bluuebunny/crossref_metadata_2025 huggingface dataset:
Toxicological Assessment of 2-Hydroxychalcone-Mediated Photodynamic Therapy: Comparative In Vitro and In Vivo Approaches
Authors: Bila Níura Madalena , Vaso Carolina Orlando , Belizário Jenyffie Araújo , Assis Letícia Ribeiro , Regasini Luís Octávio , Fontana Carla Raquel , Fusco-Almeida Ana Marisa , Costa-Orlandi Caroline Barcelos , Mendes-Giannini Maria José Soares
Published: November 2024

Background: Photodynamic therapy (PDT) is a treatment modality that uses light to activate a photosensitizing agent, destroying target cells. The growing awareness of the necessity to reduce or eliminate the use of mammals in research has prompted the search for safer toxicity testing models aligned with the new global guidelines and compliant with the relevant regulations. Objective: The objective of this study was to assess the impact of PDT on alternative models to mammals, including in vitro three-dimensional (3D) cultures and in vivo, in invertebrate animals, utilizing a potent photosensitizer, 2-hydroxychalcone. Methods: Cytotoxicity was assessed in two cellular models: monolayer (2D) and 3D. For this purpose, spheroids of two cell lines, primary dermal fibroblasts (HDFa) and adult human epidermal cell keratinocytes (HaCat), were developed and characterized following criteria on cell viability, shape, diameter, and number of cells. The survival percentages of Caenorhabditis elegans and Galleria mellonella were evaluated at 1 and 7 days, respectively. Results: The findings indicated that all the assessed platforms are appropriate for investigating PDT toxicity. Furthermore, 2-hydroxychalcone demonstrated low toxicity in the absence of light and when mediated by PDT across a range of in vitro (2D and 3D cultures) and in vivo (invertebrate animal models, including G. mellonella and C. elegans) models. Conclusion: There was a strong correlation between the in vitro and in vivo tests, with similar toxicity results, particularly in the 3D models and C. elegans, where the concentration for 50% viability was approximately 100 µg/mL.
Study on Impact of Monomers Towards High Molecular Weight Bio-Based Poly(ethylene Furanoate) via Solid State Polymerization Technique
Authors: Stanley Johan , Xanthopoulou Eleftheria , Kostoglou Margaritis , Fras Zemljič Lidija , Lambropoulou Dimitra A. , Bikiaris Dimitrios N.
Published: November 2024

In recent years, bio-based poly(ethylene furanoate) has gained the attention of packaging industries owing to its remarkable properties as a promising alternative to fossil-based polymers. It is necessary to synthesize high-molecular-weight polymers using effective and straightforward techniques for their commercialization. In this present work, poly(ethylene 2,5-furan dicarboxylate) (PEF) was produced with a high molecular weight of 0.43 dL/g using 2,5-furan dicarboxylic acid (FDCA) or its derivative Dimethyl-2,5-Furan dicarboxylate (DMFD), followed by solid-state polymerization (SSP) conducted at different temperatures and reaction times. The intrinsic viscosity ([η]), carboxyl end-group concentration (–COOH), and thermal properties of the produced polyesters were evaluated using differential scanning calorimetry (DSC). The results indicated that the SSP process improved the melting temperature and crystallinity of both the PEF samples as the reaction times and temperatures increased, as corroborated by DSC and X-ray diffraction (XRD) analyses. Additionally, both intrinsic viscosity and number-average molecular weight saw an increase with longer SSP durations and higher temperatures, while the concentration of carboxyl end groups decreased, aligning with expectations. The overall results indicate that PEF (DMFD) samples exhibited a significant increase in crystallization and molecular weight, attributed to their lower degree of crystallinity and their monomer’s high purity.
Revealing Long-Range Order in Brush-like Graft Copolymers Through In Situ Measurements of X-Ray Scattering During Deformation
Authors: Umarov Akmal Z. , Nikitina Evgeniia A. , Piryazev Alexey A. , Moutsios Ioannis , Rosenthal Martin , Kurbatov Andrey O. , Gordievskaya Yulia D. , Kramarenko Elena Yu. , Dashtimoghadam Erfan , Maw Mitchell R. , Sheiko Sergei S. , Ivanov Dimitri A.
Published: November 2024

