bluuebunny/crossref_metadata_2025 huggingface dataset:
Mindfulness in Action: Buddhist Environmental Activism of Earth Holder Berlin
Authors: Zhang Hanyi , Shi Ruotong
Published: October 2024

This study explores the intersection of mindfulness practice and environmental activism within the Earth Holder Berlin (EHB) group, a part of the Order of Interbeing (O of I) inspired by Thich Nhat Hanh. EHB, established in 2020, integrates the principles of engaged Buddhism and endeavors to advance social and ecological justice through mindful actions. Using a qualitative methodology, this research employs semi-structured interviews and participant observation to investigate the experiences of EHB members. The findings demonstrate the transformative potential of Buddhism in ecological discourse, illustrating how EHB members embody epistemic entanglement, intertwining their Buddhist backgrounds with climate knowledge. This integration not only enhances personal transformation but also serves as a catalyst for social and ecological justice, enriching our understanding of engaged Buddhism in contemporary environmental movements.
The Scintillation Counters of the High-Energy Particle Detector of the China Seismo-Electromagnetic (CSES-02) Satellite
Authors: Bartocci Simona , Battiston Roberto , Beolè Stefania , Benotto Franco , Cipollone Piero , Coli Silvia , Contin Andrea , Cristoforetti Marco , De Donato Cinzia , De Santis Cristian , Di Luca Andrea , Dumitrache Floarea , Follega Francesco Maria , Garrafa Botta Simone , Gebbia Giuseppe , Iuppa Roberto , Lega Alessandro , Lolli Mauro , Masciantonio Giuseppe , Mergè Matteo , Mese Marco , Nicolaidis Riccardo , Nozzoli Francesco , Oliva Alberto , Osteria Giuseppe , Palma Francesco , Palmonari Federico , Panico Beatrice , Perciballi Stefania , Perfetto Francesco , Picozza Piergiorgio , Pozzato Michele , Ricci Ester , Ricci Marco , Ricciarini Sergio Bruno , Sahnoun Zouleikha , Savino Umberto , Scotti Valentina , Serra Enrico , Sotgiu Alessandro , Sparvoli Roberta , Ubertini Pietro , Vilona Veronica , Zoffoli Simona , Zuccon Paolo
Published: October 2024

The High-Energy Particle Detector (HEPD-02) is one of the scientific payloads of the China Seismo-Electromagnetic Satellite (CSES-02). The HEPD-02’s main purpose is to characterize the particle environment in the Earth’s vicinity, identifying sudden changes in the fluxes and correlating them with solar and terrestrial phenomena. Additionally, HEPD-02 also has capabilities in detecting Gamma-Ray Bursts. At the core of HEPD-02, a tower of scintillation counters made of plastic and LYSO crystals is able to recognize electrons in the range between 3 and 100 MeV, protons and nuclei between 30 and 200 MeV/n. Plastic scintillators covering the calorimeter on five sides allow to reject particles entering from the top and not completely absorbed within its volume. In this work, the design of the HEPD-02 is reviewed in comparison to its predecessor, HEPD-01, highlighting the innovations of the new design. The design of each scintillation counter type has been fully validated through a campaign of prototype realization, testing, and characterization. The production of the scintillation counters, including the PMT selection process, is also discussed. Finally, the performance of the counters is compared with simulations, showing an agreement of within 20% with the expected performance, thereby meeting expectations.
Analysis of Macro- and Microphysical Characteristics of Ice Clouds over the Tibetan Plateau Using CloudSat/CALIPSO Data
Authors: Guan Yating , Wang Xin , Huo Juan , Zhang Zhihua , Duan Minzheng , Zong Xuemei
Published: October 2024

