bluuebunny/crossref_metadata_2025 huggingface dataset:
Water-Soluble Polyglycidol-Grafted Ladder Calix Resorcinarene Oligomers with Open Chain and Cyclic Topologies: Synthesis, Characteristics, and Biological Evaluation
Authors: Penchev Hristo , Dimitrov Erik , Novakov Christo , Haladjova Emi , Veleva Ralitsa , Moskova-Doumanova Veselina , Topouzova-Hristova Tanya , Rangelov Stanislav
Published: November 2024

Ladder oligomers containing calixarene skeletons in the main chain—calix[4]resorcinarene (CRA) ladder macromolecules with open chain and cyclic macromolecules with double ring-like (Noria-type) topologies—bring particular research attention as functional materials with various applications. However, there is still a remarkable lack of studies into the synthesis of fully water-soluble derivatives of these interesting macromolecules. Research on this topic would allow their bio-based research and application niche to be at least revealed. In the present study, a strategy for the synthesis of water-soluble polyglycidol-derivatized calix resorcinarene ladder oligomers with open chain and cyclic structures is introduced. A grafting from approach was used to build branched or linear polyglycidol chains from the ladder scaffolds. The novel structures were synthesized in quantitative yields and fully characterized by NMR, FTIR and UV–vis spectroscopy, gel permeation chromatography, MALDI-TOF mass spectrometry, analytical ultracentrifugation, and static light scattering to obtain the molar mass characteristics and composition. The biocompatibility and toxicity of the two polyglycidol-derivatized oligomers were investigated and the concentration dependence of the survival of three cell lines of human origin determined. The selective apoptosis effect at relatively low dissolve concentrations toward two kinds of cancerous cell lines was found.
Plasma Surface Modification of the Inner Wall of Montgomery’s Tracheal Implant (T-Tube)
Authors: Kostov Konstantin G. , Barbosa Ananias A. , do Nascimento Fellype , Cardoso Paulo F. G. , Almeida Ana C. P. L. , Quade Antje , Legendre Daniel , Hein Luiz R. O. , Silva Diego M. , Koga-Ito Cristiane Y.
Published: November 2024

Tracheal stenosis (i.e., the abnormal narrowing of the trachea) can occur due to a variety of inflammatory and infectious processes as well as due to therapeutic procedures undertaken by the patient. The most common cause of tracheal obstruction in patients has been prolonged intubation. Depending on the extent of the stenosis and its exact location, the surgical insertion of a tracheal stent is the only option for addressing this issue. The Montgomery T-tube implant is a valuable tracheal stent made from medical-grade silicone that provides a functional airway while supporting the tracheal mucosa. However, its performance is subject to gradual deterioration due to biofilm colonization of the stent’s inner wall, which may explain the discomfort claimed by many patients and clinical failures. Recently, cold atmospheric plasmas (CAPs) have emerged as an alternative technology to many conventional medical procedures, such as wound healing, skin treatment, decontamination of medical devices, etc. Here, we report on plasma-induced surface modification of the inner wall of a T-tube implant, considering future biomedical applications. To generate the plasma, we employed a cold atmospheric pressure plasma jet in gas helium, which was directly inserted into the T-tube implant. To assess the treatment uniformity, the degree of surface modification and its extension along the stent’s inner wall was analyzed using different process parameters.
On the Activation Energy of Termination in Radical Polymerization, as Studied at Low Conversion
Authors: Alghamdi Majed M. , Russell Gregory T.
Published: November 2024

The chain-length-dependent nature of the termination reaction in radical polymerization (RP) renders the overall termination rate coefficient, , a complex parameter in the usual situation where the radical chain-length distribution is non-uniform. This applies also for the activation energy of termination, Ea(), which we subject to detailed mechanistic investigation for the first time. The experimental side of this work measures Ea() for the dilute-solution, low-conversion, chemically initiated homopolymerization of styrene (ST), methyl methacrylate (MMA), butyl methacrylate, and dodecyl methacrylate. Values of 25–39 kJ mol−1 are obtained, consistent with strong chain-length-dependent termination (CLDT) for short chains. On other hand, the reanalysis of analogous bulk polymerization data for ST and MMA finds Ea() values of 18–24 kJ mol−1, consistent with weak CLDT for long chains. Both these results are as expected from the so-called composite model for CLDT. A simple analytic framework for understanding and predicting Ea() values is presented for the standard RP situation of continuous initiation. All the results of this work can be rationalized via this framework, which clearly establishes that Ea() is determined by far more than just the Ea of radical diffusion. This framework is extended to activation energy for the number-average degree of polymerization, Ea(DPn), which we measure and successfully scrutinize via our CLDT model. In the final section of this work, we make interesting, testable predictions about Ea() and/or Ea(DPn) in various RP systems of different natures to those studied here, most notably, systems involving acrylates, continuous photoinitiation, or dominant chain transfer.
Characterization of Fume Suppression Effect and Performance of SBS-Modified Asphalt with Deodorant
Authors: Guo Yinan , Zhao Yu , Sun Lianghao , Xu Xiuchen , Zhang Hongchao
Published: November 2024

