bluuebunny/crossref_metadata_2025 huggingface dataset:
Emerging Trends in Pathophysiology: Insights from the 9th International Congress of the International Society for Pathophysiology (ISP 2023)
Authors: Kubyshkin Anatolii , Bolevich Sergey , Churilov Leonid , Jakovljevic Vladimir , Kovalenko Evgeniia , Korovin Aleksandr
Published: March 2024

This article provides a summary of the 9th International Congress of the International Society for Pathophysiology (ISP 2023) which took place in Belgrade, Serbia, from 4 to 6 July 2023. It describes the main trends and the most promising areas of research in current pathophysiology, including investigations of new pathogenic pathways, and the identification of cellular and molecular mechanisms, target molecules, genetic mechanisms, and new therapeutic strategies. The present article also highlights the research conducted by leading scientific teams and national pathophysiological societies from various countries that adds to our understanding of the pathogenesis of diseases and pathological processes.
Curcumin/Carrier Coprecipitation by Supercritical Antisolvent Route
Authors: Mottola Stefania , De Marco Iolanda
Published: March 2024

In this work, polyvinylpyrrolidone (PVP)- and β-cyclodextrin (β-CD)-based composite powders containing curcumin (CURC) were obtained through the supercritical antisolvent (SAS) technique. Pressure, total concentration of CURC/carrier in dimethylsulfoxide, and CURC/carrier ratio effects on the morphology and size of the precipitated powders were investigated. Using PVP as the carrier, spherical particles with a mean diameter of 1.72 μm were obtained at 12.0 MPa, 20 mg/mL, and a CURC/PVP molar ratio equal to 1/2 mol/mol; using β-CD as the carrier, the optimal operating conditions were 9.0 MPa and 200 mg/mL; well-defined micrometric particles with mean diameters equal to 2.98 and 3.69 μm were obtained at molar ratios of 1/2 and 1/1 mol/mol, respectively. FT-IR spectra of CURC/ β-CD inclusion complexes and coprecipitated CURC/PVP powders revealed the presence of some peaks of the active compounds. The stoichiometry of the complexes evaluated through the Job method revealed that β-CD formed inclusion complexes with CURC at a molar ratio equal to 1/1. Dissolution profiles revealed that in comparison with the curve of the pure ingredient, the SAS-processed powders obtained using both PVP and β-CD have an improved release rate.
Evaluation of Monolayer and Bilayer Buccal Films Containing Metoclopramide
Authors: Grilc Blaž , Planinšek Odon
Published: March 2024

The objective of this study was to develop buccal film formulations containing metoclopramide hydrochloride monohydrate (MCP) with and without a backing layer and to evaluate their release properties and physiochemical stability. The crystallization of MCP in the polymer matrix was monitored with image analysis techniques for rapid and scalable observation. The results showed that the addition of a protective layer and its thickness significantly affected the release rate and crystallization behavior of MCP in the formulations. The crystallization of MCP increased over time, and certain formulations showed higher susceptibility to crystallization. To understand the factors affecting the crystallization of MCP, the relationship between the viscosity and pH of the casting solution was examined, but no significant correlation was found. A significant correlation was observed between the plasticizer concentration and the physical state of MCP. Through a systematic Design of Experiment (DOE) approach, an optimal formulation was devised, successfully preventing crystallization of the active ingredient. However, enhancing the overall chemical stability of the formulated product remains a challenge.
Real-Time Spatial-Division Multiplexing Transmission with Commercial 400 Gb/s Transponders
Authors: Gao Yuyang , Li Juhao , Tang Yu , Shen Lei , Zhou Xian , Zhao Chunxu , Shen Shikui , Zhang Lei , Tang Xiongyan , Chen Zhangyuan
Published: March 2024