Utilizing CloudSat/CALIPSO satellite data and ERA5 reanalysis data from 2007 to 2016, this study analyzed the distributions of optical and physical characteristics and change characteristics of ice clouds over the Tibetan Plateau (TP). The results show that the frequency of ice clouds in the cold season (November to March) on the plateau is over 80%, while in the warm season (May to September) it is around 60%. The average cloud base height of ice clouds in the warm season is 3–5 km, and mostly around 2 km in the cold season. The average cloud top height in the warm season is around 5–8 km, while in the cold season it is mainly around 4.5 km. The average thickness of ice clouds in both seasons is around 2 km. The statistical results of microphysical characteristics show that the ice water content is around 10−1 to 103 mg/m3, and the effective radius of ice clouds is mainly in the range of 10–90 μm. Both have their highest frequency in the west of the TP and lowest in the northeast. A comprehensive analysis of the change in temperature, water vapor, and ice cloud occurrence frequency shows that the rate of increase in water vapor in the warm season is greater than that in the cold season, while the rates of increase in both surface temperature and ice cloud occurrence are smaller than in the cold season. The rate of increase in temperature in the warm season is around 0.038 °C/yr, and that in the cold season is around 0.095 °C/yr. The growth rate of thin ice clouds in the warm season is around 0.15% per year, while that in the cold season is as high as 1% per year. The results suggest that the surface temperature change may be related to the occurrence frequency of thin ice clouds, with the notable increase in temperature during the cold season possibly being associated with a significant increase in the occurrence frequency of thin ice clouds.
A Patch-Level Region-Aware Module with a Multi-Label Framework for Remote Sensing Image Captioning
Authors: Li Yunpeng , Zhang Xiangrong , Zhang Tianyang , Wang Guanchun , Wang Xinlin , Li Shuo
Published: October 2024

Recent Transformer-based works can generate high-quality captions for remote sensing images (RSIs). However, these methods generally feed global or grid visual features to a Transformer-based captioning model for associating cross-modal information, which limits performance. In this work, we investigate unexplored ideas for remote sensing image captioning task, using a novel patch-level region-aware module with a multi-label framework. Due to an overhead perspective and a significantly larger scale in RSIs, a patch-level region-aware module is designed to filter the redundant information in the RSI scene, which benefits the Transformer-based decoder by attaining improved image perception. Technically, the trainable multi-label classifier capitalizes on semantic features as supplementary to the region-aware features. Moreover, modeling the inner relations of inputs is essential for understanding the RSI. Thus, we introduce region-oriented attention, which associates region features and semantic labels, omits the irrelevant regions to highlight relevant regions, and learns related semantic information. Extensive qualitative and quantitative experimental results show the superiority of our approach on the RSICD, UCM-Captions, and Sydney-Captions. The code for our method will be publicly available.
Exploring Topological Information Beyond Persistent Homology to Detect Geospatial Objects
Authors: Syzdykbayev Meirman , Karimi Hassan A.
Published: October 2024

Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the integration of topological, geometrical, and contextual information to enhance the precision of landslide detection. Topology, a fundamental branch of mathematics, explores the properties of space that are preserved under continuous transformations and focuses on the qualitative aspects of space, studying features like connectivity and exitance of loops/holes. We employed persistent homology (PH) to derive candidate polygons and applied three distinct strategies for landslide detection: without any filters, with geometrical and contextual filters, and a combination of topological with geometrical and contextual filters. Our method was rigorously tested across five different study areas. The experimental results revealed that geometrical and contextual filters significantly improved detection accuracy, with the highest F1 scores achieved when employing these filters on candidate polygons derived from PH. Contrary to our initial hypothesis, the addition of topological information to the detection process did not yield a notable increase in accuracy, suggesting that the initial topological features extracted through PH suffices for accurate landslide characterization. This study advances the field of geospatial object detection by demonstrating the effectiveness of combining geometrical and contextual information and provides a robust framework for accurately mapping landslide susceptibility.
High-Resolution Collaborative Forward-Looking Imaging Using Distributed MIMO Arrays
Authors: Shen Shipei , Niu Xiaoli , Guo Jundong , Zhang Zhaohui , Han Song
Published: October 2024

