bluuebunny/crossref_metadata_2025 huggingface dataset:
Angle-Independent Color Change in Thermoresponsive Gel-Immobilized Colloidal Amorphous Film Attached to PET Substrate
Authors: Nakagawa Sato , Hirano Yuna , Tanaka Mikako , Kanai Toshimitsu
Published: December 2023

Gel-immobilized colloidal amorphous structures comprise short-range-ordered monodisperse submicrometer particles embedded into a soft polymer gel. They exhibit an angle-independent structural color that is tunable in response to external stimuli via a volume change in the gel, which has significant potential for the development of sensors that respond to stimuli via angle-independent color changes. In this study, the amorphous structure of a charged colloidal suspension in water was immobilized in a thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) gel film and simultaneously attached to a polyethylene terephthalate (PET) substrate. The gel film exhibited a uniform angle-independent color that changed in response to changes in temperature (i.e., thermosensitivity). Attachment to the PET substrate suppressed changes in the gel film area and film distortion, despite significant volume changes in the gel. Consequently, the degree of thermosensitivity was enhanced. The PET-attached gel-immobilized colloidal amorphous film was easy to handle and had excellent flexibility, allowing it to wrap around the surfaces of curved objects. These features are advantageous for sensor applications.
Epoxy (Meth)acrylate-Based Thermally and UV Initiated Curable Coating Systems
Authors: Bednarczyk Paulina , Walkowiak Konrad , Irska Izabela
Published: December 2023

Recently, photocurable coatings are being used frequently. However, it is worth mentioning that the use of photopolymerization has its drawbacks, especially in the case of curing coatings on three-dimensional surfaces and in places that are difficult to access for UV radiation. However, it is possible to develop a system in which UV technology and thermal methods for curing coatings can be combined. Moreover, the obtained resins are derived from low-viscosity epoxy resins or diglycidyl ethers, making them an ideal building material for photopolymerization-based three-dimensional printing techniques. Due to the need to improve this method, a series of epoxy (meth)acrylates containing both epoxy and (meth)acrylate groups were obtained via the addition of acrylic or methacrylic acid to epoxy resin, diglycydylether of bisphenol A epoxy resin (DGEBA), cyclohexane dimethanol diglycidyl ether (CHDMDE) and neopentyl glycol diglycidyl ether (NPDE). The structures of the synthesized copolymers were confirmed through spectroscopic analysis (FTIR) and studied regarding their nonvolatile matter content (NV) and acid values (PAVs), as well as their epoxy equivalent values (EEs). Due to the presence of both epoxy and double carbon–carbon pendant groups, two distinct mechanisms can be applied: cationic and radical. Hence, the obtained resins can be cured using UV radiation with thermally appropriate conditions and initiators. This type of method can be used as a solution to many problems currently encountered in using UV technology, such as failure to cure coatings in underexposed areas as well as deformation of coatings. Synthesized epoxy (meth)acrylate prepolymers were employed to formulate photocurable coating compositions. Furthermore, the curing process and properties of cured coatings were investigated regarding some structural factors and parameters. Among the synthesized materials, the most promising are those based on epoxy resin, characterized by their high glass transition temperature values and satisfactory functional properties.
Effect of Injection Parameters on the MRI and Dielectric Properties of Condensation-Cured Silicone
Authors: Cristant Conor , Kolasangiani Kamal , Sadanand Siddharth , Bougherara Habiba , Sussman Dafna
Published: December 2023

Phantoms with tissue-mimicking properties play a crucial role in the calibration of medical imaging modalities, including Magnetic Resonance Imaging (MRI). Among these phantoms, silicone-based ones are widely used due to their long-term stability in MR imaging. Most of these phantoms are manufactured using traditional pour-mold techniques which often result in the production of air bubbles that can damage the phantom. This research investigates the feasibility of utilizing extrusion techniques to fabricate silicone phantoms and explores the effects of extrusion parameters including plunger speed and nozzle diameter on void content, T1 and T2 relaxation times, and dielectric properties. A custom double-syringe silicone extrusion apparatus was developed to prepare the silicone samples. The void content, relaxometry, and dielectric properties of extruded samples were measured and compared with traditional poured samples. The results show that extrusion parameters can affect the void content of the silicone samples. The presence of voids in the samples resulted in lower T1 values, indicating an inverse relationship between void content and relaxometry. This study demonstrates the potential of extrusion techniques for manufacturing silicone phantoms with reduced air bubble formation and provides valuable insights into the relationship between extrusion parameters and phantom properties.
Extraction of Cellulose from Ulva lactuca Algae and Its Use for Membrane Synthesis
Authors: Patrichi Claudia Ana Maria , Cioroiu Tirpan Doinita Roxana , Aljanabi Ali A. Abbas , Trica Bogdan , Gifu Ioana Catalina , Dobre Tanase
Published: December 2023

