bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecularly Imprinted Polymeric Nanoparticles as Drug Delivery System for Tenofovir, an Acyclic Nucleoside Phosphonate Antiviral
Authors: Mathieu Thomas , Favetta Patrick , Agrofoglio Luigi A.
Published: July 2024

A molecularly imprinted polymer of Tenofovir (1), an FDA-approved acyclic nucleoside phosphonate with antiviral activity, was synthesized using a non-covalent approach. A pre-polymerization complex was formed between (1) and DMAEMA and in-house synthetic N1-[(2-methacryloyloxy)ethyl] thymine, with EGDMA as a cross-linker in an MeCN/H2O (9:1, 1:1) mixture as a porogen, giving an imprinting factor (IF) of 5.5 at 2.10−5 mol/L. Binding parameters were determined by the Freundlich–Langmuir model, Qmax and Ka, and well as the particle morphology for MIP and NIP. Finally, the release profiles, for MIP and NIP, were obtained at 25 °C and 37 °C, which is body temperature, in a phosphate buffer saline, pH 7.4, mimicking the blood pH value, to determine the potential sustained release of our polymeric materials.
Reinforcing Efficiency of Recycled Carbon Fiber PLA Filament Suitable for Additive Manufacturing
Authors: Tammaro Loredana , Martone Alfonso , Palmieri Barbara , Borriello Carmela , Portofino Sabrina , Iovane Pierpaolo , Cilento Fabrizia , Giordano Michele , Galvagno Sergio
Published: July 2024

The use of 3D printing technology for manufacturing new products based on sustainable materials enables one to take advantage of secondary raw materials derived from recycling. This work investigates the structural performances of 3D printing composite filaments based on polylactic acid (PLA), as a matrix, reinforced by recycled carbon fiber (rCF). Carbon fibers were recovered from industrial scraps by a patented thermal process and used to produce thermoplastic composite filaments for additive manufacturing without any additional treatment and additives. The influence of the recovered carbon fiber (rCF) content on the thermal properties, mechanical properties and microstructure of the composites was studied in the range of 3–20 wt%. The recorded TGA curves exhibited a one-stage weight loss within the temperature range 290–380 °C for all samples and the residual rCF content was in good agreement with the theoretical fiber loading. The Young modulus of the extruded filaments strongly increased below a critical content (5 wt%), while at higher content the improvement was reduced. An increase in the storage modulus of 54% compared to neat PLA 3D printed sample resulted in a printed specimen with a higher rCF content. SEM images highlighted a strong rCF prevailing alignment in the direction of the extrusion flow, creating almost unidirectional reinforcement inside the filament. These findings suggest that homogeneous composite filaments reinforced with well-dispersed recycled CF without additional chemical modification and additives are suitable materials for additive manufacturing. The effect of rCF topological distribution within the material on the mechanical performances has been discussed, highlighting that the isolated fibers could efficiently transfer loads with respect to the percolated 3D network and have been correlated with the microstructure.
Controllable Assemblies of Au NPs/P5A for Enhanced Catalytic Reduction of 4-Nitrophenol
Authors: Liu Zhaona , Li Bing , Zhang Huacheng
Published: July 2024

Efficient catalytic reduction of 4-nitrophenol (4-NP) is one focus of industry and practical engineering, because 4-NP is one of the most important sources of pollution of the ecological environment and human health. Here, coassembled hybrid composites of pillar[5]arene (P5A) and gold nanoparticles (Au NPs) were successfully developed by a one-step synthetic method as a type of water-insoluble catalyst for the reduction of 4-NP. The geometric and topological structures, as well as physiochemical properties of Au NPs/P5A composite catalyst, were fully characterized and analyzed through various tests such as transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), indicating that Au NPs were well dispersed on the surface of the two-dimensional film of assembled P5A. The influence factors of the catalytic reduction of 4-NP were further investigated and discussed, confirming that the content of Au NPs and the concentration of 4-NP were very significant during the catalysis. The catalytic reaction was carried out at the catalyst concentration of 100 mg·L−1 and an initial 4-NP concentration of 90 mg·L−1 under 30 °C. The calculated reaction rate constant was 0.3959 min−1 and the reduction rate of 4-NP was more than 95% in 20 min. In addition, the as-prepared catalyst can maintain a high catalytic efficiency after five cycles. Thus, the easily recyclable composite catalyst with poor aqueous solution can exhibit prospective application to the treatment of 4-NP in water.
On Swami Vivekananda and Caste Prejudice: Ethical Implications of the Experience of Non-Duality
Authors: Long Jeffery D.
Published: July 2024

