bluuebunny/crossref_metadata_2025 huggingface dataset:
Leukocyte-Platelet-Rich Fibrin in Bone Regeneration after Periapical Surgery: A 30-Month Follow-Up Clinical Report
Authors: Qurban Hatim A. , Hamadallah Hatem Hazzaa , Madkhaly Mohammad A. , Hakeem Muhannad M. , Alqutaibi Ahmed Yaseen
Published: April 2024

Aim and background: Periapical lesions, which occur due to the infection and necrosis of dental pulp, are a significant dental pathology that poses risks to oral and systemic health. These lesions often require interventions such as root canal treatment or periapical surgery. Recent research has focused on the effectiveness of biocompatible materials, including mineral trioxide aggregate, bioceramics, and leukocyte-platelet-rich fibrin (L’PRF), in improving healing outcomes. This report presents the application of leukocyte-platelet-rich fibrin (L’PRF) derived from the patient’s autologous blood to enhance bone healing. Case description: A 61-year-old woman with well-controlled hypertension and good oral hygiene visited the dental clinic due to a painless swelling near her upper left central incisor. After examination, it was determined that she had a periapical granuloma. The patient underwent successful root canal retreatment and apical surgery, during which leukocyte-platelet-rich fibrin was applied. After 30 months, she experienced significant improvement with no symptoms and substantial bone regeneration. Conclusion: Clinical evidence and this case study indicate that leukocyte-platelet-rich fibrin (L’PRF) may enhance healing post periapical surgery. Further research, including more extensive and longer-term randomized trials, must confirm L’PRF’s effectiveness and refine treatment protocols. Clinical significance: L’PRF enhances bone healing post periapical surgery. Clinicians should consider integrating L’PRF in periapical surgeries, ensure diligent follow-up, and inform patients of its long-term advantages. Further randomized trials are needed to refine L’PRF clinical guidelines.
LVI-Fusion: A Robust Lidar-Visual-Inertial SLAM Scheme
Authors: Liu Zhenbin , Li Zengke , Liu Ao , Shao Kefan , Guo Qiang , Wang Chuanhao
Published: April 2024

With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness and accuracy. In this study, a localization and mapping scheme named LVI-fusion based on multi-sensor fusion of camera, lidar and IMU is proposed. Different sensors have different data acquisition frequencies. To solve the problem of time inconsistency in heterogeneous sensor data tight coupling, the time alignment module is used to align the time stamp between the lidar, camera and IMU. The image segmentation algorithm is used to segment the dynamic target of the image and extract the static key points. At the same time, the optical flow tracking based on the static key points are carried out and a robust feature point depth recovery model is proposed to realize the robust estimation of feature point depth. Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness.
Hierarchical Semantic-Guided Contextual Structure-Aware Network for Spectral Satellite Image Dehazing
Authors: Yang Lei , Cao Jianzhong , Wang Hua , Dong Sen , Ning Hailong
Published: April 2024

Haze or cloud always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for spectral satellite image dehazing. Specifically, a hybrid CNN–Transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrated that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.
Reduction of Subsidence and Large-Scale Rebound in the Beijing Plain after Anthropogenic Water Transfer and Ecological Recharge of Groundwater: Evidence from Long Time-Series Satellites InSAR
Authors: Zhou Chaodong , Tang Qiuhong , Zhao Yanhui , Warner Timothy A. , Liu Hongjiang , Clague John J.
Published: April 2024

Beijing, China’s capital city, has experienced decades of severe land subsidence due to the long-term overexploitation of groundwater. The implementation of the South-to-North Water Diversion Project (SNWDP) and artificial ecological restoration have significantly changed Beijing’s hydro-ecological and geological environment in recent years, leading to a widespread rise in groundwater levels. However, whether the related land subsidence has slowed down or reversed under these measures has not yet been effectively monitored and quantitatively analyzed in terms of time and space. Accordingly, in this study, we proposed using an improved time-series deformation method, which combines persistent scatterers and distributed scatterers, to process Sentinel-1 images from 2015 to 2022 in the Beijing Plain region. We performed a geospatial analysis to gain a better understanding of how the new hydrological conditions changed the pattern of deformation on the Beijing Plain. The results indicated that our combined PS and DS method provided more measurements both in total quantity and spatial density than conventional PSI methods. The land subsidence in the Beijing Plain area has been effectively alleviated from a subsidence region of approximately 1377 km2 in 2015 to only approximately 78 km2 in 2022. Ecological restoration areas in the northeastern part of the Plain have even rebounded over this period, at a maximum of approximately 40 mm in 2022. The overall pattern of ground deformation (subsidence and uplift) is negatively correlated with changes in the groundwater table (decline and rise). Local deformation is controlled by the thickness of the compressible layer and an active fault. The year 2015, when anthropogenic water transfers were eliminated and ecological measures to recharge groundwater were implemented, was the crucial turning point of the change in the deformation trend in the subsidence history of Beijing. Our findings carry significance, not only for China, but also for other areas where large-scale groundwater extractions are causing severe ground subsidence or rebound.
BLEI: Research on a Novel Remote Sensing Bare Land Extraction Index
Authors: He Chaokang , Wang Qinjun , Yang Jingyi , Xu Wentao , Yuan Boqi
Published: April 2024