Brush-like graft copolymers (A-g-B), in which linear A-blocks are randomly grafted onto the backbone of a brush-like B-block, exhibit intense strain-stiffening and high mechanical strength on par with load-bearing biological tissues such as skin and blood vessels. To elucidate molecular mechanisms underlying this tissue-mimetic behavior, in situ synchrotron X-ray scattering was measured during uniaxial stretching of bottlebrush- and comb-like graft copolymers with varying densities of poly(dimethyl siloxane) and poly(isobutylene) side chains. In an undeformed state, these copolymers revealed a single interference peak corresponding to the average spacing between the domains of linear A-blocks arranged in a disordered, liquid-like configuration. Under uniaxial stretching, the emergence of a distinct four-spot pattern in the small-angle region indicated the development of long-range order within the material. According to the affine deformation of a cubic lattice, the four-spot pattern’s interference maxima correspond to 110 reflections upon stretching along the [111] axis of the body-centered unit cell. The experimental findings were corroborated by computer simulations of dissipative particle dynamics that confirmed the formation of a bcc domain structure.
Piezoelectric Biopolymers: Advancements in Energy Harvesting and Biomedical Applications
Authors: Xu Menghan , Wen Yongxian , Shi Zhuqun , Xiong Chuanxi , Zhu Fangju , Yang Quanling
Published: November 2024

Biodegradable piezoelectric polymers have emerged as a hot research focus in bioelectronics, energy-harvesting systems, and biomedical applications, as well as in sustainable future development. Biopolymers possess plenty of features which make them promising candidates for next-generation electronic technologies, including biocompatibility, degradability, and flexibility. This review discusses piezoelectric biopolymers, focusing on the relationship between coupling mechanisms, material structures, and piezoelectric performance. Processing techniques such as annealing, mechanical drawing, and poling are introduced and further studied in terms of achieving high piezoelectric performance. This work reviews the strategies for enhancing piezoelectric properties via molecular engineering, nano structuring, and the incorporation of additives. Furthermore, the applications of these biopolymers in energy harvesting and biomedicine are provided, with a discussion of their potential in degradable bioelectronic devices. There are still challenges in optimizing piezoelectric performance and ensuring stability. Our research is expected to provide an understanding of these challenges and help to achieve a wider application of piezoelectric biopolymers.
Carbon Based Polymeric Nanocomposite Hydrogel Bioink: A Review
Authors: Madhusudhan Alle , Suhagia Tejaskumar A. , Sharma Chhavi , Jaganathan Saravana Kumar , Purohit Shiv Dutt
Published: November 2024

Carbon-based polymeric nanocomposite hydrogels (NCHs) represent a groundbreaking advancement in biomedical materials by integrating nanoparticles such as graphene, carbon nanotubes (CNTs), carbon dots (CDs), and activated charcoal (AC) into polymeric matrices. These nanocomposites significantly enhance the mechanical strength, electrical conductivity, and bioactivity of hydrogels, making them highly effective for drug delivery, tissue engineering (TE), bioinks for 3D Bioprinting, and wound healing applications. Graphene improves the mechanical and electrical properties of hydrogels, facilitating advanced tissue scaffolding and drug delivery systems. CNTs, with their exceptional mechanical strength and conductivity, enhance rheological properties, facilitating their use as bioinks in supporting complex 3D bioprinting tasks for neural, bone, and cardiac tissues by mimicking the natural structure of tissues. CDs offer fluorescence capabilities for theranostic applications, integrating imaging and therapeutic functions. AC enhances mechanical strength, biocompatibility, and antibacterial effectiveness, making it suitable for wound healing and electroactive scaffolds. Despite these promising features, challenges remain, such as optimizing nanoparticle concentrations, ensuring biocompatibility, achieving uniform dispersion, scaling up production, and integrating multiple functionalities. Addressing these challenges through continued research and development is crucial for advancing the clinical and industrial applications of these innovative hydrogels.
Novel Synthesis of Polystyrenesulfonate@AC Based on Olive Tree Leaves Biomass for the Photo-Degradation of Methylene Blue from Aqueous Solution
Authors: Alsohaimi Ibrahim Hotan
Published: November 2024

Water pollution poses significant environmental challenges, particularly from dyes used in various industrial processes. Effective removal methods are essential to mitigate their impact on aquatic environments. Activated carbon (AC) is widely used for its adsorption properties, and further modifications can enhance its efficiency. In this study, we developed polystyrene sulfonate-modified activated carbon (AC@PSS) using a facile and efficient method to improve the photo-degradation of methylene blue (MB) in aquatic environments. The modification enhanced the activated carbon’s surface features and adsorption, improving its photocatalytic activity. The photocatalysts were characterized using XRD, SEM, FTIR, and TGA. Based on Tauc’s equation, the band gap value of AC@PSS was 4.0 eV. The photocatalytic efficacy of the AC@PSS catalyst was assessed by studying the degradation of MB dye under UV-rich solar irradiation. The influence of various variables on the photo-degradation of MB dye such as pH (2–12), reaction time (0–160 min), catalyst dosage (20–80 mg), and dye concentration (10–300 mg/L) was investigated. The AC@PSS catalyst demonstrated impressive degradation efficacy for MB dye of 98% in 160 min at pH 11, a temperature of 25 °C, a catalyst dose of 60 mg, and initial MB content of 10 mg/L. The superior performance of the AC@PSS catalyst could be due to the effective separation of photogenerated electron holes. Accordingly, the photo-degradation of MB is affected by the photo-produced radical •OH. Finally, we conclude that synthesizing AC@PSS is highly effective for the degradation of MB dye.
Inactivation of Aspergillus Species and Degradation of Aflatoxins in Water Using Photocatalysis and Titanium Dioxide
Authors: Quintanilla-Villanueva Gabriela Elizabeth , Luna-Moreno Donato , Núñez-Salas Raisa Estefanía , Rodríguez-Delgado Melissa Marlene , Villarreal-Chiu Juan Francisco
Published: November 2024