The well-known modern Hindu reformer and pioneer of Vedānta in the West, Swami Vivekananda (1863–1902), based his ethical vision on mysticism: specifically, on the direct experience of non-duality and the ultimate unity and organic interconnectedness of all beings. This paper will explore the implications of this experientially based ethos for caste prejudice. Caste remains a hot-button issue in India to the present day and was no less so in the time of Swami Vivekananda. This system of social organization is rightly criticized by social justice advocates for the inequities it enshrines and enforces in Indian society. Because it has historically been justified by reference to Hindu textual sources—specifically such Dharma Śāstras as the Manusmṛti—prejudice based on caste, or casteism, has frequently been depicted, especially by critics of Hinduism, as essential or inherent to Hindu traditions. The implication of this identification of caste with Hinduism, and caste with social injustice, is that Hinduism is an intrinsically wicked and unjust religion. Such simplistic equations fail to consider the extent to which caste prejudice has been condemned by authoritative Hindu teachers, not least, by Swami Vivekananda himself. It is thus important to rearticulate Swami Vivekananda’s rejection of caste prejudice—and indeed, of all prejudice—based on Advaita Vedānta both to make the case against such prejudice in today’s world and to address criticisms of Hinduism as inherently or essentially casteist. Finally, it will be noted that Vivekananda’s criticisms of caste anticipate those of a contemporary anti-casteist voice from the Advaita tradition: that of Hindu theologian Anantanand Rambachan, who has also argued against prejudices of various kinds, including caste prejudice, based on Advaita Vedānta.
Validation and Conformity Testing of Sentinel-3 Green Instantaneous FAPAR and Canopy Chlorophyll Content Products
Authors: Camacho Fernando , Martínez-Sánchez Enrique , Brown Luke A. , Morris Harry , Morrone Rosalinda , Williams Owen , Dash Jadunandan , Origo Niall , Sánchez-Zapero Jorge , Boccia Valentina
Published: July 2024

This article presents validation and conformity testing of the Sentinel-3 Ocean Land Colour Instrument (OLCI) green instantaneous fraction of absorbed photosynthetically active radiation (FAPAR) and OLCI terrestrial chlorophyll index (OTCI) canopy chlorophyll content (CCC) products with fiducial reference measurements (FRM) collected in 2018 and 2021 over two sites (Las Tiesas—Barrax, Spain, and Wytham Woods, UK) in the context of the European Space Agency (ESA) Fiducial Reference Measurement for Vegetation (FRM4Veg) initiative. Following metrological principles, an end-to-end uncertainty evaluation framework developed in the project is used to account for the uncertainty of reference data based on a two-stage validation approach. The process involves quantifying uncertainties at the elementary sampling unit (ESU) level and incorporating these uncertainties in the upscaling procedures using orthogonal distance regression (ODR) between FRM and vegetation indices derived from Sentinel-2 data. Uncertainties in the Sentinel-2 data are also accounted for. FRM-based high spatial resolution reference maps and their uncertainties were aggregated to OLCI’s native spatial resolution using its apparent point spread function (PSF). The Sentinel-3 mission requirements, which give an uncertainty of 5% (goal) and 10% (threshold), were considered for conformity testing. GIFAPAR validation results revealed correlations > 0.95, RMSD ~0.1, and a slight negative bias (~−0.06) for both sites. This bias could be partly explained by the differences in the FAPAR definitions between the satellite product and the FRM-based reference. For the OTCI-based CCC, leave-one-out cross-validation demonstrated correlations > 0.8 and RMSDcv ~0.28 g·m−2. Despite the encouraging validation results, conclusive conformity with the strict mission requirements was low, with most cases providing inconclusive results (driven by large uncertainties in the satellite products as well as by the uncertainties in the upscaling approach). It is recommended that mission requirements for bio-geophysical products are reviewed, at least at the threshold level. It is also suggested that the large uncertainties associated with the two-stage validation approach may be avoided by directly comparing with spatially representative FRM.
A Prediction Model of Ionospheric Total Electron Content Based on Grid-Optimized Support Vector Regression
Authors: Yu Qiao , Men Xiaobin , Wang Jian
Published: July 2024

Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters C and g by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters.
Three-Dimensional Deformation Estimation from Multi-Temporal Real-Scene Models for Landslide Monitoring
Authors: Xi Ke , Tao Pengjie , Niu Zhuangqun , Zhu Xiaokun , Duan Yansong , Ke Tao , Zhang Zuxun
Published: July 2024

This study proposes a three-dimensional (3D) deformation estimation framework based on the integration of shape and texture information for real-scene 3D model matching, effectively addressing the issue of deformation assessment in large-scale geological landslide areas. By extracting and merging the texture and shape features of matched points, correspondences between points in multi-temporal real-scene 3D models are established, resolving the difficulties faced by existing methods in achieving robust and high-precision 3D point matching over landslide areas. To ensure the complete coverage of the geological disaster area while enhancing computational efficiency during deformation estimation, a voxel-based thinning method to generate interest points is proposed. The effectiveness of the proposed method is validated through tests on a dataset from the Lijie north hill geological landslide area in Gansu Province, China. Experimental results demonstrate that the proposed method significantly outperforms existing classic and advanced methods in terms of matching accuracy metrics, and the accuracy of our deformation estimates is close to the actual measurements obtained from GNSS stations, with an average error of only 2.2 cm.
Impacts of Climatic Fluctuations and Vegetation Greening on Regional Hydrological Processes: A Case Study in the Xiaoxinganling Mountains–Sanjiang Plain Region, Northeastern China
Authors: Xu Chi , Zhang Zhijie , Fu Zhenghui , Xiong Shenqing , Chen Hao , Zhang Wanchang , Wang Shuhang , Zhang Donghui , Lu Heng , Jiang Xia
Published: July 2024

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management.
A Novel Point Cloud Adaptive Filtering Algorithm for LiDAR SLAM in Forest Environments Based on Guidance Information
Authors: Yang Shuhang , Xing Yanqiu , Wang Dejun , Deng Hangyu
Published: July 2024

To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas.
Landslide Hazard Prediction Based on Small Baseline Subset–Interferometric Synthetic-Aperture Radar Technology Combined with Land-Use Dynamic Change and Hydrological Conditions (Sichuan, China)
Authors: Guo Hongyi , Martínez-Graña A. M.
Published: July 2024

Le’an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town’s terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le’an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le’an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value.
Evaluating the Sustainable Development Science Satellite 1 (SDGSAT-1) Multi-Spectral Data for River Water Mapping: A Comparative Study with Sentinel-2
Authors: Jiang Duomandi , Li Yunmei , Liu Qihang , Huang Chang
Published: July 2024

SDGSAT-1, the first scientific satellite dedicated to advancing the United Nations 2030 Agenda for Sustainable Development, brings renewed vigor and opportunities to water resource monitoring and research. This study evaluates the effectiveness of SDGSAT-1 in extracting water bodies in comparison to Sentinel-2 multi-spectral imager (MSI) data. We applied a confidence thresholding method to delineate river water from land, utilizing the Normalized Differential Water Body Index (NDWI), Normalized Difference Water Index (MNDWI), and Shaded Water Body Index (SWI). It was found that the SWI works best for SDGSAT-1 while the NDWI works best for Sentinel-2. Specifically, the NDWI demonstrates proficiency in delineating a broader spectrum of water bodies and the MNDWI effectively mitigates the impact of shadows, while SDGSAT-1’s SWI extraction of rivers offers high precision, clear outlines, and shadow exclusion. SDGSAT-1’s SWI overall outperforms Sentinel-2’s NDWI in water extraction accuracy (overall accuracy: 90% vs. 91%, Kappa coefficient: 0.771 vs. 0.416, and F1 value: 0.844 vs. 0.651), likely due to its deep blue bands. This study highlights the comprehensive advantages of SDGSAT-1 data in extracting river water bodies, providing a theoretical basis for future research.
An Innovative Device Based on Human-Machine Interface (HMI) for Powered Wheelchair Control for Neurodegenerative Disease: A Proof-of-Concept
Authors: Palumbo Arrigo , Ielpo Nicola , Calabrese Barbara , Garropoli Remo , Gramigna Vera , Ammendolia Antonio , Marotta Nicola
Published: July 2024