Bare land, as a significant land cover type on the Earth’s surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth’s surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning.
Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching
Authors: Li Lanyu , Wang Yachao , Wang Bingnan , Xiang Maosheng
Published: April 2024

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).
Aboveground Biomass Inversion Based on Object-Oriented Classification and Pearson–mRMR–Machine Learning Model
Authors: Chen Xinyang , Yang Keming , Ma Jun , Jiang Kegui , Gu Xinru , Peng Lishun
Published: April 2024

Cities play a crucial role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essential for evaluating carbon sequestration. Satellite remote sensing enables large-scale AGB inversion. However, the apparent differences between forest and grassland biomass pose a significant challenge to the accurate estimation of urban AGB using satellite-based data. To address this limitation, this study proposed a novel AGB estimation method using the eastern part of the Zhahe mining area in Huaibei City as the study area, which integrates land cover classification, feature selection, and machine learning modelling to generate high quality biomass maps of different vegetation types in an urban area with complex feature distribution. Utilizing the GEE platform and Sentinel-2 image, we developed an object-oriented machine learning classification algorithm, combining SNIC and GLCM to extract vegetation information. Optimal feature variables for forest and crop-grass AGB inversion were selected using the Pearson–mRMR algorithm. Finally, we constructed nine machine learning models for AGB inversion and selected the model with the highest accuracy to generate the AGB map of the study area. The results of the study are as follows: (1) Compared with the pixel-based classification method, the object-oriented classification method can extract the boundaries of different vegetation types more accurately. (2) Forest AGB is strongly correlated with vegetation indices and physiological parameters, while agri-grass AGB is primarily associated with vegetation indices and vegetation physiological parameters. (3) For forest AGB modelling, the RF-R model outperforms other machine learning models with an R2 of 0.77. For agri-grass AGB modelling, the XGBoost-R model is more accurate, with an R2 of 0.86. (4) The mean forest AGB in the study area was 4.60 kg/m2, while the mean agri-grass AGB was 0.71 kg/m2. High AGB values were predominantly observed in forested areas, which were mainly distributed along roads, waterways, and mountain ranges. Overall, this study contributes to a better understanding of the health of local urban ecosystems and provides valuable insights for ecosystem protection and the sustainable use of natural resources.
A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices
Authors: Fan Jiahui , Yao Yunjun , Tang Qingxin , Zhang Xueyi , Xu Jia , Yu Ruiyang , Liu Lu , Xie Zijing , Ning Jing , Zhang Luna
Published: April 2024

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.
Quantitative Research on the Morphological Characteristics of Lunar Impact Craters of Different Stratigraphic Ages since the Imbrian Period
Authors: Zhang Ke , Liu Jianzhong , Zhang Li , Gu Yaya , Yue Zongyu , Zhang Sheng , Zhang Jingyi , Qin Huibin , Liu Jingwen
Published: April 2024

Impact craters serve as recorders of lunar evolutionary history, and determining the stratigraphic ages of craters is crucial. However, the age of many craters on the Moon remains undetermined. The morphology of craters is closely related to their stratigraphic ages. In the study, we systematically and quantitatively analyzed seven morphological parameters of 432 impact craters with known stratigraphic ages (Copernican, Eratosthenian, Imbrian), including crater depth, wall width, wall height, rim height, irregularity, volume, and roughness, as well as rock abundance. The study provided a range of morphological parameters for craters from the Copernican, Eratosthenian, and Imbrian. Additionally, we derived power law relationships between five morphological parameters and crater diameter, excluding irregularity and roughness. Furthermore, the transitional crater diameters from simple to complex crater morphology were determined for the Copernican and Eratosthenian, approximately 13 km and 15 km, respectively. These results suggest systematic differences in the lunar regolith in different stratigraphic ages. For impact craters of the same diameter, as crater age increases, irregularity tends to be greater, while crater depth, wall width, wall height, rim height, volume, roughness, and rock abundance tend to be smaller. Therefore, in cases where the diameter is determined, the actual values of morphological parameters and rock abundance can be used to constrain the stratigraphic age information of craters of an unknown age.
Understanding the Spatiotemporal Dynamics and Influencing Factors of the Rice–Crayfish Field in Jianghan Plain, China
Authors: Luo Fang , Zhang Yiqing , Zhao Xiang
Published: April 2024