Aflatoxins (AF) are highly toxic secondary metabolites produced by various species of Aspergillus, posing significant health risks to humans and animals. The four most prominent types are aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1), and aflatoxin G2 (AFG2). These mycotoxins are prevalent in various environments, including water sources and food products. Among these mycotoxins, AFB1 is recognized as the most toxic, mutagenic, and carcinogenic to humans. Consequently, most efforts to mitigate the impact of AF have been focused on AFB1, with photocatalysis emerging as a promising solution. Recent research has demonstrated that using semiconductor photocatalysis, particularly titanium dioxide (TiO2), combined with UV–visible irradiation significantly enhances the efficiency of AF degradation. TiO2 is noted for its high activity under UV irradiation, non-toxicity, and excellent long-term stability, making it a favorable choice for photocatalytic applications. Furthermore, TiO2 combined with visible light has demonstrated the ability to reduce AF contamination in food products. This article summarizes the working conditions and degradation rates achieved, as well as the advantages, limitations, and areas of opportunity of these methodologies for the degradation of AF and preventing their production, thereby enhancing food and water safety.
Finite Element Simulation and Piezoelectric Sensor Array-Driven Two-Stage Impact Location on Composite Structures
Authors: Wang Zhiling , Zhong Yongteng
Published: November 2024

Impact monitoring is an effective approach to ensuring the safety of composite structures. The accuracy of current algorithms mostly depends on the number of physical sensors, which is not an economical way for large-area composite structures. In order to combine the advantages of sparse and dense arrays, a two-stage collaborative approach is proposed to locate the general areas and precise positions of impacts on composite structures. In Stage I, the steering vector information of the possible position is simulated according to the principle of array sensor signal processing, and a virtual array sparse feature map is constructed. When an actual impact arrives, a similarity algorithm is then used to find the suspected area in the map, which narrows down the search area to a large extent. In Stage II, a compensated two-dimensional multiple signal classification (2D-MUSIC) algorithm-based imaging method is applied to estimate the precise position of the impact in the suspected area. Finally, the accuracy and effectiveness of the proposed method are validated by numerical simulation and experiments on a carbon fiber composite panel. Both numerical and experimental results verify that the two-stage impact location method can effectively monitor composite structures with sufficient accuracy and efficiency.
The Revival of Confucian Philosophy Through Its Interaction with Daoism: The Case of Sixth-Century Master Liu (Liuzi)
Authors: Rogacz Dawid
Published: November 2024

This paper offers the first English-language philosophical treatment of Master Liu (Liuzi 劉子)—a treatise that gives a unique insight into the intellectual life of sixth-century China. Most probably written by Liu Zhou (d. 565) and known at the Tang court, the work was later neglected due to its eclectic label. This article argues that Liuzi integrated Confucian moral philosophy with selected Daoist ideas and responded to post-Buddhist transformations of key categories of Chinese thought in a manner that anticipates many solutions characteristic of neo-Confucian lixue. This includes an innovative understanding of such categories as spirit (shen) and heart-mind (xin), feelings (qing) and desires (yu), and, finally, reliability (xin) and balancing (quan).
Buddhist Cosmopolitanism? Abbot Chao Kung and the International Interaction of Modern Chinese Buddhism
Authors: Shao Jiade
Published: November 2024

Master Chao Kung 照空 (Ignatius Trebitsch Lincoln, 1879–1943), who asserted to be the first European to undergo monastic ordination in China, possessed a multifaceted identity. According to the Chinese newspapers of the 1930s, Master Chao Kung was documented simultaneously as a clergyman, a British Parliamentarian, and a German spy before he transitioned into monastic life. Throughout his activities as a monk in China, Chao Kung garnered significant attention from both the public and the domestic Buddhist community and frequently engaged in matters concerning Japan and Tibet, where he elicited mixed evaluations. This paper endeavors to scrutinize Chao Kung’s monastic journey during his residence in China based on an array of historical sources, to analyze the distinctiveness of his role and its impact on Chinese Buddhism as well as on the global Buddhist network, by situating it within the broader framework of the globalization of Buddhism in the modern era and the context of the intricate political dynamics of the Republican period.
The Temple as a Symbol of the Virgin Mary in Medieval Liturgical Hymns and Its Reflection in Images of the Annunciation of the 14th–15th Centuries
Authors: Salvador-González José María
Published: November 2024