SBS-modified asphalt produces a large number of hazardous fumes in the preparation process, which severely endangers health and causes environmental pollution. This paper details the design of a fume generation and collection device for asphalt and proposed a comprehensive method for analyzing fume composition. Two deodorants were incorporated into SBS-modified asphalt to mitigate the hazards of the original hazardous emissions. Then, ultraviolet–visible spectrophotometry, gas chromatography–mass spectrometry, and Fourier-transform infrared spectroscopy were combined to analyze the main component differences between asphalt fumes before and after adding deodorant, and to specify the mechanism of action of deodorants on hazardous fumes and SBS-modified asphalt. Finally, the road performance, including the physical and rheological properties of SBS-modified asphalt blended with deodorant, was evaluated. The results indicated that both deodorizers were effective in reducing the emission of hazardous substances in the fumes of SBS-modified asphalt, and no new hazardous substances were generated. Under hot mixing conditions, the addition of 0.3% of deodorant A (high boiling point ester) was effective in reducing the emission of volatile organic compounds (VOCs) by up to 41.7%, while the reduction in benzene congeners reached at least 50%. On the other hand, 1% of deodorant B (silica–magnesium compounds) reduced the emissions of VOCs and benzene congeners by 36% and 20–42%, respectively, under the same conditions. Furthermore, the addition of deodorant did not affect the original road performance, and even improved the rheological properties to a certain extent, which was conducive to the application of deodorant in pavement engineering.
Rational Fabrication of Polyhedral Oligomeric Silsesquioxane-Based Porous Organic Polymers Sustainably Used for Selective CO2 Adsorption
Authors: Li Tiantian , Kang Guodong , Liu Mengqi , Sun Congcong , Li Jie , Meng Yang , Xue Dingming
Published: November 2024

Different types of porous materials have been developed for the efficient separation of CO2 from mixtures of gases. Nevertheless, the most porous materials cannot be used for extensive industrial applications due to their non-negligible disadvantages, such as complex synthesis routes, expensive monomers, and/or costly catalysts. Therefore, a strategy for fabricating a series of polyhedral oligomeric silsesquioxane (POSS)-based porous organic polymer materials (PBPOPs) was developed through the simple condensation reaction of octaphenylsilsesquioxane and different bromine-containing monomers. It was found that PBPOP-2 exhibits the best CO2 adsorption amount of 41 cm3·g−1 at 273 K and 760 mmHg based on the accessible specific surface area, large pore volumes, and accessible pore sizes. Furthermore, PBPOP-2 exhibits efficient CO2/N2 selectivity and complete regeneration under mild conditions, which demonstrates the potential for the selective separation of CO2 from gas mixtures. This work provides a new route to developing POSS-based POPs for CO2-capture applications.
Non-Pillar Coal Mining by Driving Roadway During Mining Period in High-Gas Top-Coal-Caving Working Face
Authors: Shen Haisheng , Chen Zhongshun , Yuan Yong , Li Bo , Anamor Samuel Kofi
Published: November 2024