The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF’s spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries.
A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations
Authors: Jiao Zhonghu , Shan Xinjian
Published: April 2024

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies.
Channel Prediction for Underwater Acoustic Communication: A Review and Performance Evaluation of Algorithms
Authors: Liu Haotian , Ma Lu , Wang Zhaohui , Qiao Gang
Published: April 2024

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.
Analysis of the Low-Frequency Debris Flow Disaster Induced by a Local Rainstorm on 12 July 2022, in Pingwu County, China
Authors: Liu Mei , Deng Mingfeng , Chen Ningsheng , Tian Shufeng , Wang Tao
Published: April 2024

Low-frequency debris flows often lead to severe disasters due to large energy releases and strong concealment. However, the understanding of formation conditions, movement processes, and disaster-causing mechanisms of low-frequency debris flow is still limited, especially regarding occurrences within the large catchment (>50 km2). This study presents a typical case of large-scale, low-frequency debris flow occurring in the Heishui catchment (102.65 km2), Pingwu County, China. The movement process, disaster characteristics, and causes of the Heishui debris flow were analyzed in detail through field investigations and remote sensing interpretation. The results indicated that the Heishui debris flow is a large-scale, low-frequency, dilute debris flow with a recurrence period of over 100 years. The debris flow was primarily initiated from the right branch gully, Longchi gully, triggered by a local rainstorm with a maximum hourly rainfall return period of over 20 years. The main cause of casualties and building damage is attributed to large energy releases from boulder blockages and outbursts that occurred in the middle part of the main channel. This led to a sudden increase in peak discharge to 1287 m3/s, with a volume of 3.5 × 105 m3 of solid materials being transported to the outlet of the gully. It is essential to enhance the identification of debris flows by comprehensively considering tributary gullies’ susceptibility and strengthening joint meteorological and hydrological monitoring networks in the middle and upper reaches within large catchments. This preliminary work contributes towards improving prevention and mitigation strategies for low-frequency debris flows occurring within large catchments.
Overview and Analysis of Ground Subsidence along China’s Urban Subway Network Based on Synthetic Aperture Radar Interferometry
Authors: Wang Shunyao , Chen Zhenwei , Zhang Guo , Xu Zixing , Liu Yutao , Yuan Yuan
Published: April 2024

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations’ data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city’s gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China.
The Application of Piecewise Regularization Reconstruction to the Calibration of Strain Beams
Authors: Liu Jingjing , Jiang Wensong , Luo Zai , Zhang Penghao , Yang Li , Cheng Yinbao , Bian Dian , Li Yaru
Published: April 2024

Standard beams are mainly used for the calibration of strain sensors using their load reconstruction models. However, as an ill-posed inverse problem, the solution to these models often fails to converge, especially when dealing with dynamic loads of different frequencies. To overcome this problem, a piecewise Tikhonov regularization method (PTR) is proposed to reconstruct dynamic loads. The transfer function matrix is built both using the denoised excitations and the corresponding responses. After singular value decomposition (SVD), the singular values are divided into submatrices of different sizes by utilizing a piecewise function. The regularization parameters are solved by optimizing the piecewise submatrices. The experimental result shows that the MREs of the PTR method are 6.20% at 70 Hz and 5.86% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits MREs of 28.44% and 29.61% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates MREs of 29.98% and 18.42% at the same frequencies. Furthermore, the PREs of the PTR method are 3.54% at 70 Hz and 3.73% at 80 Hz. The traditional Tikhonov regularization method based on GCV exhibits PREs of 27.01% and 26.88% at frequencies of 70 Hz and 80 Hz, respectively, whereas the L-curve-based approach demonstrates PREs of 29.50% and 15.56% at the same frequencies. All in all, the method proposed in this paper can be extensively applied to load reconstruction across different frequencies.
Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET
Authors: Kim Jin-Woo , Kim Jae-Wan , Lee Jaeho
Published: April 2024