Green algae are a sustainable source of biopolymers for the global demand due to their high photosynthetic efficiency. This article describes the extraction of cellulose from plant systems represented by Ulva lactuca species. In order to extract various substances, algae were finely ground with the help of solvents (liquid media). This was carried out to achieve the necessary conditions that help reduce the resistance this phase shows in regard to the transport and transfer of the species being extracted. The highest yield of extracted cellulose (20,944%) was obtained for the following factors: S/L = 1/20; conc. ethanol = 90%, conc. salts = 4 g/L. Hydrogel membranes are a unique class of macromolecular networks that contain a large fraction of aqueous solvent within their structure. With the cellulose extracted from algae, we obtained membranes which underwent the process of swelling in liquid media (ethyl alcohol) of different concentrations. The swelling of biocellulose membranes in alcoholic solutions of high concentrations was investigated. It was observed that the process of absorption of the alcoholic solution by the membrane occurred rapidly in the first part. After stabilization, the membranes continued to absorb at a slower rate until stabilization or saturation concentration was reached.
“Let’s Propagate the Dharma”: A Critical Survey of the Activities of the Jogye Order of Korean Buddhism’s Seventh Dharma Propagation Bureau
Authors: Park Cheonghwan , Kim Kyungrae
Published: December 2023

The Jogye Order has been facing a deepening crisis since the turn of the millennium. The rapid decline in membership had been compounded by a growing loss of confidence in the order’s monastic leadership following a succession of scandals in the 1990s and 2010s involving sectarian infighting and high-level corruption. While the practice of “Dharma propagation” (pogyo) has been critical to Korean Buddhism’s revival over the twentieth century, the Jogye Order’s steadily worsening membership crisis has revitalized institutional interest in Dharma propagation. With the independent establishment of its “Dharma Propagation Bureau” (Pogyowon) in 1994, the order has steadily increased its financial and practical support of a diversity of propagation efforts as, over recent decades, the order knows that its long-term survival might very well depend on these efforts’ success. Given the crucial nature of the Jogye Order’s current propagation efforts, this article will conduct a critical examination of the recent history of the JO’s Dharma Propagation Bureau, with a particular focus on the Bureau’s activities under its seventh director, Ven. Jihong (in office 2016 through 2021).
Unveiling Hangzhou’s Intellectual Legacy: Chinese Buddhist Reference Works and Knowledge Production in the Song and Beyond
Authors: Baskind James Matthew , Wu Jinhui
Published: December 2023

This article explores Hangzhou’s multifaceted role in shaping Chinese Buddhist culture and contributing to knowledge production. As a vital hub of Chinese material and intellectual culture, Hangzhou’s significant contributions to Buddhism are emphasized, shedding light on its key role in disseminating Buddhist teachings and preserving knowledge. The study delves into the rich history of Buddhist reference works, particularly leishu, showcasing how these compilations were pivotal in organizing and transmitting Buddhist wisdom. The article connects Hangzhou’s intellectual legacy to the broader context of Chinese Buddhism, emphasizing its crucial position in the development and dissemination of Buddhist doctrines. Additionally, it highlights ongoing academic efforts to compile an Encyclopedia of Hangzhou Buddhist Culture, underscoring Hangzhou’s continued importance in contemporary Buddhist scholarship.
Quantitative Assessment of Factors Influencing the Spatiotemporal Variation in Carbon Dioxide Fluxes Simulated by Multi-Source Remote Sensing Data in Tropical Vegetation
Authors: Xu Ruize , Zhang Jiahua , Wang Jingwen , Yao Fengmei , Zhang Sha
Published: December 2023