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications.
Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations
Authors: Zhang Boya , Gann Daniel , Wdowinski Shimon , Lin Chaohao , Hestir Erin , Lamb-Wotton Lukas , Ishtiaq Khandker S. , Smith Kaleb , Li Yuepeng
Published: October 2024

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event.
The Impact of Drought on Vegetation at Basin Scale: A Case Study of the Wei River Basin, China
Authors: Zhao Panpan , Chai Qihui , Xie Bingbo , Li Hongyang , Yang Huicai , Wan Fang , Huang Xudong
Published: October 2024

Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management.
Soil and Water Assessment Tool (SWAT)-Informed Deep Learning for Streamflow Forecasting with Remote Sensing and In Situ Precipitation and Discharge Observations
Authors: Huang Chunlin , Zhang Ying , Hou Jinliang
Published: October 2024

In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the residual errors of the SWAT model, the SWAT-informed LSTM model (LSTM-SWAT) differs from typical LSTM approaches that predict the streamflow directly. Through numerical tests, the performance of the LSTM-SWAT was evaluated with both LSTM-only and SWAT-only models in the Upper Heihe River Basin. The outcomes showed that the LSTM-SWAT performed better than the other models, showing higher accuracy and a lower mean absolute error (MAE = 3.13 m3/s). Sensitivity experiments further showed how the quality of the training dataset affects the performance of the LSTM-SWAT. The results of this study demonstrate how the LSTM-SWAT may improve streamflow prediction greatly by remote sensing and in situ observations. Additionally, this study emphasizes the need for detailed consideration of specific sources of uncertainty to further improve the predictive capabilities of the hybrid model.
RSPS-SAM: A Remote Sensing Image Panoptic Segmentation Method Based on SAM
Authors: Liu Zhuoran , Li Zizhen , Liang Ying , Persello Claudio , Sun Bo , He Guangjun , Ma Lei
Published: October 2024

Satellite remote sensing images contain complex and diverse ground object information and the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of satellite remote sensing images a highly challenging task. Due to the lack of large-scale annotated datasets for panoramic segmentation, existing methods still suffer from weak model generalization capabilities. To mitigate this issue, this paper leverages the advantages of the Segment Anything Model (SAM), which can segment any object in remote sensing images without requiring any annotations and proposes a high-resolution remote sensing image panoptic segmentation method called Remote Sensing Panoptic Segmentation SAM (RSPS-SAM). Firstly, to address the problem of global information loss caused by cropping large remote sensing images for training, a Batch Attention Pyramid was designed to extract multi-scale features from remote sensing images and capture long-range contextual information between cropped patches, thereby enhancing the semantic understanding of remote sensing images. Secondly, we constructed a Mask Decoder to address the limitation of SAM requiring manual input prompts and its inability to output category information. This decoder utilized mask-based attention for mask segmentation, enabling automatic prompt generation and category prediction of segmented objects. Finally, the effectiveness of the proposed method was validated on the high-resolution remote sensing image airport scene dataset RSAPS-ASD. The results demonstrate that the proposed method achieves segmentation and recognition of foreground instances and background regions in high-resolution remote sensing images without the need for prompt input, while providing smooth segmentation boundaries with a panoptic segmentation quality (PQ) of 57.2, outperforming current mainstream methods.
Activation of Ms 6.9 Milin Earthquake on Sedongpu Disaster Chain, China with Multi-Temporal Optical Images
Authors: Xin Yubin , Zhao Chaoying , Li Bin , Liu Xiaojie , Gao Yang , Lou Jianqi
Published: October 2024

