula of non-uniform sampling signals with a finite length is derived, the influence of noise error on the solution is analyzed, and the THz SAR azimuth non-uniform sampling signals are processed from the wavenumber domain. Finally, simulation and experimental results verify the effectiveness of the proposed algorithm.
Posttraumatic Stress and Somatic Symptoms in Firefighters: A Mediation Analysis of the Impact of Potentially Traumatic Events
Authors: Becker Joana Proença , Paixão Rui , Correia-Santos Patrícia , Quartilho Manuel João
Published: September 2023

Firefighters are considered a high-risk group for developing stress-related psychopathologies, such as post-traumatic stress disorder (PTSD), due to repeated exposure to potentially traumatic events (PTE). Studies have indicated that PTE is also associated with the development of Somatic Symptom Disorder (SSD). However, the occurrence of this condition among firefighters is an underexplored topic. This study aimed to assess the relationships between PTE and PTSD symptoms, and the relationship between PTE and SSD symptoms. Furthermore, we aimed to assess the role of SSD in the relationship between PTE and PTSD within Portuguese firefighters who battled the 2017 violent forest fires. To this end, a sample of 116 firefighters (75% male) completed self-report measures of posttraumatic symptoms, somatic symptoms, and other psychopathological symptoms. Contrary to several literature, in the present study, neither PTSD nor SSD symptoms seem to be correlated with any sociodemographic characteristics. However, the exposure to PTE is positively correlated with both PTSD and SSD symptoms. Furthermore, SSD seems to act as a mediator in the relationship between PTE and PTSD.
Workers’ Satisfaction during the COVID-19 Pandemic in Central and Eastern Europe
Authors: Fialová Kamila
Published: September 2023

This article analyses the determinants of worker satisfaction in Central and Eastern European countries, focusing on the effects of the COVID-19 pandemic. For this purpose, the latest European Social Survey data were utilised in a multilevel framework, covering 5681 workers from eight countries. The results suggest that both the general stringency of policies aimed at containing the spread of the COVID-19 contagion and the resulting disruptions to individuals’ working lives significantly affected the well-being of workers. Workers’ satisfaction with their jobs was negatively affected mainly through decreases in work income, but the impact on their overall life satisfaction was more complex. While income reductions and workplace relocation negatively impacted life satisfaction, the opportunity and possible necessity to stay home more positively affected the life satisfaction of workers. Nevertheless, the overall stringency of governmental policies related to COVID-19 significantly reduced workers’ life satisfaction.
Research on the Factors Influencing CO2 Emission Reduction in High-Energy-Consumption Industries under Carbon Peak
Authors: Zhang Hongxing , Li Shuanbao
Published: September 2023

In the context of reaching peak carbon emissions, it is crucial to develop carbon reduction strategies for high-energy-consuming industries as part of a broader societal transition from dependence on high-pollution energy sources to low-pollution alternatives. This study focuses on carbon emission reduction in the non-ferrous metal industry, which is known for its significant energy consumption. It employs the Logarithmic Mean Divisia Index (LMDI) model to conduct empirical analyses from three perspectives: carbon emission decomposition, regionalization analysis, and carbon emission prediction. The objective is to explore the carbon emission characteristics of high-energy-consuming industries in China and provide theoretical support for future policies aimed at reducing carbon emissions in these industries. The findings reveal that the economic scale of the non-ferrous metal industry has a positive correlation with carbon emissions, while carbon emission coefficients exhibit a negative correlation. Moreover, in the prediction scenarios considered, the increase in carbon emissions resulting from the economic-scale factor accounted for 75.28%, 87.46%, and 65.21% respectively, indicating that it has the most significant influence among all factors analyzed. The study further demonstrates that under stable and active emission reduction scenarios, the future potential for carbon dioxide emission reduction in the non-ferrous metal industry is estimated to reach 858.47 million tons and 1384.65 million tons, respectively. These figures represent twice and three times the emissions recorded in 2021. By analyzing the factors influencing emission reduction, targeted regulations can be implemented to develop practical and effective strategies for reducing carbon emissions in the industry. From the analysis conducted, it can be deduced that high-energy-consuming industries, particularly the non-ferrous metal industry, exhibit relatively high levels of carbon emissions. Consequently, it is imperative to implement proactive measures to reduce these emissions. Additionally, the industry’s carbon emissions are heavily influenced by changes in economic scale due to its high dependence on it. This highlights the importance of considering economic factors when devising strategies to mitigate carbon emissions. Furthermore, the potential for improvement in the non-ferrous metal industry’s energy structure and carbon emission coefficients is limited. Simply relying on technological innovation alone may not suffice to achieve significant emission reduction goals. Therefore, it becomes crucial for the government to develop tailored emission reduction targets and policies based on the industry’s specific circumstances to attain optimal results.
Organic Matter of the Wufeng–Longmaxi Formation Shales Using Scanning Electron Microscopy
Authors: Wang Jing , Ye Fawang , Lash Gary G. , Xi Zhaodong
Published: September 2023