The current article seeks two interrelated objectives. First, we will shed light on the Mariological and Christological meanings underlying the saying templum Dei and other similar metaphorical expressions, with which countless medieval liturgical hymns, inspired by the Bible, designate the Virgin Mary. Second, we will iconographically interpret some paintings of the Annunciation from the 14th and 15th centuries that represent Mary’s house in Nazareth as a majestic temple. In this order of ideas, we will proceed according to two complementary methodological strategies: first, we will analyze an abundant corpus of fragments of liturgical hymns configured around some biblical metaphors, such as temple of God, tabernacle of the Lord, seat of Wisdom, Ark of the Covenant, temple of Solomon, or throne of the King (Solomon); second, we will analyze eight pictorial Annunciations that include a temple in their scene. The comparison of both analyses, the texts and the images, will allow us to conclude that these texts are the conceptual support of those images, and that the latter are the visual illustrations of those concepts.
Moisture Content Vegetation Seasonal Variability Based on a Multiscale Remote Sensing Approach
Authors: Santos Filippe L. M. , Rodrigues Gonçalo , Potes Miguel , Couto Flavio T. , Costa Maria João , Dias Susana , Monteiro Maria José , Ribeiro Nuno de Almeida , Salgado Rui
Published: November 2024

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R2 = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R2 = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques.
Enhancing Weather Target Detection with Non-Uniform Pulse Repetition Time (NPRT) Waveforms
Authors: Sun Luyao , Wang Tao
Published: November 2024

The velocity/distance trade-off poses a fundamental challenge in pulsed Doppler weather radar systems and is known as the velocity/distance dilemma. Techniques such as multiple-pulse repetition frequency, staggered pulse repetition time (PRT), and pulse phase coding are commonly used to mitigate this issue. The current study evaluates the adaptability/capability of a specific type of low-capture signal called the non-uniform PRT (NPRT) through analyzing the weather target characteristics of typical velocity distributions. The spectral moments estimation (SME) signal-processing algorithm of the NPRT weather echo is designed to calculate the average power, velocity, and spectrum width of the target. A comprehensive error analysis is conducted to ascertain the efficacy of the NPRT processing algorithm under influencing factors. The results demonstrate that the spectral parameters of weather target echo with a velocity of [−50,50] m/s through random-jitter NPRT signals align with radar functionality requirements (RFRs). Notably, the NPRT waveform resolves the inherent conflicts between the maximum unambiguous distance and velocity and elevates the upper limit of the maximal observation velocity. The evaluation results confirm that nonlinear radar signal processing technology can improve a radar’s detection performance and provide a new method for realizing the multifunctional observation of radar in different applications.
Deep Reinforcement Learning-Based Attention Decision Network for Agile Earth Observation Satellite Scheduling
Authors: Liu Dongning , Zhou Guanghui
Published: November 2024

Agile Earth observation satellite scheduling is crucial for space-based remote-sensing services. The sharply rising demands and explosion of the solution space pose significant challenges to the optimization of observation task scheduling. To address this issue, we propose a deep reinforcement learning-based attention decision network (ADN) to determine the task scheduling sequence. We also construct a Markov decision process model in which the original and direct attributes are defined to describe the environment and used as the input of the ADN. Moreover, a start-time-shift-based local search is proposed to improve the observation plan generated by the ADN model. A comprehensive experiment was conducted, and the results proved that the attention mechanism in our ADN was beneficial for the training process to converge to better strategies. Compared with other advanced algorithms, the proposed method obtained a better total profit in the test sets. Furthermore, our methods exhibit considerable time efficiency, even for large-scale problems.
A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat
Authors: Yang Runzhi , Li Shanshan , Zhang Bing , Jiao Quanjun , Peng Dailiang , Yang Songlin , Yu Ruyi
Published: November 2024

Spectral information plays a crucial role in fractional vegetation cover (FVC) estimation, and selecting the appropriate spectral information is essential for improving the accuracy of FVC estimation. Traditionally, spectral feature selection is primarily guided by physical mechanisms or empirical statistical models. This has led to the use of multispectral and hyperspectral images, which often result in missing or redundant information, thereby decreasing the efficiency and accuracy of FVC estimation. This study proposes a novel dual-attention network to select the feature bands of Sentinel-2 multispectral images for the accurate FVC estimation of winter wheat. In the first step, the importance of hyperspectral band reflectances was determined using simulated data from the PROSAIL model, by combining the dual-attention mechanism with the convolutional neural network (DAM-CNN). In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. Finally, the selected features were assessed based on their performance in FVC estimation using a CNN model with real data. The experimental results indicate that during the key growth period of winter wheat, the combination of red, green, and red-edge bands significantly influences the FVC estimation accuracy. Band 3 (Green), band 4 (Red), band 5 (Red-edge 1), and band 6 (Red-edge 2) of Sentinel-2 satellite images contribute most significantly to winter wheat FVC estimation, achieving an accuracy comparable to that obtained using all bands, while reducing the training time by 19.1%, as confirmed by field survey data.
Monitoring and Disaster Assessment of Glacier Lake Outburst in High Mountains Asian Using Multi-Satellites and HEC-RAS: A Case of Kyagar in 2018
Authors: Jiang Long , Lin Zhiqiang , Zhou Zhenbo , Luo Hongxin , Zheng Jiafeng , Su Dongsheng , Song Minhong
Published: November 2024