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network’s backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays.
Research on Online Monitoring Technology and Filtration Process of Inclusions in Aluminum Melt
Authors: Wu Yunfei , Yan Hao , Wang Jiahao , Zheng Jincan , Na Xianzhao , Wang Xiaodong
Published: April 2024

Online monitoring and real-time feedback on inclusions in molten metal are essential for metal quality control. However, existing methods for detecting aluminum melt inclusions face challenges, including interference, prolonged processing times, and latency. This paper presents the design and development of an online monitoring system for molten metal inclusions. Initially, the system facilitates real-time adjustment of signal acquisition parameters through a multiplexer. Subsequently, it employs a detection algorithm capable of swiftly extracting pulse peaks, with this task integrated into our proprietary host computer software to ensure timely detection and data visualization. Ultimately, we developed a monitoring device integrated with this online monitoring system, enabling the online monitoring of the aluminum alloy filtration process. Our findings indicate that the system can accurately measure the size and concentration of inclusions during the filtration process in real time, offering enhanced detection speed and stability compared to the industrial LiMCA CM (liquid metal cleanliness analyzer continuous monitoring) standard. Furthermore, our evaluation of the filtration process demonstrates that the effectiveness of filtration significantly improves with the increase in inclusion sizes, and the synergistic effect of combining CFF (ceramic foam filter) and MCF (metallics cartridge filter) filtration methods exceeds the performance of the CFF method alone. This system thus provides valuable technical support for optimizing filtration processes and controlling inclusion quality.
Concrete Highway Crack Detection Based on Visible Light and Infrared Silicate Spectrum Image Fusion
Authors: Xing Jian , Liu Ying , Zhang Guangzhu
Published: April 2024

Cracks provide the earliest and most immediate visual response to structural deterioration of asphalt pavements. Most of the current methods for crack detection are based on visible light sensors and convolutional neural networks. However, such an approach obviously limits the detection to daytime and good lighting conditions. Therefore, this paper proposes a crack detection technique cross-modal feature alignment of YOLOV5 based on visible and infrared images. The infrared spectrum characteristics of silicate concrete can be an important supplement. The adaptive illumination-aware weight generation module is introduced to compute illumination probability to guide the training of the fusion network. In order to alleviate the problem of weak alignment of the multi-scale feature map, the FA-BIFPN feature pyramid module is proposed. The parallel structure of a dual backbone network takes 40% less time to train than a single backbone network. As determined through validation on FLIR, LLVIP, and VEDAI bimodal datasets, the fused images have more stable performance compared to the visible images. In addition, the detector proposed in this paper surpasses the current advanced YOLOV5 unimodal detector and CFT cross-modal fusion module. In the publicly available bimodal road crack dataset, our method is able to detect cracks of 5 pixels with 98.3% accuracy under weak illumination.
Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure
Authors: Youssef Hamdy M. , Osman Radwa Ahmed , El-Bary Alaa A.
Published: April 2024

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system’s robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.
Multibranch Wavelet-Based Network for Image Demoiréing
Authors: Yeh Chia-Hung , Lo Chen , He Cheng-Han
Published: April 2024

Moiré patterns caused by aliasing between the camera’s sensor and the monitor can severely degrade image quality. Image demoiréing is a multi-task image restoration method that includes texture and color restoration. This paper proposes a new multibranch wavelet-based image demoiréing network (MBWDN) for moiré pattern removal. Moiré images are separated into sub-band images using wavelet decomposition, and demoiréing can be achieved using the different learning strategies of two networks: moiré removal network (MRN) and detail-enhanced moiré removal network (DMRN). MRN removes moiré patterns from low-frequency images while preserving the structure of smooth areas. DMRN simultaneously removes high-frequency moiré patterns and enhances fine details in images. Wavelet decomposition is used to replace traditional upsampling, and max pooling effectively increases the receptive field of the network without losing the spatial information. Through decomposing the moiré image into different levels using wavelet transform, the feature learning results of each branch can be fully preserved and fed into the next branch; therefore, possible distortions in the recovered image are avoided. Thanks to the separation of high- and low-frequency images during feature training, the proposed two networks achieve impressive moiré removal effects. Based on extensive experiments conducted using public datasets, the proposed method shows good demoiréing validity both quantitatively and qualitatively when compared with the state-of-the-art approaches.
Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic
Authors: Correa Ricardo , Farias Gonzalo , Fabregas Ernesto , Dormido-Canto Sebastián , Pastor Ignacio , Vega Jesus
Published: April 2024