In the global context, advancements in technology and science have rendered virtual, augmented, and mixed-reality technologies capable of transforming clinical care and medical environments by offering enhanced features and improved healthcare services. This paper aims to present a mixed reality-based system to control a robotic wheelchair for people with limited mobility. The test group comprised 11 healthy subjects (six male, five female, mean age 35.2 ± 11.7 years). A novel platform that integrates a smart wheelchair and an eye-tracking-enabled head-mounted display was proposed to reduce the cognitive requirements needed for wheelchair movement and control. The approach’s effectiveness was demonstrated by evaluating our system in realistic scenarios. The demonstration of the proposed AR head-mounted display user interface for controlling a smart wheelchair and the results provided in this paper could highlight the potential of the HoloLens 2-based innovative solutions and bring focus to emerging research topics, such as remote control, cognitive rehabilitation, the implementation of patient autonomy with severe disabilities, and telemedicine.
DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition
Authors: Guo Zijie , Guo Jian , Huang Yanan , Zhang Yibo , Ren Hengyi
Published: July 2024

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.
Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage
Authors: Guo Junqi , Xi Wenfei , Yang Zhiquan , Huang Guangcai , Xiao Bo , Jin Tingting , Hong Wenyu , Gui Fuyu , Ma Yijie
Published: July 2024

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR’s monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR’s monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.
Evaluation of the Reliability of the CCM-300 Chlorophyll Content Meter in Measuring Chlorophyll Content for Various Plant Functional Types
Authors: Van Beek Joelie M. , Zheng Ting , Wang Zhihui , Kovach Kyle R. , Townsend Philip A.
Published: July 2024

Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years.
Flexible Strain Sensors Based on Thermoplastic Polyurethane Fabricated by Electrospinning: A Review
Authors: Zhou Zhiyuan , Tang Weirui , Xu Teer , Zhao Wuyang , Zhang Jingjing , Bai Chuanwu
Published: July 2024

Over recent years, thermoplastic polyurethane (TPU) has been widely used as a substrate material for flexible strain sensors due to its remarkable mechanical flexibility and the ease of combining various conductive materials by electrospinning. Many research advances have been made in the preparation of flexible strain sensors with better ductility, higher sensitivity, and wider sensing range by using TPU in combination with various conductive materials through electrospinning. However, there is a lack of reviews that provide a systematic and comprehensive summary and outlook of recent research advances in this area. In this review paper, the working principles of strain sensors and electrospinning technology are initially described. Subsequently, recent advances in strain sensors based on electrospun TPU are tracked and discussed, with a focus on the incorporation of various conductive fillers such as carbonaceous materials, MXene, metallic materials, and conductive polymers. Moreover, the wide range of applications of electrospun TPU flexible strain sensors is thoroughly discussed. Finally, the future prospects and challenges of electrospun TPU flexible strain sensors in various fields are pointed out.
Integrating High-Performance Flexible Wires with Strain Sensors for Wearable Human Motion Detection
Authors: Wu Pucheng , He Hu
Published: July 2024

Flexible electronics have revolutionized the field by overcoming the rigid limitations of traditional devices, offering superior flexibility and adaptability. Conductive ink performance is crucial, directly impacting the stability of flexible electronics. While metal filler-based inks exhibit excellent conductivity, they often lack mechanical stability. To address this challenge, we present a novel conductive ink utilizing a ternary composite filler system: liquid metal and two micron-sized silver morphologies (particles and flakes). We systematically investigated the influence of filler type, mass ratio, and sintering process parameters on the composite ink’s conductivity and mechanical stability. Our results demonstrate that flexible wires fabricated with the liquid metal/micron silver particle/micron silver flake composite filler exhibit remarkable conductivity and exceptional bending stability. Interestingly, increasing the liquid metal content results in a trade-off, compromising conductivity while enhancing mechanical performance. After enduring 5000 bending cycles, the resistance change in wires formulated with a 4:1 mass ratio of micron silver particles to flakes is only half that of wires with a 1:1 ratio. This study further investigates the mechanism governing resistance variations during flexible wire bending. Additionally, we observed a positive correlation between sintering temperature and pressure with the conductivity of flexible wires. The significance of the sintering parameters on conductivity follows a descending order: sintering temperature, sintering pressure, and sintering time. Finally, we demonstrate the practical application of this technology by integrating the composite ink-based flexible wires with conductive polymer-based strain sensors. This combination successfully achieved the detection of human movements, including finger and wrist bending.
Industry Image Classification Based on Stochastic Configuration Networks and Multi-Scale Feature Analysis
Authors: Wang Qinxia , Liu Dandan , Tian Hao , Qin Yongpeng , Zhao Difei
Published: July 2024