As single-mode-fiber transmission systems are reaching their capacity limits, spatial-division multiplexing (SDM) techniques have been investigated to increase the per-fiber capacity. However, the compatibility with current single-mode transponders severely hinders the near-term deployment of SDM systems. In this paper, we experimentally propose two real-time SDM transmission schemes using commercial single-mode 400 G dual-polarized 16 quadrature amplitude modulation equipment. In the first experiment, 60 km weakly coupled single-mode 7-core fiber with a pair of fan-in and fan-out devices are adopted. In the second experiment, the fiber link consists of 60 km/150 km weakly coupled few-mode fiber (FMF) and low-modal-crosstalk mode multiplexers, in which only non-degenerate LP01 and LP02 modes are utilized. In order to investigate the effect of splice on SDM fiber links, 20-roll, 3 km multicore fibers (MCFs) and FMFs are spliced and tested in the experiments. The bit error rates of all SDM experiments are all below 4.75 × 10−2 forward-error-correction threshold of the 400 G transponders. The experimental results prove that the near-term deployment of SDM systems could be accelerated by utilizing weakly coupled MCFs or non-degenerate modes of weakly coupled FMFs which are compatible with commercial single-mode transponders without any software or hardware modifications.
A Study on Length Traceability and Diffraction Efficiency of Chromium Gratings
Authors: Lei Lihua , Liang Lijie , Liu Liqin , Shen Yaoqiong , Guan Yuqing , Zhang Yujie , Zou Wenzhe , Guo Chuangwei , Fu Yunxia
Published: March 2024

Measurement traceability is a prerequisite for achieving accurate and reliable results as well as technical standardization. The period of Chromium (Cr) gratings fabricated by atomic lithography can be directly traced back to natural constants. Applying the Cr grating to grating interferometry can achieve nanometer measurement traceability. This research aims to analyze the diffraction efficiency characteristics of self-traceable Cr gratings to provide a theoretical basis for the fabrication and application of Cr gratings. In this regard, we establish the theoretical model of the laser beam incident angle and grating diffraction efficiency using the rigorous coupled-wave method. Then, we analyze the influence of the laser beam incident angle on grating diffraction efficiency by simulation, verify the accuracy of the theoretical model, and finally build a measurement system for grating diffraction efficiency. Through experiments, we find that the diffraction efficiency of the grating shows a rapid increase to reach a stable maximum value followed by a decrease, when a laser beam with a wavelength of 405 nm is incident on the surface of a self-traceable grating in Transverse Magnetic (TM) polarization and the incident angle changes within an effective range. The experimental results are consistent with the trend of theoretical calculation results.
Targeting Bacterial Nanocellulose Properties through Tailored Downstream Techniques
Authors: Da Silva Pereira Everton Henrique , Mojicevic Marija , Tas Cuneyt Erdinc , Lanzagorta Garcia Eduardo , Brennan Fournet Margaret
Published: March 2024

Bacterial nanocellulose (BNC) is a biodegradable polysaccharide with unique properties that make it an attractive material for various industrial applications. This study focuses on the strain Komagataeibacter medellinensis ID13488, a strain with the ability to produce high yields of BNC under acidic growth conditions and a promising candidate to use for industrial production of BNC. We conducted a comprehensive investigation into the effects of downstream treatments on the structural and mechanical characteristics of BNC. When compared to alkaline-treated BNC, autoclave-treated BNC exhibited around 78% superior flexibility in average, while it displayed nearly 40% lower stiffness on average. An SEM analysis revealed distinct surface characteristics, indicating differences in cellulose chain compaction. FTIR spectra demonstrated increased hydrogen bonding with prolonged interaction time with alkaline solutions. A thermal analysis showed enhanced thermal stability in alkaline-treated BNC, withstanding temperatures of nearly 300 °C before commencing degradation, compared to autoclaved BNC which starts degradation around 200 °C. These findings provide valuable insights for tailoring BNC properties for specific applications, particularly in industries requiring high purity and specific mechanical characteristics.
Carbon-Based Composites with Biodegradable Matrix for Flexible Paper Electronics
Authors: Szałapak Jerzy , Zdanikowski Bartosz , Kądziela Aleksandra , Lepak-Kuc Sandra , Dybowska-Sarapuk Łucja , Janczak Daniel , Raczyński Tomasz , Jakubowska Małgorzata
Published: March 2024

The authors explore the development of paper-based electronics using carbon-based composites with a biodegradable matrix based on ethyl cellulose and dibasic ester solvent. The main focus is on screen-printing techniques for creating flexible, eco-friendly electronic devices. This research evaluates the printability with the rheological measurements, electrical properties, flexibility, and adhesion of these composites, considering various compositions, including graphene, graphite, and carbon black. The study finds that certain compositions offer sheet resistance below 1 kΩ/sq and good adhesion to paper substrates with just one layer of screen printing, demonstrating the potential for commercial applications, such as single-use electronics, flexible heaters, etc. The study also shows the impact of cyclic bending on the electrical parameters of the prepared layers. This research emphasizes the importance of the biodegradability of the matrix, contributing to the field of sustainable electronics. Overall, this study provides insights into developing environmentally friendly, flexible electronic components, highlighting the role of biodegradable materials in this evolving industry.
Silicon Hybrid EPDM Composite with High Thermal Protection Performance
Authors: Yan Chenyang , Chen Bo , Li Xiangmei , He Jiyu , Zhao Xin , Zhu Yanli , Yang Rongjie
Published: March 2024