Vegetation plays a vital role in the global carbon cycle, a function of particular significance in regulating carbon dioxide fluxes within tropical ecosystems. Therefore, it is crucial to enhance the precision of carbon dioxide flux estimates for tropical vegetation and to explore the determinants influencing carbon sequestration. In this study, Landsat series images and Sentinel-2 Multispectral Instrument satellite data were used to invert vegetation biophysical parameters, thereby improving the timeliness and resolution of state variables from the boreal ecosystem productivity simulator (BEPS). The BEPS model at a 30 m resolution was developed to accurately capture tropical vegetation carbon dioxide fluxes across Hainan Island (HN) over the preceding two decades. The impacts of climate variations and anthropogenic activities on the carbon dioxide fluxes of tropical vegetation were further quantified using quantile regression models and a land-use transfer matrix. Results indicate significant increases in both net primary productivity (NPP) and net ecosystem productivity (NEP) in HN during the period 2000–2020, by 5.81 and 4.29 g C/m2 year, respectively. Spatial trends in vegetation carbon dioxide fluxes exhibited a consistent decline from inland regions to coastal zones. Anthropogenic activities were the dominant factor in the reduced stability of coastal NPP, while the post-2005 vegetation restoration promoted the southward expansion of high NPP (>1200 g C/m2) in the central part of HN. NPP in this tropical island was more sensitive to temperature than to precipitation, with a 1 °C temperature increase resulting in 4.1 g C/m2 reduction in dry-season NPP compared to wet-season NPP. Upgrades of cropland quality and grassland restoration have improved NPP yields, and land use transfers have resulted in a 0.301 Tg C net increase in NPP. This study provides new insight into the improvement of the carbon dioxide flux model at a finer scale for tropical vegetation and highlights ecological construction as an adaptation strategy to enhance the carbon sinks of tropical vegetation under negative climate change conditions.
Propagation of Meteorological Drought to Agricultural and Hydrological Droughts in the Tropical Lancang–Mekong River Basin
Authors: Feng Ganlin , Chen Yaoliang , Mansaray Lamin R. , Xu Hongfeng , Shi Aoni , Chen Yanling
Published: December 2023

In the past several decades, drought events have occurred frequently around the world. However, research on the propagation of drought events has not been adequately explored. This study investigated the drought propagation process from meteorological drought to agricultural drought (PMAD) and from meteorological drought to hydrological drought (PMHD) using a 72-year reanalysis dataset in the tropical Lancang–Mekong River Basin. Firstly, we used a new method—Standardized Drought Analysis Toolbox—to construct drought indices. Then, a linear method (Pearson correlation analysis) and a nonlinear method (mutual information) were used to investigate the drought propagation process. Cross-wavelet analysis and wavelet coherence analysis were employed to explore the statistical relationship among the three drought types. Finally, the random forest method was applied to quantify the major factors in drought response time (DRT). The results revealed the following: (1) both linear and nonlinear methods exhibited strong temporal and spatial consistency for both PMAD and PMHD, with linear relationships being stronger than nonlinear ones. (2) The DRTs of PMAD and PMHD were around 1–2 months and 3–5 months, respectively. Significant differences existed in the DRT between the dry season and the rainy season. (3) A divergent spatial pattern of the proportion of DRT was observed between PMAD and PMHD. (4) Significant statistical correlations between meteorological drought and agricultural drought and between meteorological drought and hydrological drought were observed in specific periods for each sub-region; (5) Hydrometeorological factors contributed the most to DRT, followed by terrain factors and the land cover types. The findings of this study deepened our understanding of the spatial–temporal relationship of multiple drought propagation types in this transboundary river basin.
Satellite Estimation of pCO2 and Quantification of CO2 Fluxes in China’s Chagan Lake in the Context of Climate Change
Authors: Zhao Ruixue , Yang Qian , Wen Zhidan , Fang Chong , Li Sijia , Shang Yingxin , Liu Ge , Tao Hui , Lyu Lili , Song Kaishan
Published: December 2023

The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO2), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO2 changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO2) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO2 and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R2 = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))2/(exp(b3) + exp(b5))2, R2 = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R2 = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO2 in Chagan Lake during the ice-free period. The pCO2 in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO2 also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO2 values in the southeast of Chagan Lake were higher than those in the northwest. The CO2 fluxes were calculated based on the pCO2 and ranged from −3.69 to −2.42 mmol/m2/d, indicating that Chagan Lake was absorbing CO2 (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO2, while the Secchi disk depth was negatively correlated with pCO2. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change.
Exploring Self-Supervised Learning for Multi-Modal Remote Sensing Pre-Training via Asymmetric Attention Fusion
Authors: Xu Guozheng , Jiang Xue , Li Xiangtai , Zhang Ze , Liu Xingzhao
Published: December 2023