In recent years, disaster chains caused by glacier movements have occurred frequently in the lower Yarlung Tsangpo River in southwest China. However, it is still unclear whether earthquakes significantly contribute to glacier movements and disaster chains. In addition, it is difficult to measure the high-frequency and large gradient displacement time series with optical remote sensing images due to cloud coverage. To this end, we take the Sedongpu disaster chain as an example, where the Milin earthquake, with an epicenter 11 km away, occurred on 18 November 2017. Firstly, to deal with the cloud coverage problem for single optical remote sensing analysis, we employed multiple platform optical images and conducted a cross-platform correlation technique to invert the two-dimensional displacement rate and the cumulative displacement time series of the Sedongpu glacier. To reveal the correlation between earthquakes and disaster chains, we divided the optical images into three classes according to the Milin earthquake event. Lastly, to increase the accuracy and reliability, we propose two strategies for displacement monitoring, that is, a four-quadrant block registration strategy and a multi-window fusion strategy. Results show that the RMSE reduction percentage of the proposed registration method reaches 80%, and the fusion method can retrieve the large magnitude displacements and complete displacement field. Secondly, the Milin earthquake accelerated the Sedongpu glacier movement, where the pre-seismic velocities were less than 0.5 m/day, the co-seismic velocities increased to 1 to 6 m/day, and the post-seismic velocities decreased to 0.5 to 3 m/day. Lastly, the earthquake had a triggering effect around 33 days on the Sedongpu disaster chain event on 21 December 2017. The failure pattern can be summarized as ice and rock collapse in the source area, large magnitude glacier displacement in the moraine area, and a large volume of sediment in the deposition area, causing a river blockage.
Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021
Authors: Yang Mingxin , Chen Ang , Cao Wenqiang , Wang Shouxin , Xu Mingyuan , Gu Qiang , Wang Yanhe , Yang Xiuchun
Published: October 2024

Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R2 of 0.44 and an RMSE of 2.56 n/m2 on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management.
Dynamic Earthquake-Induced Landslide Susceptibility Assessment Model: Integrating Machine Learning and Remote Sensing
Authors: Yang Youtian , Wu Jidong , Wang Lili , Ya Ru , Tang Rumei
Published: October 2024

Earthquake-induced landslides (EQILs) represent a serious secondary disaster of earthquakes, and conducting an effective assessment of earthquake-induced landslide susceptibility (ELSA) post-earthquake is helpful in reducing risk. In light of the diverse demands for ELSA across different time periods following an earthquake and the growing availability of data, this paper proposes using remote sensing data to dynamically update the ELSA model. By studying the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023, rapid assessment results were derived from 12 pre-trained ELSA models combined with the spatial distribution of historical earthquake-related landslides immediately after the earthquake for early warning. Throughout the entire emergency response stage, the ELSA model was dynamically updated by integrating the EQILs points interpreted from remote sensing images as new training data to enhance assessment accuracy. After the emergency phase, the remote sensing interpretation results were compiled to create the new EQILs inventory. A high landslide potential area was identified using a re-trained model based on the updated inventory, offering a valuable reference for risk management during the recovery phase. The study highlights the importance of integrating remote sensing into ELSA model updates and recommends utilizing time-dependent remote sensing data for sampling to enhance the effectiveness of ELSA.
Adaptation of a Model Spike Aptamer for Isothermal Amplification-Based Sensing
Authors: Yurdusev Emre , Trahan Pierre-Luc , Perreault Jonathan
Published: October 2024