The glaciers in the High Mountain Asia (HMA) region are highly vulnerable to global warming, posing significant threats to downstream populations and infrastructure through glacier lake outburst floods (GLOFs). The monitoring and early warnings of these events are challenging due to sparse observations in these remote regions. To explore reproducing the evolution of GLOFs with sparse observations in situ, this study focuses on the outburst event and corresponding GLOFs in August 2018 caused by the Kyagar Glacier lake, a typical glacier lake of the HMA in the Karakoram, which is known for its frequent outburst events, using a combination of multi-satellite remote sensing data (Sentinel-1 and Sentinel-2) and the HEC-RAS hydrodynamic model. The water depth of the glacier lake and downstream was extracted from satellite data adapted by the Floodwater Depth Elevation Tool (FwDET) as a baseline to compare them with simulations. The elevation-water volume curve was obtained by extrapolation and was applied to calculate the water surface elevation (WSE). The inundation of the downstream of the lake outburst was obtained through flood modeling by incorporating a load elevation-water volume curve and the Digital Elevation Model (DEM) into the hydrodynamic model HEC-RAS. The results showed that the Kyagar glacial lake outburst was rapid and destructive, accompanied by strong currents at the end of each downstream storage ladder. A series of meteorological evaluation indicators showed that HEC-RAS reproduced the medium and low streamflow rates well. This study demonstrated the value of integrating remote sensing and hydrodynamic modeling into GLOF assessments in data-scarce regions, providing insights for disaster risk management and mitigation.
Unusual Sunrise and Sunset Terminator Variations in the Behavior of Sub-Ionospheric VLF Phase and Amplitude Signals Prior to the Mw7.8 Turkey Syria Earthquake of 6 February 2023
Authors: Boudjada Mohammed Y. , Biagi Pier F. , Eichelberger Hans U. , Nico Giovanni , Schwingenschuh Konrad , Galopeau Patrick H. M. , Solovieva Maria , Contadakis Michael , Denisenko Valery , Lammer Helmut , Voller Wolfgang , Giner Franz
Published: November 2024

We report on the recent earthquakes (EQs) that occurred, with the main shock on 6 February 2023, principally in the central southern part of Turkey and northwestern Syria. This region is predisposed to earthquakes because of the tectonic plate movements between Anatolian, Arabian, and African plates. The seismic epicenter was localized at 37.08°E and 37.17°N with depth in the order of 10 km and magnitude Mw7.8. We use Graz’s very-low-frequency VLF facility (15.43°E, 47.06°N) to investigate the amplitude variation in the Denizköy VLF transmitter, localized in the Didim district of Aydin Province in the western part of the Anatolian region in Turkey. Denizköy VLF transmitter is known as Bafa transmitter (27.31°E, 37.40°N), radiating at a frequency of 26.7 kHz under the callsign TBB. This signal is detected daily by the Graz facility with an appropriate signal-to-noise ratio, predominantly during night observations. We study in this analysis the variations of TBB amplitude and phase signals as detected by the Graz facility two weeks before the earthquake occurrence. It is essential to note that the TBB VLF transmitter station and the Graz facility are included in the preparation seismic area, as derived from the Dobrovolsky relationship. We have applied the multi-terminators method (MTM), revealing anomalies occurring at sunset and sunrise terminator occasions and derived from the amplitude and the phase. Minima and maxima of the TBB signal are linked to three terminators, i.e., Graz facility, TBB transmitter, and EQ epicenter, by considering the MTM method. We show that the significant anomalies are those linked to the EQ epicenter. This leads us to make evident the precursor seismic anomaly, which appears more than one week (i.e., 27 January 2023) before EQ occurrence. They can be considered the trace, the sign, and the residue of the sub-ionospheric propagation of the TBB transmitter signal disturbed along its ray path above the preparation EQ zone. We find that the sunrise–sunset anomalies are associated with tectonic regions. One is associated with the Arabian–African tectonic plates with latitudinal stresses in the south–north direction, and the second with the African–Anatolian tectonic plates with longitudinal stresses in the east–west direction. The terminator time shift anomalies prior to EQ are probably due to the lowering (i.e., minima) and raising (i.e., maxima) of the ionospheric electron density generated by atmospheric gravity waves.
Mapping Surface Deformation in Rwanda and Neighboring Areas Using SBAS-InSAR
Authors: Mugabushaka Adrien , Li Zhenhong , Zhang Xuesong , Song Chuang , Han Bingquan , Chen Bo , Liu Zhenjiang , Chen Yi
Published: November 2024