Self-supervised learning (SSL) has significantly bridged the gap between supervised and unsupervised learning in computer vision tasks and shown impressive success in the field of remote sensing (RS). However, these methods have primarily focused on single-modal RS data, which may have limitations in capturing the diversity of information in complex scenes. In this paper, we propose the Asymmetric Attention Fusion (AAF) framework to explore the potential of multi-modal representation learning compared to two simpler fusion methods: early fusion and late fusion. Given that data from active sensors (e.g., digital surface models and light detection and ranging) is often noisier and less informative than optical images, the AAF is designed with an asymmetric attention mechanism within a two-stream encoder, applied at each encoder stage. Additionally, we introduce a Transfer Gate module to select more informative features from the fused representations, enhancing performance in downstream tasks. Our comparative analyses on the ISPRS Potsdam datasets, focusing on scene classification and segmentation tasks, demonstrate significant performance enhancements with AAF compared to baseline methods. The proposed approach achieves an improvement of over 7% in all metrics compared to randomly initialized methods for both tasks. Furthermore, when compared to early fusion and late fusion methods, AAF consistently outperforms in achieving superior improvements. These results underscore the effectiveness of AAF in leveraging the strengths of multi-modal RS data for SSL, opening doors for more sophisticated and nuanced RS analysis.
Experimental Determination of the Ionospheric Effects and Cycle Slip Phenomena for Galileo and GPS in the Arctic
Authors: Beeck S.S. , Mitchell C.N. , Jensen A.B.O. , Stenseng L. , Pinto Jayawardena T. , Olesen D.H.
Published: December 2023

The ionosphere can impair the accuracy, availability and reliability of satellite-based positioning, navigation and timing. The Arctic region is particularly affected by strong ionospheric gradients and phase scintillation, posing a safety issue for critical infrastructure and operations. Ionospheric warning and impact maps can provide support to Arctic operations, but to produce such maps threshold values have to be determined. This study investigates how such thresholds can be derived from the GPS and Galileo satellite signals. Rapid changes in total electron content (TEC) or scintillation-induced receiver tracking errors could result in cycle slips or even loss of lock. Cycle slips and data outages are used as a measure of impact on the receiver in this paper. For Galileo, 73.6% of the impacts were cycle slips and 26.4% were outages, while for GPS, 29.3% of the impacts were cycle slips and 70.7% were outages. Considering the sum of cycle slips and outages, it is worth noting that the sum of impacts for Galileo signals is larger than for GPS. A range of possible explanations have been examined through hardware-in-the-loop simulations. The simulations showed that the GPS L2 signal was not adequately tracked during rapid TEC changes and TEC changes were underestimated, thus the GPS cycle slips, derived from L1 and L2 derived TEC changes, were not all registered. These results are important in designing threshold values for TEC and for scintillation impact maps as well as for the operation of GNSS equipment in the Arctic. In particular, the results show that ionospheric changes could be underestimated if GPS L1 and L2 were used in isolation from other dual frequency combinations. It is the first time this analysis has been made for Greenland and the first time that the dual frequency derivation of ionospheric delay using GPS L1 and L2 has been shown to underestimate large TEC gradients. This has important implications for informing GNSS operations that rely on GPS to provide reliable estimates of the ionosphere.
A Multi-Input Convolutional Neural Networks Model for Earthquake Precursor Detection Based on Ionospheric Total Electron Content
Authors: Uyanık Hakan , Şentürk Erman , Akpınar Muhammed Halil , Ozcelik Salih T. A. , Kokum Mehmet , Freeshah Mohamed , Sengur Abdulkadir
Published: December 2023

Earthquakes occur all around the world, causing varying degrees of damage and destruction. Earthquakes are by their very nature a sudden phenomenon and predicting them with a precise time range is difficult. Some phenomena may be indicators of physical conditions favorable for large earthquakes (e.g., the ionospheric Total Electron Content (TEC)). The TEC is an important parameter used to detect pre-earthquake changes by measuring ionospheric disturbances and space weather indices, such as the global geomagnetic index (Kp), the storm duration distribution (Dst), the sunspot number (R), the geomagnetic storm index (Ap-index), the solar wind speed (Vsw), and the solar activity index (F10.7), have also been used to detect pre-earthquake ionospheric changes. In this study, the feasibility of the 6th-day earthquake prediction by the deep neural network technique using the previous five consecutive days is investigated. For this purpose, a two-staged approach is developed. In the first stage, various preprocessing steps, namely TEC signal improvement and time-frequency representation-based TEC image construction, are performed. In the second stage, a multi-input convolutional neural network (CNN) model is designed and trained in an end-to-end fashion. This multi-input CNN model has a total of six inputs, and five of the inputs are designed as 2D and the sixth is a 1D vector. The 2D inputs to the multi-input CNN model are TEC images and the vector input is concatenated space weather indices. The network branches with the 2D inputs contain convolution, batch normalization, and Rectified Linear Unit (ReLU) activation layers, and the branch with the 1D input contains a ReLU activation layer. The ReLU activation outputs of all the branches are flattened and then concatenated. And the classification is performed via fully connected, softmax, and classification layers, respectively. In the experimental work, earthquakes with a magnitude of Mw5.0 and above that occurred in Turkey between 2012 and 2019 are used as the dataset. The TEC data were recorded by the Turkey National Permanent GNSS Network-Active (TNPGN-Active) Global Navigation Satellite System (GNSS) stations. The TEC data five days before the earthquake were marked as “precursor days” and the TEC data five days after the earthquake were marked as “normal days”. In total, 75% of the dataset is used to train the proposed method and 25% of the dataset is used for testing. The classification accuracy, sensitivity, specificity, and F1-score values are obtained for performance evaluations. The results are promising, and an 89.31% classification accuracy is obtained.
Integrating GRACE/GRACE Follow-On and Wells Data to Detect Groundwater Storage Recovery at a Small-Scale in Beijing Using Deep Learning
Authors: Hu Ying , Chao Nengfang , Yang Yong , Wang Jiangyuan , Yin Wenjie , Xie Jingkai , Duan Guangyao , Zhang Menglin , Wan Xuewen , Li Fupeng , Wang Zhengtao , Ouyang Guichong
Published: December 2023