For industry image data, this paper proposes an image classification method based on stochastic configuration networks and multi-scale feature extraction. The multi-scale features are extracted from images of different scales using deep 2DSCN, and the hidden features of multiple layers are also connected together to obtain more informational features. The integrated features are fed into SCNs to learn a classifier which improves the recognition rate for different categories. In the experiments, a handwritten digit database and an industry hot-rolled steel strip database are used, and the comparison results demonstrate the proposed method can effectively improve the classification accuracy.
Lane Attribute Classification Based on Fine-Grained Description
Authors: He Zhonghe , Gong Pengfei , Ye Hongcheng , Gan Zizheng
Published: July 2024

As an indispensable part of the vehicle environment perception task, road traffic marking detection plays a vital role in correctly understanding the current traffic situation. However, the existing traffic marking detection algorithms still have some limitations. Taking lane detection as an example, the current detection methods mainly focus on the location information detection of lane lines, and they only judge the overall attribute of each detected lane line instance, thus lacking more fine-grained dynamic detection of lane line attributes. In order to meet the needs of intelligent vehicles for the dynamic attribute detection of lane lines and more perfect road environment information in urban road environment, this paper constructs a fine-grained attribute detection method for lane lines, which uses pixel-level attribute sequence points to describe the complete attribute distribution of lane lines and then matches the detection results of the lane lines. Realizing the attribute judgment of different segment positions of lane instances is called the fine-grained attribute detection of lane lines (Lane-FGA). In addition, in view of the lack of annotation information in the current open-source lane data set, this paper constructs a lane data set with both lane instance information and fine-grained attribute information by combining manual annotation and intelligent annotation. At the same time, a cyclic iterative attribute inference algorithm is designed to solve the difficult problem of lane attribute labeling in areas without visual cues such as occlusion and damage. In the end, the average accuracy of the proposed algorithm reaches 97% on various types of lane attribute detection.
Reconstruction of Optical Properties in Turbid Media: Omitting the Need of the Collimated Transmission for an Integrating Sphere Setup
Authors: Ni Dongqin , Karmann Niklas , Hohmann Martin
Published: July 2024

Currently, the most reliable approach to reconstruct optical properties, namely absorption coefficient, reduced scattering coefficient, scattering coefficient and asymmetry factor, of turbid media is through inverse Monte Carlo simulation. To determine these optical properties, three measurements are required: total transmission, total reflection and collimated transmission. However, the accurate determination of the collimated transmission is very difficult. To overcome the difficulty of measuring the collimated transmission, it is proposed to measure the total transmission and total reflection of the same sample with two different thicknesses instead. To prove this alternative solution, machine learning is used to prove that the repeated measurement for two different thicknesses carries all the necessary information. As a result, all four optical properties can be measured with high accuracy, particularly for interpolation problems where the test data fall within the range of the training data. For extrapolation problems, high accuracy can be achieved in the determination of at least the absorption coefficient, reduced scattering coefficient and scattering coefficient. Hence, these results allow that in the future, an easier and therefore more precise reconstruction of the optical properties is possible, potentially even with inverse Monte Carlo simulations as the current standard.
The Fast and Reliable Detection of Multiple Narrowband FH Signals: A Practical Framework
Authors: Aydin Mutlu , Dalveren Yaser , Kara Ali , Derawi Mohammad
Published: July 2024