Surface deformation poses significant risks to urban infrastructure, agriculture, and the environment in many regions worldwide, including Rwanda and the neighboring areas. This study focuses on surface deformation mapping and time series analysis in Rwanda and the neighboring areas from 2 July 2016 to 8 June 2023 using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The influence of atmospheric delay error is effectively reduced by integrating the Generic Atmospheric Correction Online Service (GACOS), which provides precise atmospheric delay maps. Then the SBAS-InSAR method is utilized to generate surface deformation maps and displacement time series across the region. The results of this study indicated that the maximum deformation rate was −0.11 m/yr (subsidence) and +0.13 m/yr (uplift). Through time series analysis, we quantified subsidence and uplift areas and identified key drivers of surface deformation. Since subsidence or uplift varies across the region, we have summarized the different deformation patterns and briefly analyzed the factors that may lead to deformation. Finally, this study underscores the importance of SBAS-InSAR for tracking surface deformation in Rwanda and the neighboring areas, which offers valuable perspectives for sustainable land utilization strategizing and risk mitigation.
Nighttime Lights and Population Variations in Cities of South/Southeast Asia: Distance-Decay Effect and Implications
Authors: McAvoy Griffin , Vadrevu Krishna P.
Published: November 2024

Urbanization in South and Southeast Asia is accelerating due to economic growth, industrialization, and rural-to-urban migration, with megacities like Mumbai, Delhi, and Jakarta leading the trend. By analyzing VIIRS nighttime satellite data from 323 cities across 17 countries, we investigated the relationship between nighttime light (NTL) brightness and population density at varying distances from city centers. Our findings reveal a significant distance-decay effect, with both the intensity of NTL brightness and the strength of the NTL-population density relationship decreasing as the distance from city centers increases. A clear negative exponential relationship with the highest R2 was observed between NTL brightness and the distance from the city center. Our analysis indicates that a 105 km radius most effectively captures the extent of major metropolitan areas, showing a peak correlation between NTL brightness and population density. Cities like Delhi and Bangkok exhibit high NTL brightness, reflecting advanced infrastructure, while mountainous or desert cities such as Kabul and Thimphu show lower brightness due to geographical constraints. These results highlight the importance of adaptive urban planning, infrastructure development, and sustainability practices in managing urbanization challenges in South and Southeast Asia.
The Maya Landscape of El Infiernito, Chiapas, Mexico: Comparison of Occupied and Unoccupied Airborne LiDAR Mapping Systems
Authors: Schroder Whittaker , Murtha Timothy , Scherer Andrew K. , Morell-Hart Shanti , Golden Charles , Broadbent Eben N. , Zambrano Angélica M. Almeyda , Fernandez-Diaz Juan Carlos
Published: November 2024

Landscape-oriented approaches in archaeology have moved beyond site-based research to interpret how people have engaged with, modified, and constructed the environment and how the legacies of these activities continue to influence land use. In the Maya Lowlands, landscape archaeology is related to the analysis of settlement patterns, households, agricultural intensification, and water management. The increasing availability of LiDAR data has revolutionized the mapping of archaeological landscapes under vegetation, especially in tropical environments like the Maya Lowlands, but researchers still emphasize site-oriented settlement densities and infrastructure. Furthermore, the accessibility of drone-based LiDAR platforms has the potential to collect data across several seasons or years to facilitate change detection. In this paper, we compare three LiDAR datasets collected from 2018 to 2023, using both occupied and unoccupied airborne systems. The landscape surrounding the archaeological site of El Infiernito, Chiapas, Mexico near the Classic period (AD 250–800) dynastic capital of Piedras Negras, Guatemala was selected to compare these LiDAR datasets in the context of prior, extensive ground-based fieldwork. These data were used to interpret the built environment, land use, hydrology, landscapes of movement, and other infrastructure constructed and modified by several communities beginning in the Late Preclassic period (400 BC–AD 250) to the present. When used alongside systematic survey and ground verification, the combination of several LiDAR platforms to collect data across different seasons at El Infiernito enhanced the understanding of the spatial distribution of archaeological sites and features across the karst landscape.
LIO-SAM++: A Lidar-Inertial Semantic SLAM with Association Optimization and Keyframe Selection
Authors: Shen Bingke , Xie Wenming , Peng Xiaodong , Qiao Xiaoning , Guo Zhiyuan
Published: November 2024