To solve the problem of the inability to achieve Y-shaped ventilation in the boundary coal mining of high-gas mines and the problem of gas accumulation in the upper corner of a fully mechanized mining face, non-pillar coal mining technology is proposed by a driving roadway during the mining period. A high-gas working face requires Y-shaped ventilation to achieve upper corner gas control, but Y-shaped ventilation conditions are not available at the boundary coal body. In order to handle this challenge, studies have suggested non-pillar coal mining technology, which involves excavating roadways while mining in order to achieve non-pillar coal extraction and use recoverable wide coal pillars. During the simultaneous excavation of a working face and roadway, studies analyzed the distribution characteristics of the complicated stress environment. Following an evaluation of the impact of coal pillar width on the quality of an excavation roadway, this study’s development is in terms of an effective technique for retaining coal pillars as established. During the mining period of a working face, in the goaf of the working face, the research analyzed the distribution properties of the gas flow field, and findings from the study indicate that the width of the recovered coal pillar influences the distribution of gas. Finally, the width of the coal pillar was comprehensively determined, forming non-pillar coal mining technology by a driving roadway during the mining period. The on-site practice has shown that using a wide coal pillar with a width of 70 m to protect the roadway significantly reduces the deformation of the surrounding rock in the mining roadway, the gas concentration at the return airway is lower than the safety production standard, and by decreasing the mining succession time by 15 months, studies achieved improving the working face’s coal extraction rate by 12.6%.
Multi-Model Fusion Demand Forecasting Framework Based on Attention Mechanism
Authors: Lei Chunrui , Zhang Heng , Wang Zhigang , Miao Qiang
Published: November 2024

The accuracy of demand forecasting is critical for supply chain management and strategic business decisions. However, as data volumes grow and demand patterns become increasingly complex, traditional forecasting methods encounter significant challenges in processing intricate multi-dimensional data and achieving a satisfactory predictive accuracy. To address these challenges, this paper proposed an end-to-end multi-model demand forecasting framework based on attention mechanisms. The framework employs a dual attention mechanism to dynamically extract features from both the temporal and product dimensions, while integrating conditional information captured through convolutional neural networks, thereby enhancing its ability to model complex demand patterns. Additionally, a channel attention mechanism is introduced to perform the weighted fusion of outputs from multiple predictive models, thereby overcoming the limitations of single-model approaches and improving adaptability to varying demand patterns across diverse scenarios. The experimental results demonstrate that the proposed method outperforms conventional approaches across several evaluation metrics, achieving a 42% reduction in Mean Squared Error (MSE) compared to the baseline model. This notable improvement enhances both the accuracy and stability of demand forecasting. The framework offers valuable insights for addressing large-scale and complex demand patterns, providing guidance for precise decision-making and resource optimization within supply chain management. Future research will concentrate on further enhancing the model’s generalization capability to manage missing data and demand fluctuations. Additionally, efforts will focus on integrating diverse heterogeneous data sources to assess its performance in various practical scenarios, ultimately improving the model’s accuracy and flexibility.
Comparison of Full-Arch Intraoral Scans Immediately After Implant Insertion Versus Healed Tissue: A Multicentric Clinical Study
Authors: Bagnasco Francesco , Menini Maria , Pesce Paolo , Crupi Armando , Gibello Umberto , Delucchi Francesca , Carossa Massimo , Pera Francesco
Published: November 2024

Objectives: The purpose of this clinical study is to compare implant full-arch intraoral scans taken immediately after implant placement with those obtained after tissue healing in patients rehabilitated with implant-supported fixed prostheses. Methods: Between September 2023 and March 2024, a total of 19 patients with compromised residual dentition (6 women; 13 men) were rehabilitated using 4-to-6 immediately loaded post-extraction implants. These implants supported fixed full-arch screw-retained prostheses either in the lower jaw (9 patients) or upper jaw (10 patients). Intraoral scans were taken immediately after implant placement (termed “immediate scan”). After a healing period of four months, the provisional prosthesis was removed, and a second intraoral scan was performed using the same scan bodies and scan pattern as the initial scan (termed “delayed scan”). The two scans were overlaid, and the discrepancies between them were measured. Results: The average discrepancy between the immediate and delayed scans was 0.1905 mm. Our statistical analysis revealed larger discrepancies for implants placed in the posterior areas, with the implant in site 1.6 (Implant 1) showing a discrepancy of 0.2326 mm, and the implant in site 2.6 (Implant 4) showing a discrepancy of 0.2124 mm (p = 0.05). No statistically significant difference was observed when comparing patients treated in the upper and lower jaws. Conclusions: Within the limitations of the study and based on this result, clinicians should be aware that an immediate post-surgical intraoral digital scan for implant-supported full-arch rehabilitations may result in a higher risk of imprecision. Furthermore, according to the results of the study, the accuracy of the digital impression on implant full-arch rehabilitations seems to be influenced by the clinician’s skills. Further studies with larger sample sizes are required to confirm our results.
Does Interoceptive Awareness Influence Depression Through Anxiety in Peruvian Adults? A Mediation Analysis
Authors: Vivas-Rivas Leidy Johana , Serpa-Barrientos Antonio , Oblitas-Guerrero Susan M. , Carranza-Cubas Sandra P. , Saintila Jacksaint
Published: November 2024