The effects of octaphenylsilsesquioxane (OPS), fumed silica, and silica aerogel on the thermal insulation properties of ethylene propylene diene monomer (EPDM) rubber were studied. On this basis, two kinds of fillers with good performances were selected to study the thermal insulation of an EPDM full-formula system. The results show that the addition of fumed silica or silica aerogel had a positive effect on the thermal insulation performance of EPDM rubber and its composite. A 30 wt% silica aerogel can be well dispersed in the EPDM rubber system and with a lower thermal conductivity compared with fumed silica. EPDM composite with 23.4 wt% fumed silica can produce more char residues at 1000 °C than at 500 °C in a burn-through test and formed the compact and porous char at 1000 °C, which had a lowest thermal conductivity. EPDM composite with fumed silica cannot be burned through 1000 °C burning, and comparison with silica aerogel revealed that it achieved the lowest back temperature and had a temperature of 388 °C after 800 s.
Advances in Additive Manufacturing of Polymer-Fused Deposition Modeling on Textiles: From 3D Printing to Innovative 4D Printing—A Review
Authors: Franco Urquiza Edgar Adrian
Published: March 2024

Technological advances and the development of new and advanced materials allow the transition from three-dimensional (3D) printing to the innovation of four-dimensional (4D) printing. 3D printing is the process of precisely creating objects with complex shapes by depositing superimposed layers of material. Current 3D printing technology allows two or more filaments of different polymeric materials to be placed, which, together with the development of intelligent materials that change shape over time or under the action of an external stimulus, allow us to innovate and move toward an emerging area of research, innovative 4D printing technology. 4D printing makes it possible to manufacture actuators and sensors for various technological applications. Its most significant development is currently in the manufacture of intelligent textiles. The potential of 4D printing lies in modular manufacturing, where fabric-printed material interaction enables the creation of bio-inspired and biomimetic devices. The central part of this review summarizes the effect of the primary external stimuli on 4D textile materials, followed by the leading applications. Shape memory polymers attract current and potential opportunities in the textile industry to develop smart clothing for protection against extreme environments, auxiliary prostheses, smart splints or orthoses to assist the muscles in their medical recovery, and comfort devices. In the future, intelligent textiles will perform much more demanding roles, thus envisioning the application fields of 4D printing in the next decade.
Data-Based Modeling, Multi-Objective Optimization and Multi-Criteria Decision Making of a Catalytic Ozonation Process for Degradation of a Colored Effluent
Authors: Nabavi Seyed Reza , Ghahri Saheleh , Rangaiah Gade Pandu
Published: March 2024

In the catalytic ozonation process (COP), the reactions are complex, and it is very difficult to determine the effect of different operating parameters on the degradation rate of pollutants. Data-based modeling tools, such as the multilayer perceptron (MLP) neural network, can be useful in establishing the complex relationship of degradation efficiency with the operating variables. In this work, the COP of acid red 88 (AR88) with Fe3O4 nano catalyst was investigated in a semi-batch reactor and a MLP model was developed to predict the degradation efficiency (%DE) of AR88 in the range of 25 to 96%. The MLP model was trained using 78 experimental data having five input variables, namely, AR88 initial concentration, catalyst concentration, pH, inlet air flow rate and batch time (in the ranges of 150–400 mg L−1, 0.04–0.4 g L−1, 4.5–8.5, 0.5–1.90 mg min−1 and 5–30 min, respectively). Its optimal topology was obtained by changing the number of neurons in the hidden layer, the momentum and the learning rates to 7, 0.075 and 0.025, respectively. A high correlation coefficient (R2 > 0.98) was found between the experimental and predicted values by the MLP model. Simultaneous maximization of %DE and minimization of Fe3O4 concentration was carried out by multi-objective particle swarm optimization (MOPSO) and the Pareto-optimal solutions were successfully obtained. The trade-off was analyzed through multi-criteria decision making, and one Pareto-optimal solution was selected. The developed model and optimal points are useful for treatment of AR88 wastewater.
An Integrated Design Method for Used Product Remanufacturing Process Based on Multi-Objective Optimization Model
Authors: Ke Chao , Chen Yanxiang , Gan Muyang , Liu Yang , Ji Qunjing
Published: March 2024