Groundwater depletion is adversely affecting Beijing’s ecology and environment. However, the effective execution of the South-to-North Water Diversion Project’s middle route (SNDWP-MR) is anticipated to mitigate Beijing’s groundwater depletion. Here, we propose a robust hybrid statistical downscaling method aimed at enhancing the capability of the Gravity Recovery and Climate Experiment (GRACE) to detect the small-scale groundwater storage anomaly (GWSA) in Beijing. We used three deep learning (DL) methods to reconstruct the 0.5° × 0.5° terrestrial water storage anomaly (TWSA) between 2004 and 2021. Moreover, multiple processing strategies were used to downscale the GWSA to 0.25° from 2004 to 2021 by integrating wells and GRACE/GRACE follow-on data from the optimal DL model. Additionally, we analyzed the spatiotemporal evolution trends of GW in Beijing before and after the implementation of the SNDWP-MR. The results show that the long short-term memory model delivers optimal performance in the TWSA reconstruction of Beijing, with the correlation coefficient (CC), Nash–Sutcliffe coefficient (NSE), and root mean square error (RMSE) being 0.98, 0.96, and 10.19 mm, respectively. The GWSA before and after downscaling is basically consistent with wells data, but the CC and RMSE of downscaling the GWSA from 2004 to 2021 are improving by 34% and 31%, respectively. Before the SNDWP-MR (2004–2014), the trend of GWSA in Beijing was −17.68 ± 4.46 mm/y, with a human contribution of 69.30%. After SNDWP-MR (2015–2021), GWSA gradually increased by 10.00 mm per year, with the SNDWP-MR accounting for 18.30%. This study delivers a technical innovation reference for dynamically monitoring a small-scale GWSA from GRACE/GRACE-FO data.
Study on Improvement of Radio Propagation Characteristics of Cast Iron Boxes for Water Smart Meters
Authors: Tateishi Eiichi , Yi Yuantong , Kai Nobuhiro , Kumagae Takaya , Yamaguchi Tatsuya , Kanaya Haruichi
Published: December 2023

Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water meters is expected to bring many benefits such as reduced labor by automating meter reading, early identification of leaks, and visualization of pipeline data to strengthen the infrastructure of water services, business continuity, and customer service, as detailed data can be obtained using wireless communication. Demonstration tests are actively conducted in Japan; however, many problems have been reported with cast iron meter boxes blocking radio waves. To address the issue, a low-cost slit structure for cast iron meter boxes is investigated in this study. The results confirm that the L-shaped tapered slit array structure with a cavity, which can be fabricated in a cast iron integral structure, satisfies the design loads required for road installation. The proposed slit structure achieved gain characteristics from −3.32 to more than 9.54 dBi in the 800 to 920 MHz band. The gain characteristics of conventional cast iron meter boxes range from −15 to −20 dBi, and the gain has been significantly improved. Antennas with a gain of −2.0 to +1.5 dB (0.8 to 2.5 GHz) were used for the transmitter antenna, which was found to have a higher gain than the transmit antenna in the 800 to 880 MHz frequency band. In the 1.5 to 2.0 GHz band, a high peak gain of 4.25 dBi was achieved at 1660 MHz, with no null and the lowest gain confirmed that this is an improvement of more than 10 dBi over conventional products.
Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms
Authors: Ali Hanan Awad Hassan , Seytnazarov Shinnazar
Published: December 2023