Interoceptive awareness is currently gaining considerable attention and is in an active phase of research, supported by a growing body of empirical evidence from diverse fields such as neurophysiology, cognitive neuroscience, mind–body therapies, psychiatry, and psychometrics. The aim of this study was to examine the relationship between interoceptive awareness, anxiety, and depression in Peruvian adults. A total of 414 participants residing in Lima, Peru, were included, with 85% being young adults aged 18–27 years (252 women = 61%; 162 men = 39%; age range = 18–64; M age = 23.4). The results indicated that the indirect effect of interoceptive awareness on depression through anxiety accounted for 61.7% of the mediation, while the direct effect of interoceptive awareness on depression accounted for 38.3%. In conclusion, the hypothesis that anxiety negatively mediates the relationship between interoceptive awareness and depression in Peruvian adults was confirmed. This research may have implications in clinical settings, particularly for the prevention and psychotherapeutic intervention of anxiety and depression, through the promotion of adaptive interoceptive awareness.
Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs
Authors: Memon Qurban A. , Al Ahmad Mahmoud , Pecht Michael
Published: November 2024

Quantum computing stands at the precipice of technological revolution, promising unprecedented computational capabilities to tackle some of humanity’s most complex problems. The field is highly collaborative and recent developments such as superconducting qubits with increased scaling, reduced error rates, and improved cryogenic infrastructure, trapped-ion qubits with high-fidelity gates and reduced control hardware complexity, and photonic qubits with exploring room-temperature quantum computing are some of the key developments pushing the field closer to demonstrating real-world applications. However, the path to realizing this promise is fraught with significant obstacles across several key platforms, including sensitivity to errors, decoherence, scalability, and the need for new materials and technologies. Through an exploration of various quantum systems, this paper highlights both the potential and the challenges of quantum computing and discusses the essential role of middleware, quantum hardware development, and the strategic investments required to propel the field forward. With a focus on overcoming technical hurdles through innovation and interdisciplinary research, this review underscores the transformative impact quantum computing could have across diverse sectors.
Ecospiritual Praxis: Cultivating Connection to Address the Climate Crisis
Authors: Bock Cherice
Published: November 2024

This article suggests ecospirituality as a connection point between religion, science, and other disciplines, as well as the relationships between people, the land and waters, the community of all life, and the Divine. Ecospirituality connects different disciplines and highlights the interconnectedness between people and the rest of the natural world, and it also catalyzes action through spiritual experience and meaning-making. A review of different disciplines’ research on ecospirituality is provided. A description of an ecospiritual praxis cycle is offered, based on interviews and survey data. This ecospiritual praxis cycle may be able to help move people toward practical and efficacious actions of care for the community of all life to participate in the collective transformation that needs to occur in order to address the climate and ecological crises. This article identifies disconnection between theory and action regarding climate and environmental knowledge and collective action as one of the main problems, which “undisciplining” religion and science can help overcome.
The Tension Between Buddhism and Science Within Contemporary Chinese Buddhists: A Case Study on the Religious Conversion Narrative Among Monastics in Larung Gar Buddhist Academy
Authors: Liu Yingxu , An Saiping
Published: November 2024

This article delves into the perception of monastics from Larung Gar Buddhist Academy of Western China concerning the intertwining relationship between Buddhism and science, along with the impact of this perception on their worldview and life trajectory. Many monastics at Larung Gar Buddhist Academy initially held a high regard for science, dismissing Buddhism as superstition. However, upon gaining a comprehensive understanding of Buddhism through various opportunities, they came to believe that certain tenets of Buddhism are compatible with science, even suggesting that Buddhism could address some of the methodological and epistemological limitations of science and offer solutions to some issues that science is unable to resolve. This ultimately led them to embrace Buddhism and renounce worldly life. This study employs a case study to investigate the understanding of the relationship between Buddhism and science amongst the general public in contemporary China, an area underexplored by previous scholarship that predominantly concentrated on the philosophical scrutiny of the apologetic discourses towards the reconciliation between Buddhism and science of influential Buddhist ascetics and lay practitioners. Also, this study endeavors to demonstrate that despite the ongoing secularization of contemporary Chinese Buddhism in the “public sphere”, within the “private sphere” of Chinese Buddhism, there remain individuals who are pursuing the religious, sacred, and transcendental dimensions of Buddhism.
Reducing Diagnostic Delay in Axial Spondyloarthritis: Could Lipocalin 2 Biomarkers Help?
Authors: Pritzker Kenneth P. H. , Samari Arash
Published: November 2024