Isothermal amplification (IA) techniques like rolling circle amplification (RCA) and loop-mediated isothermal amplification (LAMP) have gained significant attention in recent years due to their ability to rapidly amplify DNA or RNA targets at a constant temperature without the need for complex thermal cycling equipment. Such technologies, combined with colorimetric systems rendering visual confirmation of the amplification event, are ideal for the development of point-of-need detection methods suitable for field settings where access to specialized laboratory equipment is limited. The utility of these technologies, thus far limited to DNA and RNA targets, could be broadened to a wide range of targets by using aptamers. Composed of DNA or RNA themselves, aptamers can bind to substances, including proteins, metabolites, and inorganic substances. Their nucleic acid nature can potentially allow them to serve as a bridge, extending the reach of DNA/RNA-centric technologies to the broader molecular world. Indeed, the change in aptamer conformation occurring during ligand interaction can be used to elaborate ligand-responding RCA or LAMP templates. By using an existing aptamer targeting SARS-CoV-2 Spike protein as a model, we explored the possibility of establishing ligand-responsive IA systems. Our study used aptamers with simple sequence modifications as templates in LAMP assays and hyperbranched RCA (HRCA) by exploiting the dynamic nature of the model aptamer to trigger these IA systems. Importantly, our work uniquely demonstrates that this aptamer’s dynamic response to ligand binding can regulate both RCA and LAMP processes. This novel approach of using aptamer conformational changes to trigger LAMP paves the way for new aptamer-based detection assays. Our system detects 50 nM of Spike protein, with LAMP occurring within 30 min in the presence of Spike. The colorimetric readout showed clear results, allowing for the detection of Spike protein presence.
Theories and Methods for Indoor Positioning Systems: A Comparative Analysis, Challenges, and Prospective Measures
Authors: Hailu Tesfay Gidey , Guo Xiansheng , Si Haonan , Li Lin , Zhang Yukun
Published: October 2024

In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users’ location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments.
Characterization of Defocused Coherent Imaging Systems with Periodic Objects
Authors: Massaro Gianlorenzo , D’Angelo Milena
Published: October 2024

Recent advancements in quantum and quantum-inspired imaging techniques have enabled high-resolution 3D imaging through photon correlations. These techniques exhibit reduced degradation of image resolution for out-of-focus samples compared to conventional methods (i.e., intensity-based incoherent imaging). A key advantage of these correlation-based approaches is their independence from the system numerical aperture (NA). Interestingly, both improved resolution of defocused images and NA-independent scaling are linked to the spatial coherence of light. This suggests that while correlation measurements exploit spatial coherence, they are not essential for achieving this imaging advantage. This discovery has led to the development of optical systems that achieve similar performance by using spatially coherent illumination and relying on intensity measurements: direct 3D imaging with NA-independent resolution was recently demonstrated in a correlation-free setup using LED light. Here, we explore the physics behind the enhanced performance of defocused coherent imaging, showing that it arises from the modification of the sample’s spatial harmonic content due to diffraction, unlike the blurring seen in conventional imaging. The results we present are crucial for understanding the implications of the physical differences between coherent and incoherent imaging, and are expected to pave the way for the practical application of the discovered phenomena.
Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems
Authors: Imam Niddal H.
Published: October 2024

There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted.
Two-Phase Globally Coupled Low-Density Parity Check Decoding Aided with Early Termination and Forced Convergence
Authors: Zhu Kun , Yang Hongwen
Published: October 2024

To enhance the decoding efficiency of Globally Coupled (GC) LDPC codes, we incorporated Early Termination (ET) and Forced Convergence (FC) into the local/global two-phase decoding algorithm to expedite the decoding process. The two-phase decoding scheme integrates the ET technique to halt unnecessary iterations in the local decoding phase while employing the FC technique to accelerate convergence in the global phase decoding. The application of ET technology in the local decoding of GC-LDPC codes will not cause performance loss as in traditional block codes and will cause considerable complexity gains. For a longer code length and larger convergence differences between nodes’ global codes, the FC technique operates more efficiently in global code than local code. Two variants are proposed for the ET scheme in the local decoding, namely ET-1 and ET-2. The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. In the case of ET-2, the saved local iterations are transformed to global decoding equally. The results show that ET-1 saves considerable decoding time complexity and ET-2 improves the performance of the GC-LDPC code with the same decoding time complexity. The combined approach of ET-1 with FC reduces the decoding time complexity up to 42% at a low Signal Noise Rate region while maintaining its performance; ET-2-FC two-phase decoding saves approximately 25% decoding time complexity while improving the BER by about 0.18 dB and FER by about 0.23 dB.
Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor
Authors: Fan Hao , Li Chenxi , Gao Bo , Xu Huangrong , Chen Yuwei , Zhang Xuming , Li Xu , Yu Weixing
Published: October 2024