The use of wireless signals for device-free activity recognition and precise indoor positioning has gained significant popularity recently. By taking advantage of the characteristics of the received signals, it is possible to establish a mapping between these signals and human activities. Existing approaches for detecting human walking direction have encountered challenges in adapting to changes in the surrounding environment or different people. In this paper, we propose a new approach that uses the channel state information of received wireless signals, a Hampel filter to remove the outliers, a Discrete wavelet transform to remove the noise and extract the important features, and finally, machine and deep learning algorithms to identify the walking direction for different people and in different environments. Through experimentation, we demonstrate that our approach achieved accuracy rates of 92.9%, 95.1%, and 89% in detecting human walking directions for untrained data collected from the classroom, the meeting room, and both rooms, respectively. Our results highlight the effectiveness of our approach even for people of different genders, heights, and environments, which utilizes machine and deep learning algorithms for low-cost deployment and device-free detection of human activities in indoor environments.
Optimal Configuration of Multi-Task Learning for Autonomous Driving
Authors: Jun Woomin , Son Minjun , Yoo Jisang , Lee Sungjin
Published: December 2023

For autonomous driving, it is imperative to perform various high-computation image recognition tasks with high accuracy, utilizing diverse sensors to perceive the surrounding environment. Specifically, cameras are used to perform lane detection, object detection, and segmentation, and, in the absence of lidar, tasks extend to inferring 3D information through depth estimation, 3D object detection, 3D reconstruction, and SLAM. However, accurately processing all these image recognition operations in real-time for autonomous driving under constrained hardware conditions is practically unfeasible. In this study, considering the characteristics of image recognition tasks performed by these sensors and the given hardware conditions, we investigated MTL (multi-task learning), which enables parallel execution of various image recognition tasks to maximize their processing speed, accuracy, and memory efficiency. Particularly, this study analyzes the combinations of image recognition tasks for autonomous driving and proposes the MDO (multi-task decision and optimization) algorithm, consisting of three steps, as a means for optimization. In the initial step, a MTS (multi-task set) is selected to minimize overall latency while meeting minimum accuracy requirements. Subsequently, additional training of the shared backbone and individual subnets is conducted to enhance accuracy with the predefined MTS. Finally, both the shared backbone and each subnet undergo compression while maintaining the already secured accuracy and latency performance. The experimental results indicate that integrated accuracy performance is critically important in the configuration and optimization of MTL, and this integrated accuracy is determined by the ITC (inter-task correlation). The MDO algorithm was designed to consider these characteristics and construct multi-task sets with tasks that exhibit high ITC. Furthermore, the implementation of the proposed MDO algorithm, coupled with additional SSL (semi-supervised learning) based training, resulted in a significant performance enhancement. This advancement manifested as approximately a 12% increase in object detection mAP performance, a 15% improvement in lane detection accuracy, and a 27% reduction in latency, surpassing the results of previous three-task learning techniques like YOLOP and HybridNet.
A Smart Real-Time Parking Control and Monitoring System
Authors: Elfaki Abdelrahman Osman , Messoudi Wassim , Bushnag Anas , Abuzneid Shakour , Alhmiedat Tareq
Published: December 2023

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle’s license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.
Turbofan Engine Health Assessment Based on Spatial–Temporal Similarity Calculation
Authors: Peng Cheng , Hu Xin , Tang Zhaohui
Published: December 2023

Aiming at the problem of the remaining useful life prediction accuracy being too low due to the complex operating conditions of the aviation turbofan engine data set and the original noise of the sensor, a residual useful life prediction method based on spatial–temporal similarity calculation is proposed. The first stage is adaptive sequence matching, which uses the constructed spatial–temporal trajectory sequence to match the sequence to find the optimal matching sample and calculate the similarity between the two spatial–temporal trajectory sequences. In the second stage, the weights of each part are assigned by the two weight allocation algorithms of the weight training module, and then the final similarity is calculated by the similarity calculation formula of the life prediction module, and the final predicted remaining useful life is determined according to the size of the similarity and the corresponding remaining life. Compared with a single model, the proposed method emphasizes the consistency of the test set and the training set, increases the similarity between samples by sequence matching with other spatial–temporal trajectories, and further calculates the final similarity and predicts the remaining use through the weight allocation module and the life prediction module. The experimental results show that compared with other methods, the root mean square error (RMSE) index and the remaining useful life health score (Score) index are reduced by 12.6% and 14.8%, respectively, on the FD004 dataset, and the RMSE index is similar to that in other datasets; the Score index is reduced by about 10%, which improves the prediction accuracy of the remaining useful life and can provide favorable support for the operation and maintenance decision of turbofan engines.
Vibration-Based Recognition of Wheel–Terrain Interaction for Terramechanics Model Selection and Terrain Parameter Identification for Lugged-Wheel Planetary Rovers
Authors: Lv Fengtian , Li Nan , Gao Haibo , Ding Liang , Deng Zongquan , Yu Haitao , Liu Zhen
Published: December 2023