Early diagnosis and therapy in axial spondyloarthritis, axSpA, is known to reduce long-term morbidity. However, the time from symptom onset to diagnosis is typically delayed by several years, and this situation has not improved in recent years despite greater clinical awareness and better imaging. This narrative review discusses the underlying causes for axSpA diagnostic delay. It is proposed that to reduce axSpA diagnostic delay, a better understanding of the axSpA subclinical inflammatory process is required, together with machine learning-enabled inflammation/repair biomarkers such as lipocalin 2 and lipocalin 2-matrix metalloprotease 9, developed through extensive clinical domain knowledge.
Secchi Depth Retrieval in Oligotrophic to Eutrophic Chilean Lakes Using Open Access Satellite-Derived Products
Authors: Rivera-Ruiz Daniela , Arumí José Luis , Lillo-Saavedra Mario , Esse Carlos , Arancibia-Ávila Patricia , Urrutia Roberto , Portuguez-Maurtua Marcelo , Ogashawara Igor
Published: November 2024

The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as the Case 2 Networks (C2RCC-Nets), are notably underrepresented. This study evaluates the capability of C2RCC-Nets using different neural networks—Case-2 Regional/Coast Color (C2RCC), C2X-Extreme (C2X), and C2X-Complex (C2XC)—to estimate Secchi depth in Lake Lanalhue (eutrophic), Lake Villarrica (oligo-mesotrophic), and Lake Panguipulli (oligotrophic). The evaluation used different statistical methods such as Spearman’s correlation and normalized error metrics (nRMSE, nMAE, and nbias) to assess the agreement between satellite-derived data and in situ measurements. C2XC demonstrated the best fit for Lake Lanalhue, with an nRMSE = 33.13%, nMAE = 23.51%, and nbias = 8.57%, in relation to the median ground truth values. In Lake Villarrica, the C2XC neural network displayed a moderate correlation (rs = 0.618) and error metrics, with an nRMSE of 24.67% and nMAE of 20.67%, with an nbias of 4.21%. In the oligotrophic Lake Panguipulli, no relationship was observed between estimated and measured values, which could be related to the fact that the selected neural networks were developed for very case 2 waters. These findings highlight the need for methodological advancements in processing satellite-derived water quality products for Chile’s optical water types, particularly for very clear waters. Nonetheless, this study underscores the need for model-specific calibration of C2RCC-Nets, as lakes with different optical water types and trophic states may require tailored training ranges for inherent optical properties.
Towards 3D Reconstruction of Multi-Shaped Tunnels Utilizing Mobile Laser Scanning Data
Authors: Ding Xuan , Chen Shen , Duan Mu , Shan Jinchang , Liu Chao , Hu Chuli
Published: November 2024

Using digital twin models of tunnels has become critical to their efficient maintenance and management. A high-precision 3D tunnel model is the prerequisite for a successful digital twin model of tunnel applications. However, constructing high-precision 3D tunnel models with high-quality textures and structural integrity based on mobile laser scanning data remains a challenge, particularly for tunnels of different shapes. This study addresses this problem by developing a novel method for the 3D reconstruction of multi-shaped tunnels based on mobile laser scanning data. This method does not require any predefined mathematical models or projection parameters to convert point clouds into 2D intensity images that conform to the geometric features of tunnel linings. This method also improves the accuracy of 3D tunnel mesh models by applying an adaptive threshold approach that reduces the number of pseudo-surfaces generated during the Poisson surface reconstruction of tunnels. This method was experimentally verified by conducting 3D reconstruction tasks involving tunnel point clouds of four different shapes. The superiority of this method was further confirmed through qualitative and quantitative comparisons with related approaches. By automatically and efficiently constructing a high-precision 3D tunnel model, the proposed method offers an important model foundation for digital twin engineering and a valuable reference for future tunnel model construction projects.
Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China
Authors: Xu Xiaolin , Li Dan , Liu Hongxi , Zhao Guang , Cui Baoshan , Yi Yujun , Yang Wei , Du Jizeng
Published: November 2024

Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land cover and their effects on carbon storage, land–atmosphere energy balance, and water cycle processes. However, current data products use different classification methods, resulting in significant classification inconsistency and triggering serious disagreements among related studies. Here, we compared four mainstream land cover products in China, namely GLC_FCS30, CLCD, Globeland30, and CNLUCC. The result shows that only 50.34% of the classification results were consistent across the four datasets. The differences between pairs of datasets ranged from 21.10% to 37.53%. Importantly, most inconsistency occurs in transitional zones among land cover types sensitive to climate change and human activities. Based on the accuracy evaluation, CLCD is the most accurate land cover product, with an overall accuracy reaching 86.98 ± 0.76%, followed by CNLUCC (81.38 ± 0.87%) and GLC_FCS30 (77.83 ± 0.80%). Globeland30 had the lowest accuracy (75.24 ± 0.91%), primarily due to misclassification between croplands and forests. Misclassification diagnoses revealed that vegetation-related spectral confusion among land cover types contributed significantly to misclassifications, followed by slope, cloud cover, and landscape fragmentation, which affected satellite observation angles, data availability, and mixed pixels. Automated classification methods using the random forest algorithm can perform better than those that depend on traditional human–machine interactive interpretation or object-based approaches. However, their classification accuracy depends more on selecting training samples and feature variables.
The Performance of GPM IMERG Product Validated on Hourly Observations over Land Areas of Northern Hemisphere
Authors: Lv Pengfei , Wu Guocan
Published: November 2024

The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, p < 0.05) and Europe (0.308, p < 0.05), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval.
Correction: Wolswijk et al. Can Mangrove Silviculture Be Carbon Neutral? Remote Sens. 2022, 14, 2920
Authors: Wolswijk Giovanna , Barrios Trullols Africa , Hugé Jean , Otero Viviana , Satyanarayana Behara , Lucas Richard , Dahdouh-Guebas Farid
Published: November 2024

In the original publication [...]
Radiometric Cross-Calibration of HJ-2A/CCD3 Using the Random Forest Algorithm and a Spectral Interpolation Convolution Method with Sentinel-2/MSI
Authors: Zhou Xiang , Chen Yidan , Xie Yong , Han Jie , Shao Wen
Published: November 2024

In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%.
Adaptive Kernel Convolutional Stereo Matching Recurrent Network
Authors: Wang Jiamian , Sun Haijiang , Jia Ping
Published: November 2024

For binocular stereo matching techniques, the most advanced method currently is using an iterative structure based on GRUs. Methods in this class have shown high performance on both high-resolution images and standard benchmarks. However, simply replacing cost aggregation with a GRU iterative method leads to the original cost volume for disparity calculation lacking non-local geometric and contextual information. Based on this, this paper proposes a new GRU iteration-based adaptive kernel convolution deep recurrent network architecture for stereo matching. This paper proposes a kernel convolution-based adaptive multi-scale pyramid pooling (KAP) module that fully considers the spatial correlation between pixels and adds new matching attention (MAR) to refine the matching cost volume before inputting it into the iterative network for iterative updates, enhancing the pixel-level representation ability of the image and improving the overall generalization ability of the network. At present, the AKC-Stereo network proposed in this paper has a higher improvement than the basic network. On the Sceneflow dataset, the EPE of AKC-Stereo reaches 0.45, which is 0.02 higher than the basic network. On the KITTI 2015 dataset, the AKC-Stereo network outperforms the base network by 5.6% on the D1-all metric.
Toward Versatile Small Object Detection with Temporal-YOLOv8
Authors: van Leeuwen Martin C. , Fokkinga Ella P. , Huizinga Wyke , Baan Jan , Heslinga Friso G.
Published: November 2024