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network “HSC3D” for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.
A Pseudo-Labeling Multi-Screening-Based Semi-Supervised Learning Method for Few-Shot Fault Diagnosis
Authors: Liu Shiya , Zhu Zheshuai , Chen Zibin , He Jun , Chen Xingda , Chen Zhiwen
Published: October 2024

In few-shot fault diagnosis tasks in which the effective label samples are scarce, the existing semi-supervised learning (SSL)-based methods have obtained impressive results. However, in industry, some low-quality label samples are hidden in the collected dataset, which can cause a serious shift in model training and lead to the performance of SSL-based method degradation. To address this issue, the latest prototypical network-based SSL techniques are studied. However, most prototypical network-based scenarios consider that each sample has the same contribution to the class prototype, which ignores the impact of individual differences. This article proposes a new SSL method based on pseudo-labeling multi-screening for few-shot bearing fault diagnosis. In the proposed work, a pseudo-labeling multi-screening strategy is explored to accurately screen the pseudo-labeling for improving the generalization ability of the prototypical network. In addition, the AdaBoost adaptation-based weighted technique is employed to obtain accurate class prototypes by clustering multiple samples, improving the performance that deteriorated by low-quality samples. Specifically, the squeeze and excitation block technique is used to enhance the useful feature information and suppress non-useful feature information for extracting accuracy features. Finally, three well-known bearing datasets are selected to verify the effectiveness of the proposed method. The experiments illustrated that our method can receive better performance than that of the state-of-the-art methods.
Upper and Lower Limb Training Evaluation System Based on Virtual Reality Technology
Authors: Zhao Jian , Gao Hanlin , Yang Chen , Kuang Zhejun , Liu Mingliang , Dang Zhuozheng , Shi Lijuan
Published: October 2024

Upper and lower limb rehabilitation training is essential for restoring patients’ physical movement ability and enhancing muscle strength and coordination. However, traditional rehabilitation training methods have limitations, such as high costs, low patient participation, and lack of real-time feedback. The purpose of this study is to design and implement a rehabilitation training evaluation system based on virtual reality to improve the quality of patients’ rehabilitation training. This paper proposes an upper and lower limb rehabilitation training evaluation system based on virtual reality technology, aiming to solve the problems existing in traditional rehabilitation training. The system provides patients with an immersive and interactive rehabilitation training environment through virtual reality technology, aiming to improve patients’ participation and rehabilitation effects. This study used Kinect 2.0 and Leap Motion sensors to capture patients’ motion data and transmit them to virtual training scenes. The system designed multiple virtual scenes specifically for different upper and lower limbs, with a focus on hand function training. Through these scenes, patients can perform various movement training, and the system will provide real-time feedback based on the accuracy of the patient’s movements. The experimental results show that patients using the system show higher participation and better rehabilitation training effects. Compared with patients receiving traditional rehabilitation training, patients using the virtual reality system have significantly improved movement accuracy and training participation. The virtual reality rehabilitation training evaluation system developed in this study improves the quality of patients’ rehabilitation and provides personalized treatment information to medical personnel through data collection and analysis, promoting the systematization and personalization of rehabilitation training. This system is innovative and has broad application potential in the field of rehabilitation medicine.
Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks
Authors: Wu Jie , Yang Shuai , Wang Xiaoming , Pei Yu , Wang Shuai , Song Congcong
Published: October 2024