Nuclear fusion is a potential source of energy that could supply the growing needs of the world population for millions of years. Several experimental thermonuclear fusion devices try to understand and control the nuclear fusion process. A very interesting diagnostic called Thomson scattering (TS) is performed in the Spanish fusion device TJ-II. This diagnostic takes images to measure the temperature and density profiles of the plasma, which is heated to very high temperatures to produce fusion plasma. Each image captures spectra of laser light scattered by the plasma under different conditions. Unfortunately, some images are corrupted by noise called stray light that affects the measurement of the profiles. In this work, we propose the use of deep learning models to reduce the stray light that appears in the diagnostic. The proposed approach utilizes a Pix2Pix neural network, which is an image-to-image translation based on a generative adversarial network (GAN). This network learns to translateimages affected by stray light to images without stray light. This allows for the effective removal of the noise that affects the measurements of the TS diagnostic, avoiding the need for manual image processing adjustments. The proposed method shows a better performance, reducing the noise up to 98% inimages, which surpassesprevious works that obtained 85% for the validation dataset.
Identifying Characteristic Fire Properties with Stationary and Non-Stationary Fire Alarm Systems
Authors: Wiśnios Michał , Tatko Sebastian , Mazur Michał , Paś Jacek , Łukasiak Jarosław Mateusz , Klimczak Tomasz
Published: April 2024

The article reviews issues associated with the operation of stationary and non-stationary electronic fire alarm systems (FASs). These systems are employed for the fire protection of selected buildings (stationary) or to monitor vast areas, e.g., forests, airports, logistics hubs, etc. (non-stationary). An FAS is operated under various environmental conditions, indoor and outdoor, favourable or unfavourable to the operation process. Therefore, an FAS has to exhibit a reliable structure in terms of power supply and operation. To this end, the paper discusses a representative FAS monitoring a facility and presents basic tactical and technical assumptions for a non-stationary system. The authors reviewed fire detection methods in terms of fire characteristic values (FCVs) impacting detector sensors. Another part of the article focuses on false alarm causes. Assumptions behind the use of unmanned aerial vehicles (UAVs) with visible-range cameras (e.g., Aviotec) and thermal imaging were presented for non-stationary FASs. The FAS operation process model was defined and a computer simulation related to its operation was conducted. Analysing the FAS operation process in the form of models and graphs, and the conducted computer simulation enabled conclusions to be drawn. They may be applied for the design, ongoing maintenance and operation of an FAS. As part of the paper, the authors conducted a reliability analysis of a selected FAS based on the original performance tests of an actual system in operation. They formulated basic technical and tactical requirements applicable to stationary and mobile FASs detecting the so-called vast fires.
Occupational Safety Assessment for Surface Mine Systems: The Case in Jordan
Authors: Khrais Samir K. , Yared Tamer Elia , Saifan Noor Majid , Al-Hawari Tarek H. , Dweiri Fikri
Published: April 2024

Surface mining is one of the hazardous industries that have several risky operations, including transportation, treatment, and mineral extraction. To avoid the risk of disaster, it is important to evaluate safety procedures and determine expected hazards. The aim of this study is to develop a thorough safety evaluation model for the surface mining industry based on the analytic hierarchy process (AHP), one important multi-criteria decision-making approach. A total of 11 criteria and 36 sub-criteria that are both independent and homogeneous were involved in the decision problem. Further, a deep sensitivity analysis was conducted to assess the stability of the ranking preference. The findings indicate that four out of the eleven criteria are particularly significant. To test the model’s applicability and effectiveness, a case study was conducted involving three surface mining companies located in the north of Jordan. The results demonstrate that the model is reliable, applicable, and effective in addressing real-world problems.
Comparative Kinetic Analysis of Triclosan Degradation under UV-C and Simulated Solar Irradiation
Authors: González-Fernández Lázaro Adrián , Chems Myriam , Medellín-Castillo Nahum Andrés , Castillo-Ramos Ventura , Sánchez-Polo Manuel , Vilasó-Cadre Javier E. , Ocampo-Pérez Raúl
Published: April 2024