Deep learning has become the preferred method for automated object detection, but the accurate detection of small objects remains a challenge due to the lack of distinctive appearance features. Most deep learning-based detectors do not exploit the temporal information that is available in video, even though this context is often essential when the signal-to-noise ratio is low. In addition, model development choices, such as the loss function, are typically designed around medium-sized objects. Moreover, most datasets that are acquired for the development of small object detectors are task-specific and lack diversity, and the smallest objects are often not well annotated. In this study, we address the aforementioned challenges and create a deep learning-based pipeline for versatile small object detection. With an in-house dataset consisting of civilian and military objects, we achieve a substantial improvement in YOLOv8 (baseline mAP = 0.465) by leveraging the temporal context in video and data augmentations specifically tailored to small objects (mAP = 0.839). We also show the benefit of having a carefully curated dataset in comparison with public datasets and find that a model trained on a diverse dataset outperforms environment-specific models. Our findings indicate that small objects can be detected accurately in a wide range of environments while leveraging the speed of the YOLO architecture.
Simultaneous Structural Monitoring over Optical Ground Wire and Optical Phase Conductor via Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry
Authors: Canudo Jorge , Sevillano Pascual , Iranzo Andrea , Kwik Sacha , Preciado-Garbayo Javier , Subías Jesús
Published: November 2024

Optimizing the use of existing high-voltage transmission lines demands real-time condition monitoring to ensure structural integrity and continuous service. Operating these lines at the current capacity is limited by safety margins based on worst-case weather scenarios, as exceeding these margins risks bringing conductors dangerously close to the ground. The integration of optical fibers within modern transmission lines enables the use of Distributed Fiber Optic Sensing (DFOS) technology, with Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry (CP-ΦOTDR) proving especially effective for this purpose. CP-ΦOTDR measures wind-induced vibrations along the conductor, allowing for an analysis of frequency-domain vibration modes that correlate with conductor length and sag across spans. This monitoring system, capable of covering distances up to 40 km from a single endpoint, enables dynamic capacity adjustments for optimized line efficiency. Beyond sag monitoring, CP-ΦOTDR provides robust detection of external threats, including environmental interference and mechanical intrusions, which could compromise cable stability. By simultaneously monitoring the Optical Phase Conductor (OPPC) and Optical Ground Wire (OPGW), this study offers the first comprehensive, real-time evaluation of both structural integrity and potential external aggressions on overhead transmission lines. The findings demonstrate that high-frequency data offer valuable insights for classifying mechanical intrusions and environmental interferences based on spectral content, while low-frequency data reveal the diurnal temperature-induced sag evolution, with distinct amplitude responses for each cable. These results affirm CP-ΦOTDR’s unique capacity to enhance line safety, operational efficiency, and proactive maintenance by delivering precise, real-time assessments of both structural integrity and external threats.
Moisture Distribution and Ice Front Identification in Freezing Soil Using an Optimized Circular Capacitance Sensor
Authors: Hu Xing , Dong Qiao , Shi Bin , Yao Kang , Chen Xueqin , Yuan Xin
Published: November 2024

As the interface between frozen and unfrozen soil, the ice front is not only a spatial location concept, but also a potentially dangerous interface where the mechanical properties of soil could change abruptly. Accurately identifying its spatial position is essential for the safe and efficient execution of large-scale frozen soil engineering projects. Electrical capacitance tomography (ECT) is a promising method for the visualization of frozen soil due to its non-invasive nature, low cast, and rapid response. This paper presents the design and optimization of a mobile circular capacitance sensor (MCCS). The MCCS was used to measure frozen soil samples along the depth direction to obtain moisture distribution and three-dimensional images of the ice front. Finally, the experimental results were compared with the simulation results from COMSOL Multiphysics to analyze the deviations. It was found that the fuzzy optimization design based on multi-criteria orthogonal experiments makes the MCCS meet various performance requirements. The average permittivity distribution was proposed to reflect moisture distribution along the depth direction and showed good correlation. Three-dimensional reconstructed images could provide the precise position of the ice front. The simulation results indicate that the MCCS has a low deviation margin in identifying the position of the ice front.
Skin Phototype Classification with Machine Learning Based on Broadband Optical Measurements
Authors: Yu Xun , Ong Keat Ghee , McGeehan Michael Aaron
Published: November 2024

The Fitzpatrick Skin Phototype Classification (FSPC) scale is widely used to categorize skin types but has limitations such as the underrepresentation of darker skin phototypes, low classification resolution, and subjectivity. These limitations may contribute to dermatological care disparities in patients with darker skin phototypes, including the misdiagnosis of wound healing progression and escalated dermatological disease severity. This study introduces (1) an optical sensor measuring reflected light across 410–940 nm, (2) an unsupervised K-means algorithm for skin phototype classification using broadband optical data, and (3) methods to optimize classification across the Near-ultraviolet-A, Visible, and Near-infrared spectra. The differentiation capability of the algorithm was compared to human assessment based on FSPC in a diverse participant population (n = 30) spanning an even distribution of the full FSPC scale. The FSPC assessment distinguished between light and dark skin phototypes (e.g., FSPC I vs. VI) at 560, 585, and 645 nm but struggled with more similar phototypes (e.g., I vs. II). The K-means algorithm demonstrated stronger differentiation across a broader range of wavelengths, resulting in better classification resolution and supporting its use as a quantifiable and reproducible method for skin type classification. We also demonstrate the optimization of this method for specific bandwidths of interest and their associated clinical implications.
Strong Interference Elimination in Seismic Data Using Multivariate Variational Mode Extraction
Authors: Yu Zhichao , Tan Yuyang , Lv Yiran
Published: November 2024

Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals.
The Dynamics of Humanitarian Diplomacy During Wartime: Insights from Tigray Crisis in Ethiopia
Authors: Geremedhn Mulubrhan Atsbaha , Gebrihet Hafte Gebreselassie
Published: November 2024

This study examines the role of humanitarian diplomacy during the Tigray humanitarian crisis in Ethiopia, a humanitarian disaster marked by severe shortages in food, healthcare, and essential services that deeply affect civilians. A qualitative approach using both primary and secondary data grounds the study by exploring key actors in humanitarian diplomacy, their successes, and barriers to aid delivery. Humanitarian actors, such as UN agencies, international NGOs, donor countries, the EU, the US, and the African Union, have engaged with the Ethiopian government, the TPLF, and the Tigray Transitional Government to alleviate the crisis. Notable achievements in humanitarian diplomacy include negotiations, information gathering, communication, civilian needs assessment, resource mobilization, advocacy for international law, and distressed civilians. Humanitarian diplomacy has facilitated international aid operations, saving lives during critical periods, despite practical difficulties. Diplomatic efforts have faced significant interruptions due to access restrictions imposed by the Ethiopian government, security threats from ongoing fighting leading to attacks on aid convoys and casualties among aid workers, and bureaucratic obstacles imposed by the Ethiopian government. This study highlights the necessity for effective humanitarian diplomacy in accounting for complex political landscapes in conflict-affected regions, developing flexible strategies that enhance access to aid, and improving humanitarian interventions.
Dialogue with Avatars in Simulation-Based Social Work Education: A Scoping Review
Authors: Vidman Åsa , Tham Pia
Published: November 2024

Virtual reality provides students with the opportunity to have simulated experiences in a safe setting and is mostly used to teach direct practice skills. One of the most advanced ways of using virtual simulation in social work education is to interact with avatars. Aim: The overall aim of this scoping review was to find out what is known about the use of dialogue with avatars in virtual reality in simulation-based social work education. Materials: Using Arksey and O’Malley’s scoping review framework, 11 articles were included in this review. Results: The skills taught with the avatars varied, as did the ways of preparing students for the sessions. The training was assessed as meaningful learning in a safe and comfortable environment, offering an opportunity to train in practical skills. According to the pre- and post-tests, in several studies the students’ skills seemed to have improved after the training. The qualitative data also pointed to skill developments. Conclusion: Training with avatars seems to be a useful way of preparing students for their future profession and seems to hold great potential in preparing students for demanding situations that cannot be easily trained for in a classroom. The results also point to technical elements that would benefit from development.
Low-Cost Solution for Air Quality Monitoring: Unmanned Aerial System and Data Transmission via LoRa Protocol
Authors: Parra-Medina Francisco David , Vélez-Guerrero Manuel Andrés , Callejas-Cuervo Mauro
Published: November 2024

For both human health and the environment, air pollution is a serious concern. However, the available air quality monitoring networks have important limitations, such as the high implementation costs, limited portability, and considerable operational complexity. In this context, unmanned aerial systems (UASs) are emerging as a useful technological alternative due to their ability to cover large distances and access areas that are difficult or impossible for humans to reach. This article presents the development of an integrated platform that combines an unmanned aerial system (UAS) with specialized sensors to measure key parameters in relation to air quality, such as carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). In addition, a web application called PTECA is developed to visualize the data gathered by the wireless sensor array in real time. The platform incorporates a system that allows real-time tracking of the UAS route and measurement values during sample collection, employing the LoRa communication protocol. This solution represents a low-cost alternative that mitigates some of the limitations of traditional monitoring networks by offering greater portability and accessibility in terms of data collection. Preliminary tests successfully demonstrate the viability of the proposed system in a controlled airspace using geofencing.