The design for the remanufacturing process (DFRP) is a key part of remanufacturing, which directly affects the cost, performance, and carbon emission of used product remanufacturing. However, used parts have various failure forms and defects, which make it hard to rapidly generate the remanufacturing process scheme for simultaneously satisfying remanufacturing requirements regarding cost, performance, and carbon emissions. This causes remanufactured products to lose their energy-saving and emission-reduction benefits. To this end, this paper proposes an integrated design method for the used product remanufacturing process based on the multi-objective optimization model. Firstly, an integrated DFRP framework is constructed, including design information acquisition, the virtual model construction of DFRP solutions, and the multi-objective optimization of the remanufacturing process scheme. Then, the design matrix, sensitivity analysis, and least squares are applied to construct the mapping models between performance, carbon emissions, cost, and remanufacturing process parameters. Meanwhile, a DFRP multi-objective optimization model with performance, carbon emission, and cost as the design objectives is established, and a teaching–learning based adaptive optimization algorithm is employed to solve the optimization model to acquire a DFRP solution satisfying the target information. Finally, the feasibility of the method is verified by the DFRP of the turbine blade as an example. The results show that the optimized remanufacturing process parameters reduce carbon emissions by 11.7% and remanufacturing cost by USD 0.052 compared with the original process parameters, and also improve the tensile strength of the turbine blades, which also indicates that the DFPR method can effectively achieve energy saving and emission reduction and ensure the performance of the remanufactured products. This can greatly reduce the carbon emission credits of the large-scale remanufacturing industry and promote the global industry’s sustainable development; meanwhile, this study is useful for remanufacturing companies and provides remanufacturing process design methodology support.
Transfemoral Amputee Stumble Detection through Machine-Learning Classification: Initial Exploration with Three Subjects
Authors: Galey Lucas , Fuentes Olac , Gonzalez Roger V.
Published: March 2024

Objective: To train a machine-learning (ML) algorithm to classify stumbling in transfemoral amputee gait. Methods: Three subjects completed gait trials in which they were induced to stumble via three different means. Several iterations of ML algorithms were developed to ultimately classify whether individual steps were stumbles or normal gait using leave-one-out methodology. Data cleaning and hyperparameter tuning were applied. Results: One hundred thirty individual stumbles were marked and collected during the trials. Single-layer networks including Long-Short Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gradient Recurrent Unit (GRU) were evaluated at 76% accuracy (LSTM and GRU). A four-layer LSTM achieved an 88.7% classic accuracy, with 66.9% step-specific accuracy. Conclusion: This initial trial demonstrated the ML capabilities of the gathered dataset. Though further data collection and exploration would likely improve results, the initial findings demonstrate that three forms of induced stumble can be learned with some accuracy. Significance: Other datasets and studies, such as that of Chereshnev et al. with HuGaDB, demonstrate the cataloging of human gait activities and classifying them for activity prediction. This study suggests that the integration of stumble data with such datasets would allow a knee prosthesis to detect stumbles and adapt to gait activities with some accuracy without depending on state-based recognition.
Role and Function of Receptor Tyrosine Kinases in BRAF Mutant Cancers
Authors: Biersack Bernhard , Tahtamouni Lubna , Höpfner Michael
Published: March 2024

The development of potent BRAF inhibitors has revolutionized the treatment of BRAF mutant cancers, in particular, melanomas. However, BRAF mutant cancers of other entities, e.g., colorectal cancers, display distinctly reduced responses to BRAF inhibitors. In addition, the emergence of cancer resistance to BRAF inhibitor treatment poses a severe problem. The reactivation of MAPK/ERK signaling was identified as an important mode of BRAF inhibitor resistance. Receptor tyrosine kinases (RTKs), which are prominent anticancer drug targets in their own right, play a crucial role in the development of drug resistance to BRAF inhibitors and the reactivation of MAPK/ERK signal transduction, as well as the establishment of bypassing signaling pathways. MAPK reactivation can occur via increased expression of RTKs, altered RTK signaling, and post-translational processes, among others. This review summarizes the influence of pertinent RTKs on BRAF mutant cancers and BRAF inhibitor resistance and outlines possible and proven ways to circumvent BRAF-associated resistance mechanisms.
Recalibrating Christian Ethics at Corinth: Paul’s Use of Jesus the Prototype and Collective Remembrance to Provide Spiritual Guidance on Weaker Brothers and Food Offered to Idols
Authors: Williams H. H. Drake
Published: March 2024