Fine-grained organic matter (OM) particles are commonly widely dispersed in shale deposits. However, carrying out investigations of pores hosted by OM particles and the nature of grain interactions in OM particles and associated detrital grains using optical microscopy is difficult at best. Scanning electron microscopy (SEM) is much better suited for characterizing the microstructure of dispersed OM particles and has found wide application in the study of unconventional oil and gas systems. Scanning electron microscopy was used to define the types of OM contained in marine shale deposits of the Wufeng and Longmaxi Formations spanning the Ordovician–Silurian transition in South China. Of particular interest was the development of OM-hosted pores and the identification of the factors that controlled their formation. The dominant OM type contained in the studied deposits is pyrobitumen, with subordinate graptolitic OM and sparse OM of unknown origin. Pyrobitumen is present in four forms, including pore fillings among authigenic quartz grains, within framboidal pyrite, and between authigenic clay grains and massive material. Diagenetic alteration has given rise to OM pores of differing morphology, size, and time of formation. Common small, equisized circular or oval OM pores are most developed and appear to have formed in association with the generation of hydrocarbons. Shale deposits containing abundant pyrobitumen filling interparticle pores among authigenic quartz crystals display robust reservoir and fracturing capacity. A sedimentary environment appears to have been the main factor affecting the type of OM and the nature of its association with detrital and authigenic minerals. Results of this study demonstrate that a sedimentary environment is a primary requisite for the formation of highly prospective/high-yielding hydrocarbon shale reservoir deposits.
The Correlation between Urban Form and Carbon Emissions: A Bibliometric and Literature Review
Authors: Zhu Yanyi , Hu Youpei
Published: September 2023

Urban carbon emissions contribute significantly to global warming, but various factors impact these emissions. This study focuses on the correlation between urban form and carbon emissions. Urban form is an entity that can be directly manipulated and optimized by disciplines such as architecture, urban design, and urban planning. The improvement of urban form, particularly at the meso–micro scale, is relatively rapid and affordable compared to other carbon-related factors, such as macro-industry or energy structure. Therefore, conducting a study on the correlation between urban form and carbon emissions is crucial, and the findings will provide direct scientific support for low-carbon city planning. The paper combines bibliometric analysis with a literature review. First, we explore research hotspots and trends using bibliometric analysis. Second, we organize the literature review based on the main research components, methods, and findings in this field. Finally, we propose a framework and direction for future research. It was found that (1) numerous study methodologies are currently being used to investigate the direct and indirect impacts of urban form on carbon emissions, with Chinese scholars’ research progressing rapidly; (2) the primary focus of the study is on the carbon emissions related to residents’ consumption, and there are still issues with inconsistent measurement approaches; (3) there is more research conducted on the macro-scale of cities but not enough on the meso–micro scale. Future research must focus more on meso–microscale analysis, quantifying the key influences and pathways of urban form on carbon emissions. Additionally, it is crucial to establish a comprehensive research framework that can serve as a guide for more effective urban development aimed at reducing carbon emissions.
Towards a Paradigm Shift on Mapping Muddy Waters with Sentinel-2 Using Machine Learning
Authors: Psychalas Christos , Vlachos Konstantinos , Moumtzidou Anastasia , Gialampoukidis Ilias , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: September 2023