Frequency hopping (FH) is a well-known technique that is commonly used in communication systems owing to its many advantages, including its strong anti-jamming capability. In this technique, basically, radio signals are transmitted by switching the carrier between different frequency channels. As a result, the FH signal is not stationary; hence, its spectrum is expected to change over time. Therefore, the task of detection and parameter estimation of FH signals is very challenging in practice. To address this challenge, the study presented in this article proposes a method that detects and estimates the parameters of multiple narrowband FH signals. In the proposed method, first, short-time Fourier transform (STFT) is utilized to analyze FH signals, and a practical binarization process based on thresholding is used to detect FH signals. Then, a new algorithm is proposed to ensure that the center frequencies of the detected signals are successfully separated. Next, another algorithm is proposed to estimate the parameters of the detected signals. After estimating the parameters for the entire spectrum, an approach is used to detect FH signals. Lastly, the hop-clustering process is applied to separate the hops into groups without time overlap. The simulation results show that the proposed method can be an efficient way for the fast and accurate parameter estimation and detection of multiple narrowband FH signals.
Enhanced Indoor Positioning Using RSSI and Time-Distributed Auto Encoder-Gated Recurrent Unit Model
Authors: Wei Zhe , Zhou Zhanpeng , Yu Shuyan , Chen Jialei
Published: July 2024

This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data. Experimental validation demonstrates that the proposed approach achieves a 75.9% improvement in localization accuracy over simple neural network methods and markedly enhances the speed of localization, thereby proving its practical applicability in real-world indoor localization scenarios.
Circular Economy for Sustainability: A Bibliometric Analysis for the Tourism/Hospitality Field
Authors: Şahin Esra , Yiğitoğlu Vedat , Gök Demir Zuhal , Karakaya Çiğdem , Erendağ Sümer Fulya , Güneri Beysun
Published: July 2024

The tourism and hospitality sector is known for its excessive consumption of natural resources, waste generation, and negative environmental impacts. The concept of circular economy (CE) has gained increasing attention in recent years as a strategy to promote sustainability and resource efficiency in various industries, including tourism and hospitality. This bibliometric analysis aims to provide a comprehensive overview of current research trends regarding the adoption of CE principles in the tourism and hospitality sector. Unlike conventional methods, a comprehensive bibliometric analysis combined studies from Web of Science and Scopus databases between 2013 and 2024 using the keywords circular economy, tourism, and hospitality. In this research, which includes 314 articles selected in accordance with the PRISMA protocol, it is seen that CE applications are becoming increasingly important. The research results show that there has been a significant increase in the number of publications since 2015; the most productive countries are Italy, Spain, and China, and topics such as waste management and recycling stand out as motor themes. It is thought that the number of studies on practical applications and niche themes should increase and future research suggestions are presented.
Understanding the Dry-to-Wet Transition of Summer Precipitation over the Three-Rivers Headwater Region: Atmospheric Circulation Mechanisms
Authors: Liu Xuan , Yang Mingxiang , Zhou Feng , Wen Fan , Zhang Xiaotan , Gao Chao , Wang Hejia , Dong Ningpeng
Published: July 2024

Summer precipitation has changed over the Three-Rivers Headwater (TRH) region, which may have an impact on droughts and floods in Asia. This study examines the notable interdecadal variation from dry to wet conditions in summer (June to August) precipitation over the TRH region during the period of 1979–2020. The changes could have been influenced by atmospheric circulations. This study aims to improve our understanding of the interdecadal variation in summer precipitation over the TRH region. Our findings reveal that a zonally oriented teleconnection wave train is generated across the Eurasian mid-to-high latitudes, originating from the North Atlantic and propagating to northern East Asia along the westerly jet. This results in a weakened and northward-shifted westerly jet. Additionally, anticyclonic circulation anomalies over the northern Tibetan Plateau contribute to easterly water-vapor transport anomalies in the region, reducing water-vapor export at the eastern boundary. Concurrently, an anomalous cyclone over the Arabian Sea and an anomalous anticyclone over the Bay of Bengal enhance the influx of oceanic water vapor into the TRH region. The enhanced Walker circulation further augments the equatorial easterly, which in turn strengthens the anomalous anticyclone over the Bay of Bengal. Consequently, these atmospheric changes contribute to the increased summer precipitation over the TRH region, elucidating the mechanisms behind the observed dry-to-wet transition.
Satellite Long-Term Monitoring of Wetland Ecosystem Functioning in Ramsar Sites for Their Sustainable Management
Authors: Demarquet Quentin , Rapinel Sébastien , Arvor Damien , Corgne Samuel , Hubert-Moy Laurence
Published: July 2024