Social identity theory has provided a fresh lens that can be used to look at Paul’s letters. Prototypes provide a helpful means to examine social identity and ethics in communities, as suggested by Warren Carter. In 1 Corinthians, Jesus Christ is presented as a prototype, although the Corinthians did not meet him. Collective memory theory has also provided a means to look at recollections of the person of Jesus recorded in the New Testament. While the number of recollections of Jesus that his recipients had is still open to question, this study finds Bauckham’s approach to the memory of Jesus in Paul to be the most sustainable. Studies by Dale Alison and Richard Burridge provide a general picture of ideas in the Synoptic tradition. When the fruits of prototype studies are combined with the collective memory of Jesus, it provides fresh insight into Paul’s commandment to imitate Jesus Christ, which was issued in 1 Cor 11:1. The fruits of these combined methods reveal the influence of the life of Jesus in the commands to look after the weak brother, abstain from idol feasts, and to do everything to God’s glory. Through the recollection of the lifestyle of Jesus, Paul recalibrates the Corinthian behavior so that it agrees with the prototype.
What Matters for Comovements among Gold, Bitcoin, CO2, Commodities, VIX and International Stock Markets during the Health, Political and Bank Crises?
Authors: Frikha Wajdi , Béjaoui Azza , Bariviera Aurelio F. , Jeribi Ahmed
Published: March 2024

This paper analyzes the connectedness between gold, wheat, and crude oil futures, Bitcoin, carbon emission futures, and international stock markets in the G7, BRICS, and Gulf regions with the outbreak of exogenous and unexpected shocks related to health, banking, and political crises. To this end, we use a wavelet-based method on the returns of different assets during the period 2 January 2019, to 21 April 2023. The empirical findings show that the existence of time-varying linkages between markets is well documented and appears stronger during the COVID-19 pandemic. However, it seems to diminish for some associations with the advent of the Russia-Ukraine War. The empirical results also show that investor risk perceptions measured by the VIX are negatively and substantially linked to stock markets in different regions. Other interesting findings emerge from the connectedness analysis with the outbreak of Silicon Valley bankruptcy. In particular, Bitcoin tends to regain its role as a safe-haven asset against some G7 stock markets during the bank crisis. Such findings can provide valuable insights for investors and policymakers concerning the relationship between different markets during different crises.
Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm
Authors: Li Haibin , Zhang Qi , Pan Mengchun , Chen Dixiang , Liu Zhongyan , Yan Liang , Xu Yujing , Ding Zengquan , Yu Ziqiang , Liu Xu , Wan Ke , Dai Weiji
Published: March 2024

The construction of a high-precision geomagnetic map is a prerequisite for geomagnetic navigation and magnetic target-detection technology. The Kriging interpolation algorithm makes use of the variogram to perform linear unbiased and optimal estimation of unknown sample points. It has strong spatial autocorrelation and is one of the important methods for geomagnetic map construction. However, in a region with a complex geomagnetic field, the sparse geomagnetic survey lines make the ratio of line-spacing resolution to in-line resolution larger, and the survey line direction differs from the geomagnetic trend, which leads to a serious effect of geometric anisotropy and thus, reduces the interpolation accuracy of the geomagnetic maps. Therefore, this paper focuses on the problem of geometric anisotropy in the process of constructing a geomagnetic map with sparse data, analyzes the influence of sparse data on geometric anisotropy, deduces the formula of geometric anisotropy correction, and proposes a modified interpolation algorithm accounting for geometric anisotropy correction of variogram for sparse geomagnetic data. The results of several sets of simulations and measured data show that the proposed method has higher interpolation accuracy than the conventional spherical variogram model in a region where the geomagnetic anomaly gradient changes sharply, which provides an effective way to build a high-precision magnetic map of the complex geomagnetic field under the condition of sparse survey lines.
Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China
Authors: Gan Zhiquan , Guo Shurong , Chen Chunpeng , Zheng Hanjie , Hu Yuekai , Su Hua , Wu Wenting
Published: March 2024

Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal dynamics of tidal flats. However, there is no cost-effective way to map morphological changes on a large spatial scale due to the inaccessibility of the mudflats. In this study, we proposed a pixel-based multi-indices tidal flat mapping algorithm that precisely characterizes 2D/3D morphological changes in tidal flats in northern China using time-series remote sensing data. An overall accuracy of 0.95 in delineating tidal flats to a 2D extent was achieved, with 11,716 verification points. Our results demonstrate that the reduction in sediment discharge from rivers along the coastlines of the Yellow and Bohai Seas has resulted in an overall decline in the area of tidal flats, from 4856.40 km2 to 4778.32 km2. Specifically, 3D analysis showed that significant losses were observed in the mid-to-high-tidal flat zones, while low-elevation tidal flats experienced an increase in area due to the transformations in mid-to-high-tidal flats. Our results indicate that the sediment inputs from rivers and the succession of native vegetation are the primary drivers leading to 2D/3D morphological changes of tidal flats following the cessation of extensive land reclamation in northern China.
Numerical Evaluation of Planetary Radar Backscatter Models for Self-Affine Fractal Surfaces
Authors: Virkki Anne
Published: March 2024

Numerous analytical radar-scattering laws have been published through the past decades to interpret planetary radar observations, such as Hagfors’ law, which has been commonly used for the Moon, and the cosine law, which is commonly used in the shape modeling of asteroids. Many of the laws have not been numerically validated in terms of their interpretation and limitations. This paper evaluates radar-scattering laws for self-affine fractal surfaces using a numerical approach. Traditionally, the autocorrelation function and, more recently, the Hurst exponent, which describes the self-affinity, have been used to quantify the height correlation. Here, hundreds of three-dimensional synthetic surfaces parameterized using a root-mean-square (rms) height and a Hurst exponent were generated, and their backscattering coefficient functions were computed to evaluate their consistency with selected analytical models. The numerical results were also compared to empirical models for roughness and radar-scattering measurements of Hawaii lava flows and found consistent. The Gaussian law performed best at predicting the rms slope regardless of the Hurst exponent. Consistent with the literature, it was found to be the most reliable radar-scattering law for the inverse modeling of the rms slopes and the Fresnel reflection coefficient from the quasi-specular backscattering peak, when homogeneous statistical properties and a ray-optics approach can be assumed. The contribution of multiple scattering in the backscattered power increases as a function of rms slope up to about 20% of the backscattered power at normal incidence when the rms slope angle is 46°.
Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance
Authors: Deng Jiawen , Huang Lijia
Published: March 2024

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.
Combining Cylindrical Voxel and Mask R-CNN for Automatic Detection of Water Leakages in Shield Tunnel Point Clouds
Authors: Chen Qiong , Kang Zhizhong , Cao Zhen , Xie Xiaowei , Guan Bowen , Pan Yuxi , Chang Jia
Published: March 2024

Water leakages can affect the safety and durability of shield tunnels, so rapid and accurate identification and diagnosis are urgently needed. However, current leakage detection methods are mostly based on mobile LiDAR data, making it challenging to detect leakage damage in both mobile and terrestrial LiDAR data simultaneously, and the detection results are not intuitive. Therefore, an integrated cylindrical voxel and Mask R-CNN method for water leakage inspection is presented in this paper. This method includes the following three steps: (1) a 3D cylindrical-voxel data organization structure is constructed to transform the tunnel point cloud from disordered to ordered and achieve the projection of a 3D point cloud to a 2D image; (2) automated leakage segmentation and localization is carried out via Mask R-CNN; (3) the segmentation results of water leakage are mapped back to the 3D point cloud based on a cylindrical-voxel structure of shield tunnel point cloud, achieving the expression of water leakage disease in 3D space. The proposed approach can efficiently detect water leakage and leakage not only in mobile laser point cloud data but also in ground laser point cloud data, especially in processing its curved parts. Additionally, it achieves the visualization of water leakage in shield tunnels in 3D space, making the water leakage results more intuitive. Experimental validation is conducted based on the MLS and TLS point cloud data collected in Nanjing and Suzhou, respectively. Compared with the current commonly used detection method, which combines cylindrical projection and Mask R-CNN, the proposed method can achieve water leakage detection and 3D visualization in different tunnel scenarios, and the accuracy of water leakage detection of the method in this paper has improved by nearly 10%.
A Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images
Authors: Liu Bing , Li Tianhong
Published: March 2024

Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (CODMn). The highest R2s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the CODMn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers.
Assimilation of Hyperspectral Infrared Atmospheric Sounder Data of FengYun-3E Satellite and Assessment of Its Impact on Analyses and Forecasts
Authors: Liu Ruixia , Lu Qifeng , Wu Chunqiang , Ni Zhuoya , Wang Fu
Published: March 2024

HIRAS-II is the hyperspectral detector carried on FengYun-3E which is the world’s first meteorological satellite in dawn–dusk orbit. It fills the observation gaps during the dawn and dusk periods of polar orbit meteorological satellites, enabling a 100% global data coverage and assimilation of polar orbit satellite data within each 6 h window for numerical weather forecasting models. With 3053 vertical detection channels, it provides high-resolution vertical temperature and humidity information, thus playing an important role in improving the forecast skills of the global medium-range weather prediction models. This study assimilated data from 56 CO2 channels of FY-3E HIRAS into the CMA-GFS 4DVAR system. Two sets of experiments, FY3EHIRAS and CTRL, were designed, conducting a one-month cycle assimilation test to evaluate the impact of assimilating FY-3E HIRAS data on CMA-GFS analysis and forecasting. Using the ECMWF reanalysis data ERA5 as a reference, the study demonstrated that after assimilating data from FY-3E HIRAS’s 56 CO2 channels, there was a certain extent of improvement in the temperature field at almost all model levels. The RMSE notably reduced in the southern hemisphere’s temperature analysis field near the surface and at 500 hPa by 3.5% and 2%, respectively. The most significant improvement in the entire temperature analysis field was observed in the tropical region, followed by the southern and then the northern hemisphere. Additionally, there was a reduction in RMSE for the height and wind fields, showing considerable improvement compared to the CTRL experiment. Overall, assimilating the FY-3E HIRAS data led to positive improvements in the forecasting skills for temperature, wind fields, and height fields in both the southern and northern hemispheres. The forecasting effectiveness was slightly lower in the tropical region but displayed an overall neutral-to-positive effect.
Satellite-Based Water Quality Assessment of the Beijing Section of the Grand Canal: Implications for SDG11.4 Evaluation
Authors: Xie Ya , Zhou Qing , Xiao Xiao , Chen Fulong , Huang Yingchun , Kang Jinlong , Wang Shenglei , Zhang Fangfang , Gao Min , Du Yichen , Shen Wei , Li Junsheng
Published: March 2024

The Beijing-Hangzhou Grand Canal in China became a World Cultural Heritage Site in 2014, and the water quality of this ancient man-made canal has increasingly attracted societal attention. This study focuses on monitoring the water quality of the Beijing section of the Grand Canal (BGC) using remote sensing technology. Analysis of the comprehensive trophic level index (TLI) indicates that the water in the Canal was predominantly light eutrophic from 2016 to 2022. The annual average results of the TLI reveal that the water quality in the Kunming Lake and North Canal of BGC is generally good, characterized by some mesotrophic waters, and others are in light eutrophication. The TLI for the entire BGC water body decreased from 64.7 in 2016 to 60.3 in 2022, indicating an improvement trend in water quality. Notably, the proportion of good water with TLI less than 60 increased from 50% in 2016 to 83% in 2022. This improvement of water quality may be attributed to the reduced use of fertilizers and pesticides and the implementation of various environmental policies by Beijing Municipal government. These findings offer valuable insights for the management and protection of the water resources of the BGC, and further contribute to the evaluation of the United Nations Sustainable Development Goal (SDG) 11.4.
HyPedSim: A Multi-Level Crowd-Simulation Framework—Methodology, Calibration, and Validation
Authors: Dang Huu-Tu , Gaudou Benoit , Verstaevel Nicolas
Published: March 2024

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based model to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data are extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimises the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimising computational performance compared to other models.
Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control
Authors: Hu Yaozeng , Liu Jianze , Wang Zhuang , Zhang Jingming , Liu Jiang
Published: March 2024

In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.
Sons al Balcó: A Comparative Analysis of WASN-Based LAeq Measured Values with Perceptual Questionnaires in Barcelona during the COVID-19 Lockdown
Authors: Bonet-Solà Daniel , Bergadà Pau , Dorca Enric , Martínez-Suquía Carme , Alsina-Pagès Rosa Ma
Published: March 2024