The quality of drinking water is a critical factor for public health and the environment. Inland drinking water reservoirs are essential sources of freshwater supply for many communities around the world. However, these reservoirs are susceptible to various forms of contamination, including the presence of muddy water, which can pose significant challenges for water treatment facilities and lead to serious health risks for consumers. In addition, such reservoirs are also used for recreational purposes which supports the local economy. In this work, we show as a proof-of-concept that muddy water mapping can be accomplished with machine learning-based semantic segmentation constituting an extra source of sediment-laden water information. Among others, such an approach can solve issues including (i) the presence/absence, frequency and spatial extent of pollutants (ii) generalization and expansion to unknown reservoirs (assuming a curated global dataset) (iii) indications about the presence of other pollutants since it acts as their proxy. Our train/test approach is based on 13 Sentinel-2 (S-2) scenes from inland/coastal waters around Europe while treating the data as tabular. Atmospheric corrections are applied and compared based on spectral signatures. Muddy water and non-muddy water samples are taken according to expert knowledge, S-2 scene classification layer, and a combination of normalized difference indices (NDTI and MNDWI) and are evaluated based on their spectral signature statistics. Finally, a Random Forest model is trained, fine-tuned and evaluated using standard classification metrics. The experiments have shown that muddy water can be detected with high enough discrimination capacity, opening the door to more advanced image-based machine learning techniques.
State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter
Authors: Fan Ruolan , Li Gang , Wu Yanan
Published: September 2023

As a new type of transportation, the distributed drive electric vehicle is regarded as the main development direction of electric vehicles in the future. Due to the advantages of the independently controllable driving torque of each wheel, it provides more favorable conditions for vehicle active safety control. Acquiring accurate and real-time parameters such as vehicle speed and side slip angle is a prerequisite for vehicle active safety control. Therefore, relying on the National Natural Science Foundation of China, this paper takes the distributed drive electric vehicle in the form of four-wheel independent drive and steering as the research object. Taking the measurement data of low-cost vehicle sensors as input and adaptive Kalman filtering as theoretical support, the sub-filter of federal Kalman filtering adds a fuzzy controller on the basis of volumetric Kalman filtering, and designs the vehicle driving state estimation algorithm to realize the accurate estimation of driving state information. Finally, the typical experimental conditions are selected, and the designed algorithm is verified by the co-simulation of MATLAB/Simulink and CarSim. At the same time, the algorithm is further verified based on the driving simulator hardware-in-the-loop experimental platform. The results show that the designed estimation algorithm has good effects in terms of accuracy, stability, and real-time performance.
Assessing the Optimal Contributions of Renewables and Carbon Capture and Storage toward Carbon Neutrality by 2050
Authors: Nguyen Dinh Hoa , Chapman Andrew , Tsuji Takeshi
Published: September 2023

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the appropriate combination of technologies which will enable the required reductions in such a way that they are appealing to investors. Around the globe, solar and wind power lead in terms of renewable energy deployment, while carbon capture and storage (CCS) is scaling up toward making a significant contribution to deep carbon cuts. Using Japan as a case study nation, this research proposes a linear optimization modeling approach to identify the potential contributions of renewables and CCS toward maximizing carbon reduction and identifying their economic merits over time. Results identify that the combination of these three technologies could enable a carbon dioxide emission reduction of between 55 and 67 percent in the energy sector by 2050 depending on resilience levels and CCS deployment regimes. Further reductions are likely to emerge with increased carbon pricing over time. The findings provide insights for energy system design, energy policy making and investment in carbon reducing technologies which underpin significant carbon reductions, while identifying potential regional social co-benefits.