This research delves deeply into the intricate degradation kinetics of triclosan, employing two distinct methodologies: UV and simulated solar irradiation. Through a comprehensive comparative analysis, the study endeavors to elucidate the efficacy of these techniques, aiming to shed light on their respective methodological strengths and limitations. The study compares the efficacy of UV and simulated solar irradiation techniques for triclosan degradation, revealing that both methods exhibit effectiveness in degrading triclosan, with variations observed in degradation rates and byproduct formation. Through a detailed examination of the kinetics of triclosan degradation, the study reveals the intricate pathways and mechanisms involved in the photodegradation process. Results highlight the influence of irradiance levels and residence times on degradation efficiency. The research identifies optimal conditions for triclosan degradation, emphasizing the importance of residence time and irradiance levels. Results show that a residence time of 4 h and an irradiance level of 450 W m−2 maximize degradation efficiency. Analysis of degradation byproducts provides insights into the transformation pathways of triclosan under UV and simulated solar irradiation, indicating the formation of 2,4-dichlorophenol, quinone, and hydroquinone as primary byproducts.
Robust Enhancement of Direct Air Capture of CO2 Efficiency Using Micro-Sized Anion Exchange Resin Particles
Authors: Liu Shuohan , Hu Junqiang , Zhang Fan , Zhu Jianzhong , Shi Xiaoyang , Wang Lei
Published: April 2024

In the quest to mitigate carbon dioxide emissions, it becomes essential to address the existing atmospheric CO2. Effective and economical methodologies, particularly those without additional energy consumption, are crucial. Currently, a leading method is the direct capture of CO2 using ion exchange resins, which achieve the adsorption and desorption of carbon dioxide simply by using the humidity variations. This technology, though minimizing additional energy cost, still needs improvement in its efficiency in CO2 capture capacity and compared to other methods. In this work, we develop low-cost techniques to reduce the AmberLite™ IRA900 Cl (IRA-900) anion exchange resin to micro size, and observe significant performance enhancement on CO2 capture efficiency contingent on reducing the particle diameters. This performance disparity is attributed to the differential water adsorption capacities inherent in particles of diverse diameters. Our results reveal that smaller resin particles outperform their larger counterparts, exhibiting accelerated adsorption rates and expedited transitions from wet to dry states. Notably, these smaller particles display a quintupled enhancement in adsorption efficacy relative to non-treated particles and a marked increase in relative adsorption capacity. Upon treatment, IRA-900 demonstrates robust CO2 processing efficiency, achieving a peak adsorption rate of 1.28 g/mol·h and a maximum desorption rate of 1.18 g/mol·h. Also, the material is subjected to almost 100 cycles of testing, and even after 100 cycles, the resin particles maintain a capacity of 100%. Moreover, our material can be fully regenerated to 100% efficiency by simply immersing it in water. Simultaneously, storing it in water allows for the long-term maintenance of its performance without other treatment methods. A key observation is the resin’s sustained performance stability post extended exposure to humid conditions. These outcomes offer substantial practical implications, emphasizing the relevance of our study in practical environmental applications.
The Basics of Home Office (Re)institutionalisation from the Perspective of Experiences from the COVID-19 Era
Authors: Rymaniak Janusz , Davidavičienė Vida , Lis Katarzyna
Published: April 2024

After COVID-19, the dynamics of home office development slowed down. According to the experiences of major companies, the problem is “fluctuating”. Research from the pandemic era covers various aspects and fails to present the concept of synthetic developmental conditions. Therefore, the issue of extending the organisation to home spaces was undertaken, identifying the determinants of the sustainable development of workplaces and organisations. The key is to create sustainable work systems, i.e., adapted to the environment, while being creative, critical, and productive. This study aims to establish the cultural determinants of home office development based on empirical research from the perspective of neo-institutionalism. For this purpose, the institutional logics were subject to operationalisation and the co-dependencies of metalogics with the types of organisational cultures assumed in the Cameron–Quinn OCAI model were established. The information was collected in a CAWI survey. The presented research procedure constitutes a methodical innovation. The results demonstrate a trend of changes in the dominating types of organisational culture from market control to cooperation and creation with a stimulating role of professional logics (occupation, organisation) from the perspective of working from home which brings sustainable management and business models. There is a subtle role of home logic and low employee readiness level. The adapted cultural approach demonstrates that increasing effectiveness of home office solutions requires development of integrated practices, which consider this type of work as separate and specialised. The past corporate failures and spontaneous methods cannot serve as grounds for strategies (long-term solutions). However, the research demonstrates a low level and limited range of employee impact on the organisational culture. It also indicates the need for additional analyses to determine the concept of designing workplaces in the Sustainability–Harmonisation trajectory.