The mobility and activity restrictions imposed in Spain due to the COVID-19 pandemic caused a significant improvement in the urban noise pollution that could be objectively measured in those cities with acoustic sensor networks deployed. This significant change in the urban soundscapes was also perceived by citizens who positively appraised this new acoustic scenario. In this work, authors present a comparative analysis between different noise indices provided by 70 sound sensors deployed in Barcelona, both during and before the lockdown, and the results of a perceptual test conducted in the framework of the project Sons al Balcó during the lockdown, which received more than one hundred contributions in Barcelona alone. The analysis has been performed by clustering the objective and subjective data according to the predominant noise sources in the location of the sensors and differentiating road traffic in heavy, moderate and low-traffic areas. The study brings out strong alignments between a decline in noise indices, acoustic satisfaction improvement and changes in the predominant noise sources, supporting the idea that objective calibrated data can be useful to make a qualitative approximation to the subjective perception of urban soundscapes when further information is not available.
Spectral–Spatial Feature Fusion for Hyperspectral Anomaly Detection
Authors: Liu Shaocong , Li Zhen , Wang Guangyuan , Qiu Xianfei , Liu Tinghao , Cao Jing , Zhang Donghui
Published: March 2024

Hyperspectral anomaly detection is used to recognize unusual patterns or anomalies in hyperspectral data. Currently, many spectral–spatial detection methods have been proposed with a cascaded manner; however, they often neglect the complementary characteristics between the spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral–spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with an entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce the spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets covering ocean and airport scenes prove that the proposed SSIF produces superior detection results over other state-of-the-art detection techniques.
Trends in Digital Twin Framework Architectures for Smart Cities: A Case Study in Smart Mobility
Authors: Faliagka Evanthia , Christopoulou Eleni , Ringas Dimitrios , Politi Tanya , Kostis Nikos , Leonardos Dimitris , Tranoris Christos , Antonopoulos Christos P. , Denazis Spyros , Voros Nikolaos
Published: March 2024

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as “Smart Parking”, “Environmental Behavior Analysis on Traffic Incidents”, and “Emergency Management”, we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity.
Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers
Authors: Pradhan Anuj K. , Lin Brian T. W. , Wege Claudia , Babel Franziska
Published: March 2024

A third of large truck crashes are associated with driver-related factors, especially speeding. This study aimed to examine the impact of behavior-based safety (BBS) programs on speeding. Speeding data were examined from a trucking fleet that had incorporated a BBS program using in-vehicle data recorders (IVDR) and post hoc feedback. Speeding events were examined over 37 weeks in two stages—an initial 4-week period (Stage 1), and the final 30 weeks (Stage 2). In Stage 1, data were collected without any feedback. In Stage 2, a subset of the drivers received feedback. A cluster analysis was performed based on the speeding event rate from Stage 1. The analysis yielded two clusters per group based on risk. The higher-risk cluster contained fewer drivers and showed a greater reduction in speeding with the BBS program, compared to the lower-risk cluster. Both clusters showed significant decreases in speeding across Stage 2. The BBS program was associated with reduced speeding, with a more pronounced reduction for the higher-risk drivers, highlighting the role of BBS programs in trucking and underscoring the importance of driver sub-groups. Targeted safety approaches may be more efficient and yield higher safety benefits than a one-size fits all approach.
A Chemical Safety Assessment of Lyocell-Based Activated Carbon Fiber with a High Surface Area through the Evaluation of HCl Gas Adsorption and Electrochemical Properties
Authors: Kim Jong Gu , Bai Byong Chol
Published: March 2024

This study investigates lyocell-based activated carbon fibers (ACFs) for their suitability in adsorbing and electrochemically detecting toxic HCl gas. ACFs were prepared via steam activation, varying temperature (800–900 °C) and time (40–240 min) to assess their adsorption and sensing capabilities. The adjustment of activation temperature and reaction time aimed to regulate the uniformity of the pore structure and pore size of the active reaction area, as well as the number of reaction sites in the ACFs. Optimal ACFs were achieved at 900 °C for 50 min, exhibiting the highest specific surface area (1403 m2/g) and total pore volume (0.66 cm3/g). Longer reaction times resulted in pore formation and disorder, reducing mechanical strength. The ACFs prepared under optimal conditions demonstrated a rapid increase in resistance during sensor measurement, indicating a significant sensitivity to HCl gas. These findings suggest the potential of ACFs for efficient HCl gas adsorption (1626.20 mg/g) and highlight the importance of activation parameters in tailoring their properties for practical applications.