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel–terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel–terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel–terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel–terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.
Validation of the Short Physical Performance Battery via Plantar Pressure Analysis Using Commercial Smart Insoles
Authors: Jang Chan Woong , Park Kyoungmin , Paek Min-Chul , Jee Sanghyun , Park Jung Hyun
Published: December 2023

This cross-sectional study, conducted at a tertiary care hospital’s rehabilitation clinic, aimed to validate Short Physical Performance Battery (SPPB) results obtained through plantar pressure analysis using commercial smart insoles (SPPB-SI) and to compare these results to manually acquired results by an experienced examiner (SPPB-M). This study included 40 independent-walking inpatients and outpatients aged 50 or older. SPPB-SI and SPPB-M were administered concurrently, with the smart insoles providing plantar pressure data that were converted into time–pressure curves. Two interpreters assessed the curves, determining component completion times for the SPPB-SI scores. Among the 40 participants (mean age: 72.98, SD: 9.27), the mean total SPPB-SI score was 7.72 ± 2.50, and the mean total SPPB-M score was 7.95 ± 2.63. The time recordings and measured scores of each SPPB-SI component exhibited high reliability with inter- and intra-interpreter correlation coefficients of 0.9 and 0.8 or higher, respectively. The intraclass correlation coefficient between the total SPPB-SI and SPPB-M scores was 0.831 (p < 0.001), and that between the component scores of the two measurements ranged from 0.837 to 0.901 (p < 0.001). Consistent correlations with geriatric functional parameters were observed for both SPPB-SI and SPPB-M. This study underscores the potential of commercial smart insoles as reliable tools for conducting SPPB assessments.
Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR
Authors: Zhao Min , Fang Zehao , Ding Ning , Li Nan , Su Tengfei , Qian Huihuan
Published: December 2023

This paper introduces a novel method for enhancing underground pipeline inspection, specifically addressing limitations associated with traditional closed-circuit television (CCTV) systems. These systems, commonly used for capturing visual data of sewer system deformations, heavily rely on subjective human expertise, leading to limited accuracy in detection. Furthermore, their inability to perform quantitative analyses of deformation extent hampers overall inspection effectiveness. Our proposed method leverages laser point cloud data and employs a 3D scanner for objective detection of geometric deformations in underground pipe corridors. By utilizing this approach, we enable a quantitative assessment of blockage levels, offering a significant improvement over traditional CCTV-based methods. The key advantages of our method lie in its objectivity and quantification capabilities, ultimately enhancing detection reliability, accuracy, and overall inspection efficiency.
Physical Noninvasive Attacks on Photoplethysmogram by Computer Controlled Blood Pressure Cuff
Authors: Yoshida Kazuki , Sawano Ryota , Okamoto Masahiro , Murao Kazuya , Tsuchida Shuhei , Terada Tsutomu
Published: December 2023

Sensor data has been used in social security and welfare infrastructures such as insurance and medical care to provide personalized products and services; there is a risk that attackers can alter sensor data to obtain unfair benefits. We consider that one of the attack methods to modify sensor data is to attack the wearer’s body to modify biometric information. In this study, we propose a noninvasive attack method to modify the sensor value of a photoplethysmogram. The proposed method can disappear pulse wave peaks by pressurizing the upper arm with air pressure to control blood volume. Seven subjects experiencing a rest environment and five subjects experiencing an after-exercise environment wore five different models of smartwatches, and three pressure patterns were performed. It was confirmed in both situations that the displayed heart rate decreased from the true heart rate.
Thermo-Optic Response and Optical Bistablility of Integrated High-Index Doped Silica Ring Resonators
Authors: Hu Junkai , Wu Jiayang , Jin Di , Chu Sai Tak , Little Brent E. , Huang Duan , Morandotti Roberto , Moss David J.
Published: December 2023