Current lidar-inertial SLAM algorithms mainly rely on the geometric features of the lidar for point cloud alignment. The issue of incorrect feature association arises because the matching process is susceptible to influences such as dynamic objects, occlusion, and environmental changes. To address this issue, we present a lidar-inertial SLAM system based on the LIO-SAM framework, combining semantic and geometric constraints for association optimization and keyframe selection. Specifically, we mitigate the impact of erroneous matching points on pose estimation by comparing the consistency of normal vectors in the surrounding region. Additionally, we incorporate semantic information to establish semantic constraints, further enhancing matching accuracy. Furthermore, we propose an adaptive selection strategy based on semantic differences between frames to improve the reliability of keyframe generation. Experimental results on the KITTI dataset indicate that, compared to other systems, the accuracy of the pose estimation has significantly improved.
Design, Fabrication and Characterization of Disk Resonator Gyroscope with Vibration and Shock Resistance
Authors: Zhai Zhaoyang , Bie Xiaorui , Zhu Bingchen , Qi Zhenxiang , Wang Bowen , Wang Kunfeng , Zou Xudong
Published: November 2024

This paper presents a comprehensive optimization of an outer frame anchor disk resonator gyroscope (DRG) with enhanced resistance to vibration and shock, achieved by increasing the resonant frequency of the tub and translation modes. Furthermore, the wineglass mode retains a high quality factor, enhancing sensitivity and reducing the angle random walk (ARW). The performance of the proposed DRG is analyzed using dynamic equations, and its structural parameters are optimized through finite element analysis (FEA). The prototype device was fabricated using a two-mask silicon-on-insulator (SOI) process on (100) single-crystal silicon (SCS), which is better suited for complementary metal-oxide–semiconductor (CMOS) integration compared to (111) SCS. Experimental results show an ARW of 0.63°/h and a bias instability (BI) of 7.7°/h, with no significant performance degradation observed under vibrational environments, indicating potential for tactical-grade performance.
Defect Detection and 3D Reconstruction of Complex Urban Underground Pipeline Scenes for Sewer Robots
Authors: Liu Ruihao , Shao Zhongxi , Sun Qiang , Yu Zhenzhong
Published: November 2024

Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers. The framework encompasses positioning, defect detection, model deployment, 3D reconstruction, and the measurement of realistic pipelines. A lightweight Sewer-YOLO-Slim model is introduced, which reconstructs the YOLOv7-tiny network by adjusting its backbone, neck, and head. Channel pruning is applied to further reduce the model’s complexity. Additionally, a multiview reconstruction technique is employed to build a 3D model of the pipeline from images captured by the sewer robot, allowing for accurate measurements. The Sewer-YOLO-Slim model achieves reductions of 60.2%, 60.0%, and 65.9% in model size, parameters, and floating-point operations (FLOPs), respectively, while improving the mean average precision (mAP) by 1.5%, reaching 93.5%. Notably, the pruned model is only 4.9 MB in size. Comprehensive comparisons and analyses are conducted with 12 mainstream detection algorithms to validate the superiority of the proposed model. The model is deployed on edge devices with the aid of TensorRT for acceleration, and the detection speed reaches 15.3 ms per image. For a real section of the pipeline, the maximum measurement error of the 3D reconstruction model is 0.57 m. These results indicate that the proposed sewer inspection framework is effective, with the detection model exhibiting advanced performance in terms of accuracy, low computational demand, and real-time capability. The 3D modeling approach offers valuable insights for underground pipeline data visualization and defect measurement.
RF Energy Harvesting and Wireless Power Transfer for IoT
Authors: Alcaraz López Onel Luis , Suto Katsuya
Published: November 2024

The rapid proliferation of the Internet of Things (IoT) has transformed modern living by interconnecting billions of devices across industrial, commercial, and domestic sectors [...]
Digital Twin for a Frequency Mixer Used as a Phase Sensor
Authors: Pires Carlos , Abreu Manuel , Godinho Isabel , Agostinho Rui , Sousa João A.
Published: November 2024

The Portuguese Institute for Quality is responsible for the realization and dissemination of the frequency standard in Portugal. There are several techniques for frequency transfer, but we use a frequency mixer to detect phase variations between two light signals with different wavelengths, traveling along an optical fibre. In this paper, we present the development of a digital twin (DT) that replicates the use of a frequency mixer to improve the frequency transfer problem. A setup was built to train and validate the technique: a frequency mixer was used to determine the phase difference between the two signals, which are caused by temperature gradients in the fibre, together with real-time temperature data from sensors placed along the fibre and on the mixer itself. The DT was trained with two machine learning algorithms, in particular, ARIMA and LSTM networks. To estimate the accuracy of the frequency mixer working as a phasemeter, several sources of uncertainty were considered and included in the DT model, with the goal of obtaining a phase value measurement and its uncertainty in real time. The JCGM 100:2008 and JCGM 101:2008 approaches were used for the estimation of the uncertainty budget. With this work, we merge DT technology with a frequency mixer used for phase detection to provide its value and uncertainty in real time.
Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI
Authors: Seshimo Hiroyuki , Rashed Essam A.
Published: November 2024