The long-term monitoring of wetland ecosystem functioning is critical because wetlands, which provide multiple services, can be affected by human activities and climate change. The aim of this study was to monitor wetland ecosystem functioning in the long term using the Landsat archive. Four contrasting, Ramsar wetlands were selected in boreal, temperate, arid, and tropical areas. First, the annual sum of the normalized difference vegetation index (NDVI-I) was calculated as an indicator of annual net primary productivity for the period 1984–2021 using the continuous change detection and classification (CCDC) algorithm. Next, the influence of the number of Landsat images and class of land use and land cover (LULC) on the accuracy of the CCDC was investigated. Finally, correlations between annual NDVI-I and climate were analyzed. The results revealed that NDVI-I accuracy was influenced mainly by the LULC class and to a lesser extent by the number of cloud-free Landsat observations. Infra- and inter-site variations in NDVI-I were high and showed an overall increasing trend. NDVI-I was positively correlated with the mean temperature. This study shows that this approach applied in contrasting sites is robust for the long-term monitoring of wetland ecosystem functioning and can be used to improve the implementation of international biodiversity conservation policies.
Multi-Objective Optimization of Short-Inverted Transport Scheduling Strategy Based on Road–Railway Intermodal Transport
Authors: Guo Dudu , Su Yinuo , Zhang Xiaojiang , Yang Zhen , Duan Pengbin
Published: July 2024

This study focuses on the ‘short-inverted transportation’ scenario of intermodal transport. It proposes a vehicle unloading reservation mechanism to optimize the point-of-demand scheduling system for the inefficiency of transport due to the complexity and uncertainty of the scheduling strategy. This paper establishes a scheduling strategy optimization model to minimize the cost of short backhaul and obtain the shortest delivery time window and designs a hybrid NSGWO algorithm suitable for multi-objective optimization to solve the problem. The algorithm incorporates the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm based on the Grey Wolf Optimizer (GWO) algorithm, compensating for a single algorithm’s premature convergence. The experiment selects a logistics carrier’s actual road–rail intermodal short-inverted data and compares and verifies the above data. The results show that the scheduling scheme obtained by this algorithm can save 41.01% of the transport cost and shorten the total delivery time by 46.94% compared with the original scheme, which can effectively protect the enterprise’s economic benefits while achieving timely delivery. At the same time, the optimized scheduling plan resulted in a lower number of transport vehicles, which positively impacted the sustainability of green logistics.
Coastal Waterfront Transformations, Fishing Structures, and Sustainable Tourism
Authors: Balsas Carlos J. L.
Published: July 2024

Fishing is a socioeconomic activity with highly visible impacts on the water–land interface of cities. Tourism, the number and type of visitors, and attractions depend on the image and experience of coastal places. How has fishing evolved over time? How has planning attempted to influence and adapt the land use and built-up structures, leading to the activity’s development and commercialization? It utilizes three pairwise cases to analyze the land use transformations associated with fishing activities in cities and some of their most important structures, with impacts on tourism activities such as fish markets, waterfront and pier restaurants, festival marketplaces, and recreational facilities. The three pairwise cases are in three different regions of the world (i.e., North America, Southern Europe, and the Pacific Rim). New Bedford, Massachusetts (USA) and Figueira da Foz (Portugal) are utilized to analyze the land use transformations associated with fishing activities in cities. Fish markets in Tokyo (Japan) and Sydney (Australia) are analyzed to study built-up structures where fish are commercialized. Finally, the last pairwise waterfronts consisting of San Francisco (California, USA) and Fremantle (Western Australia) are examined to understand their fish consumption. The research design and methods comprised in loco visits to the six case studies; discussions with stakeholders; visual documentation and analysis; and a distillation of implications for public policy. The findings demonstrate that more attention needs to be paid to land use changes, the co-existence of working harbor operations with recreational uses, the accessibility to those areas, the “publicness” of the areas in terms of public spaces and other coastal amenities, and the impacts of mixed-use developments on adjacent residential areas. Many waterfronts have suffered radical changes and ought to be made more accessible, authentic, and livable not through the effects of nature or markets alone but with planning and urban design interventions.