The engineering of thermo-optic effects has found broad applications in integrated photonic devices, facilitating efficient light manipulation to achieve various functionalities. Here, we perform both an experimental characterization and a theoretical analysis of these effects in integrated microring resonators made from high-index doped silica, which have had many applications in integrated photonics and nonlinear optics. By fitting the experimental results with theory, we obtain fundamental parameters that characterize their thermo-optic performance, including the thermo-optic coefficient, the efficiency of the optically induced thermo-optic process, and the thermal conductivity. The characteristics of these parameters are compared to those of other materials commonly used for integrated photonic platforms, such as silicon, silicon nitride, and silica. These results offer a comprehensive insight into the thermo-optic properties of doped silica-based devices. Understanding these properties is essential for efficiently controlling and engineering them in many practical applications.
Velocity and Color Estimation Using Event-Based Clustering
Authors: Lesage Xavier , Tran Rosalie , Mancini Stéphane , Fesquet Laurent
Published: December 2023

Event-based clustering provides a low-power embedded solution for low-level feature extraction in a scene. The algorithm utilizes the non-uniform sampling capability of event-based image sensors to measure local intensity variations within a scene. Consequently, the clustering algorithm forms similar event groups while simultaneously estimating their attributes. This work proposes taking advantage of additional event information in order to provide new attributes for further processing. We elaborate on the estimation of the object velocity using the mean motion of the cluster. Next, we are examining a novel form of events, which includes intensity measurement of the color at the concerned pixel. These events may be processed to estimate the rough color of a cluster, or the color distribution in a cluster. Lastly, this paper presents some applications that utilize these features. The resulting algorithms are applied and exercised thanks to a custom event-based simulator, which generates videos of outdoor scenes. The velocity estimation methods provide satisfactory results with a trade-off between accuracy and convergence speed. Regarding color estimation, the luminance estimation is challenging in the test cases, while the chrominance is precisely estimated. The estimated quantities are adequate for accurately classifying objects into predefined categories.
Boundaries of Parental Consent: The Example of Hypospadias Surgery
Authors: Roen Katrina , Sterling Rogena
Published: December 2023

Human rights organisations raise concerns about medical interventions on children with intersex variations, particularly when these interventions impinge on the child’s bodily autonomy and are without a sound biomedical basis. Psychosocial literature and legal literature have made very different contributions to thinking about the healthcare of people with intersex variations, but both literatures pay attention to the process of informing patients about elective interventions and the workings of consent. The present paper addresses the absence of dialogue across medical, legal, and psychosocial literatures on the surgical treatment of children with intersex variations. The analysis presented in this paper focusses on the assumptions underpinning the practice of allowing parents to consent on behalf of their children to elective surgery in the instance of hypospadias. In this paper, we (i) introduce consent from a medico-legal perspective, (ii) analyse selected documents (including medical, psychosocial, and human rights documents) in relation to the concept of parental consent on behalf of a child, and (iii) reconsider the current practice of inviting parents to give consent for elective genital surgery on infants. What emerges from our analysis is a picture of long-term relationships and interactions over time within which the consent process is located. The focus is not whether consent is granted, but whether free and informed consent is granted. This picture allows us to expand the understanding of “informed consent,” highlighting the importance of producing ethical interactions between health professionals and patients with the view that these relationships last for years. Understanding consent as a process, considering information as dynamic, partial, and negotiated, and understanding the doctor–patient interaction as relational might enable us to imagine the kind of informed consent process that genuinely works for everyone concerned. Our examination of selected legal, medical, and psychosocial texts raises doubt about whether current hospital practice meets the requirement of informed parental consent on behalf of children undergoing hypospadias surgery.
Biological Activities in Artificially Heavy-Metal-Contaminated Growing Substrates
Authors: Papa Stefania , Alvarez-Romero Marta
Published: December 2023

The ingestion of vegetables grown in soils or in cultivation substrate contaminated with heavy metals (HMs) and irrigated with wastewater is a potential problem for human health and food quality. The increasing disappearance of fertile soils has led to an increase in the practice of soil-less cultivation and the use of growing substrates, but the choice of the right substrate and its sustainable management is essential to ensure the production of quality and safe vegetables for all while minimizing the impact on the environment and human health. The present study measures the combined effects of different HMs (V, Ni, Cd, Pb, Cu, Cr) on microbial biomass, respiration, and enzyme activities (EAs) in an artificially contaminated commercial growing substrate. The concentrations of HMs were estimated by Atomic Absorption Spectroscopy; enzyme activities via spectrophotometric assays; respiration via CO2 evolution; and microbial biomass C via the fumigation extraction method. The results showed a reduction in both respiration and all enzyme activities. The reduction in EAs highlighted a notable influence on microorganism-mediated C, N, S, and P cycles, strongly reducing substrate health. Microbial biomass did not show significant differences, but the increase in the metabolic quotient highlighted how the toxicity of HMs reduces the energy use efficiency of microbial metabolic processes.