In order to solve the problem that existing visible and infrared image fusion methods rely only on the original local or global information representation, which has the problem of edge blurring and non-protrusion of salient targets, this paper proposes a layered fusion method based on channel attention mechanism and improved Generative Adversarial Network (HFCA_GAN). Firstly, the infrared image and visible image are decomposed into a base layer and fine layer, respectively, by a guiding filter. Secondly, the visible light base layer is fused with the infrared image base layer by histogram mapping enhancement to improve the contour effect. Thirdly, the improved GAN algorithm is used to fuse the infrared and visible image refinement layer, and the depth transferable module and guided fusion network are added to enrich the detailed information of the fused image. Finally, the multilayer convolutional fusion network with channel attention mechanism is used to correlate the local information of the layered fusion image, and the final fusion image containing contour gradient information and useful details is obtained. TNO and RoadSence datasets are selected for training and testing. The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency.
The Development and Evaluation of Biosorbent Composite Spheres for the Adsorption and Quantification of Copper
Authors: Pérez-Silva Irma , Páez-Hernández María Elena , Martínez-Flores Francisco Javier , Tavizón-Pozos Jesús Andrés , Romero Giaan Arturo Álvarez
Published: October 2024

Separation techniques are employed to treat and preconcentrate samples. Preconcentration commonly employs adsorption due to the wide range of sorbents available. The biosorbent composite has emerged as a highly effective alternative, primarily due to its selectivity for active sites and its impressive adsorption capability. This study aimed to assess and create a spherical biosorbent composite using cellulose acetate and avocado seed. The purpose of this work was to use a biosorbent composite for copper adsorption by flame atomic absorption spectrometry. The copper adsorption follows the Langmuir isotherm, which indicates that it occurs in a monolayer and is homogeneous. Additionally, the adsorption nature is favorable according to the RL factor. The highest capacity for copper adsorption is 0.121 mg g−1. The report describes the methodology and validation process for quantifying copper. The findings demonstrate that the composite biosorbent enables accurate preconcentration and quantification of copper found in decongestants.
Solid–Liquid Phase Equilibria of the Aqueous Quaternary System Rb+, Cs+, Mg2+//SO42− - H2O at T = 323.2 K
Authors: Yu Zhangfa , Zeng Ying , Li Xuequn , Sun Hongbo , Li Longgang , He Wanghai , Chen Peijun , Yu Xudong
Published: October 2024

Sulfate-type salt lakes constitute over half of the total salt lakes in China and are rich in rare elements, such as rubidium and cesium. However, the complex interactions between ions make the separation and extraction process quite challenging. To address this, phase equilibrium studies were conducted on the sulfate system containing rubidium, cesium, and magnesium. Specifically, the phase equilibria of the aqueous quaternary system Rb+, Cs+, Mg2+//SO42− - H2O at 323.2 K were investigated using the isothermal dissolution method. The solubility, density, and refractive index of the system were experimentally measured. The results indicate that the system at 323.2 K belongs to a complex type with the formation of one solid solution (Rb, Cs)2SO4 and two double salts (Rb2SO4·MgSO4·6H2O, Cs2SO4·MgSO4·6H2O). The corresponding phase diagram consists of four quaternary invariant points, nine univariate curves, and six crystallization regions. Among these, the crystalline region for Cs2SO4·MgSO4·6H2O is the largest, while that for the single salt Cs2SO4 is the smallest. Moreover, the crystalline regions for the double salt and solid solutions are significantly larger than those for the single salt, highlighting the difficulty in separation of valuable single salts. A comparison of multi-temperature phase diagrams from 298.2 K to 323.2 K reveals that the crystalline form of MgSO4 changes from MgSO4·7H2O (298.2 K) to MgSO4·6H2O (323.2 K). As the temperature increases, the phase regions for Rb2SO4, Cs2SO4, (Rb, Cs)2SO4, and Cs2SO4·MgSO4·6H2O expand, while the phase region of Rb2SO4·MgSO4·6H2O contracts, indicating that the single salts (Rb2SO4, Cs2SO4) are more readily precipitated at higher temperature, which provides theoretical guidance for the future production and separation of Rb, Cs, and Mg from sulfate-type salt lakes.
Exploration of the Functional Constituents of the Substrate of Flammulina velutipes
Authors: Jiang Ruixiang , Zheng Renqing , Yuan Bin , Li Bo , Chen Yiyong , Zheng Yongbiao
Published: October 2024