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnostics due to its ability to produce high-contrast images across a variety of sequences, each highlighting distinct tissue characteristics. This study focuses on enabling multimodal MRI sequences to advance the automatic segmentation of low-grade astrocytomas, a challenging task due to their diffuse and irregular growth patterns. A novel mutual-attention deep learning framework is proposed, which integrates complementary information from multiple MRI sequences, including T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, to enhance the segmentation accuracy. Unlike conventional segmentation models, which treat each modality independently or simply concatenate them, our model introduces mutual attention mechanisms. This allows the network to dynamically focus on salient features across modalities by jointly learning interdependencies between imaging sequences, leading to more precise boundary delineations even in regions with subtle tumor signals. The proposed method is validated using the UCSF-PDGM dataset, which consists of 35 astrocytoma cases, presenting a realistic and clinically challenging dataset. The results demonstrate that T2w/FLAIR modalities contribute most significantly to the segmentation performance. The mutual-attention model achieves an average Dice coefficient of 0.87. This study provides an innovative pathway toward improving segmentation of low-grade tumors by enabling context-aware fusion across imaging sequences. Furthermore, the study showcases the clinical relevance of integrating AI with multimodal MRI, potentially improving non-invasive tumor characterization and guiding future research in radiological diagnostics.
An Industrial Internet Security Assessment Model Based on a Selectable Confidence Rule Base
Authors: Yang Qingqing , Li Shiming , Wang Yuhe , Li Guoxing , Yuan Yanbin
Published: November 2024

To mitigate the impact of network security on the production environment in the industrial internet, this paper proposes a confidence rule-based security assessment model for the industrial internet that uses selective modeling. First, a definition of selective modeling tailored to the characteristics of the industrial internet is provided. Based on this, the assessment process of the Selectable Belief Rule Base (BRB-s) model is introduced. Then, in combination with the Selection covariance matrix adaptive evolution strategy (S-CMA-ES) algorithm, a parameter optimization method for the BRB-s model is designed, which expands the selective constraints on expert knowledge. This model establishes a better unidirectional selection strategy among different subgroups, and while expanding the selection constraints on expert knowledge, it achieves better evaluation results. This effectively addresses the issue of reduced modeling accuracy caused by insufficient data and poor data quality. Finally, the experiments of different evaluation models on industrial data sets are compared, and good results are obtained, which verify the evaluation accuracy of the industrial Internet network security situation assessment model proposed in this paper and the feasibility and effectiveness of the S-CMA-ES optimization algorithm.
Enhancing LiDAR Mapping with YOLO-Based Potential Dynamic Object Removal in Autonomous Driving
Authors: Jeong Seonghark , Shin Heeseok , Kim Myeong-Jun , Kang Dongwan , Lee Seangwock , Oh Sangki
Published: November 2024

In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. GPS, while commonly used for localization, often fails in urban environments due to signal blockages. To address this limitation, our system integrates YOLOv4 with LiDAR, enabling the removal of dynamic objects to improve map accuracy and localization in high-traffic areas. Existing methods using LiDAR segmentation for map matching often suffer from missed detections and false positives, degrading performance. Our approach leverages YOLOv4’s robust object detection capabilities to eliminate potentially dynamic objects while retaining static environmental features, such as buildings, to enhance map accuracy and reliability. The proposed system was validated using a mid-size SUV equipped with LiDAR and camera sensors. The experimental results demonstrate significant improvements in map-matching and localization performance, particularly in urban environments. The system achieved RMSE (Root Mean Square Error) reductions compared to conventional methods, with RMSE values decreasing from 0.9870 to 0.9724 in open areas and from 1.3874 to 1.1217 in urban areas. These findings highlight the ability of the Vision + LiDAR + NDT method to enhance localization performance in both simple and complex environments. By addressing the challenges of dynamic obstacles, the proposed system effectively improves the accuracy and robustness of autonomous navigation in high-traffic settings without relying on GPS.
Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter
Authors: Zhang Yan , Zhang Yongbo , Yu Jinhui , Zhao Fei , Zhu Shihao
Published: November 2024

As sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In this paper, a structural online damage identification and dynamic reliability prediction method based on Unscented Kalman Filter (UKF) is presented. Specifically, in the Wiener degradation process with random effects on structural performance, the structural damage identification is initially realized using UKF. Following that, the EM algorithm is employed for estimating the performance model parameters. Eventually, dynamic reliability prediction is realized based on conditional probability. The simulation results indicate that the method effectively estimates the damage state during the structure’s use while providing accurate, real-time, and dynamic reliability predictions for the system.