After harvesting, the substrate of Flammulina velutipes (SFV) is repurposed for uses such as feed, fuel, and culture medium. This study identified four phenolic acids and one flavonoid from the SFV for the first time using solvent extraction and chromatography: ferulic acid (1), ρ-coumaric acid (2), vanillic acid (3), 3-methoxygallic acid (4), and tricin (5). They showed significant activity in the DPPH scavenging test with concentrations determined by HPLC as follows: ferulic acid (218.88 mg/kg), ρ-coumaric acid (157.31 mg/kg), vanillic acid (281.54 mg/kg), 3-methoxygallic acid (33.85 mg/kg), and tricin (713.42 mg/kg). These findings indicate that the SFV is a valuable source of bioactive compounds for medicinal and health-promoting applications.
Exploring Urban Socio-Spatial Disparities in Liverpool, Manchester, and Newcastle upon Tyne Metropolitan Areas
Authors: De Falco Antonio
Published: October 2024

In recent decades, rising social disparities in European cities have structured new forms of urban inequalities. Employing an innovative analysis approach based on statistical, geographical, and spatial methods, this paper aims to explore the social geography of population groups in Liverpool, Manchester, and Newcastle upon Tyne by examining how the spatial distribution of socio-economic groups intersects with urban settlements of the resident foreign population. The study addresses methodological challenges in segregation measurement while shedding light on existing spatial patterns of vulnerable groups and emerging trajectories of residential segregation at the metropolitan scale, utilising the most recent UK Census data.
Exploring the Impact of Values Education on Sustainable Environmental Awareness and Behavior Among Eighth-Grade Students
Authors: Kalaycı Alas Dilan , Korutürk Kurthan
Published: October 2024

Education plays a crucial role in fostering numerous established and sustainable behaviors in individuals. Among these behaviors, environmental awareness is particularly important because of its significant sociological impact. The aim of this study is to investigate the effect of values education on environmental awareness and pro-environmental behavior. This study employs a mixed-method approach, combining both quantitative and qualitative research methods. The quantitative part of this research consists of 225 eighth-grade students and the qualitative part consists of six Turkish teachers. As a result of the quantitative analysis, it was found that the environmental attitude levels of the middle school students participating in this study were at a high level. In the qualitative analysis, it was found that the teachers participating in this study established a strong connection between mother tongue education, values education, and environmental education. The outcomes from the qualitative and quantitative analysis were discussed in depth and compared with the results previously presented in the literature. Additionally, the findings of this study indicated opportunities for future research as presented in the Discussion Section.
Assessing the Prospects and Risks of Delivering Sustainable Urban Development Through 3D Concrete Printing Implementation
Authors: Adamtsevich Liubov , Pustovgar Andrey , Adamtsevich Aleksey
Published: October 2024

The article presents the results of a comprehensive study of the use of 3D Concrete printing (3DCP) technology to create urban infrastructure facilities according to sustainable development principles. The work includes a study of scientific articles on the subject area under consideration, a survey of additive construction market participants, as well as an analysis and generalization of promising areas for technology development and methods for improving the quality of objects erected using 3DCP. As part of the conducted literature review, publications included in the Scopus database for the period 2015–2024 were selected for analysis using the keywords ‘Sustainable development + 3DCP’ and ‘Sustainable construction + 3DCP’. The following conclusions were made: (i) the most popular publications are review articles about the development of materials and technologies for 3DCP and (ii) the most sought-after are the studies in the field of partial application of 3DCP technology, existing equipment and materials for 3DCP, and assessment of the effectiveness and cost-effectiveness of 3DCP use. For this purpose, a questionnaire was developed consisting of three blocks: equipment and technologies; structures and materials for 3DCP; the ecology and economics of 3DCP applicability. As a result, four main risks have been identified, which represent promising areas for 3DCP development.