bluuebunny/crossref_metadata_2025 huggingface dataset:
A Conjugated Microporous Polymer/Wood Aerogel with Physical Adsorption, Chemical Degradation and Antibacterial Self-Cleaning Triple Sewage Treatment Functions
Authors: Kong Fanwei , Ge Junkang , Zhu Zihao , Chen Chunxia , Peng Jinsong , Li Xiaobai , Li Bin , Ma Hongwei
Published: September 2023

Conjugated microporous polymers (CMPs) have important applications in the fields of optoelectronics and sewage treatment due to their high specific surface area, broad visible absorption, processability and simple synthesis process. Biocompatibility, recycling, mass production and solar photodegradation are particularly important in wastewater treatment. Here, A CMP with a high specific surface area and a hierarchical pore structure (CPOP) was constructed based on 4,4′,4″-Tris(carbazol-9-yl)-triphenylamine (3CZ-TPA). Furthermore, a CMP-loaded wood aerogel (CPOP/wood aerogel) with physical adsorption, chemical degradation, bacterial inhibition and self-cleaning properties was prepared by in situ polymerization and used for wastewater treatment. The obtained CPOP/wood aerogel is highly biocompatible and easy to recycle. In addition, the inherent broad visible light absorption property of CPOP endows it with promising photocatalytic properties. Subsequently, we investigated the photocatalytic mechanism of CPOP, and the results showed that it was mainly affected by peroxyl radicals, which implied and confirmed its microbial self-cleaning for secondary cleaning of water pollutants. The reported studies on CPOP/wood aerogel provide a new direction for water purification materials with excellent adsorption, degradation and antibacterial properties.
Recent Progress of Low Dielectric and High-Performance Polybenzoxazine-Based Composites
Authors: Fan Zexu , Li Bo , Ren Dengxun , Xu Mingzhen
Published: September 2023

With the rapid advancement of intelligent electronics, big data platforms, and other cutting-edge technologies, traditional low dielectric polymer matrix composites are no longer sufficient to satisfy the application requirements of high-end electronic information materials, particularly in the realm of high integration and high-frequency, high-speed electronic communication device manufacturing. Consequently, resin-based composites with exceptional low dielectric properties have garnered unprecedented attention. In recent years, benzoxazine-based composites have piqued the interest of scholars in the fields of high-temperature-resistant, low dielectric electronic materials due to their remarkable attributes such as high strength, high modulus, high heat resistance, low curing shrinkage, low thermal expansion coefficient, and excellent flame retardancy. This article focuses on the design and development of modification of polybenzoxazine based on low dielectric polybenzoxazine modification methods. Studies on manufacturing polybenzoxazine co-polymers and benzoxazine-based nanocomposites have also been reviewed.
Bright and Stable Nanomaterials for Imaging and Sensing
Authors: Farinha José Paulo Sequeira
Published: September 2023

This review covers strategies to prepare high-performance emissive polymer nanomaterials, combining very high brightness and photostability, to respond to the drive for better imaging quality and lower detection limits in fluorescence imaging and sensing applications. The more common approaches to obtaining high-brightness nanomaterials consist of designing polymer nanomaterials carrying a large number of fluorescent dyes, either by attaching the dyes to individual polymer chains or by encapsulating the dyes in nanoparticles. In both cases, the dyes can be covalently linked to the polymer during polymerization (by using monomers functionalized with fluorescent groups), or they can be incorporated post-synthesis, using polymers with reactive groups, or encapsulating the unmodified dyes. Silica nanoparticles in particular, obtained by the condensation polymerization of silicon alcoxides, provide highly crosslinked environments that protect the dyes from photodegradation and offer excellent chemical modification flexibility. An alternative and less explored strategy is to increase the brightness of each individual dye. This can be achieved by using nanostructures that couple dyes to plasmonic nanoparticles so that the plasmon resonance can act as an electromagnetic field concentrator to increase the dye excitation efficiency and/or interact with the dye to increase its emission quantum yield.
Hydrophobized MFC as Reinforcing Additive in Industrial Silica/SBR Tire Tread Compound
Authors: Liu Ming , Imiete Iikpoemugh Elo , Staropoli Mariapaola , Steiner Pascal , Duez Benoît , Lenoble Damien , Scolan Emmanuel , Thomann Jean-Sébastien
Published: September 2023

Silica is used as reinforcing filler in the tire industry. Owing to the intensive process of silica production and its high density, substitution with lightweight bio-based micro fibrillated cellulose (MFC) is expected to provide lightweight, sustainable, and highly reinforced tire composite. MFC was modified with oleoyl chloride, and the degree of substitution (DS) was maintained between 0.2 and 0.9. Subsequently, the morphology and crystallinity of the modified MFC were studied and found to be significantly dependent on the DS. The advantages associated with the use of the modified MFC in synergy with silica for the reinforcement of styrene butadiene rubber (SBR) nanocomposite was investigated in comparison with silica/SBR compound. The structural changes occasioned by the DS values influenced the processability, curing kinetics, modulus-rolling resistance tradeoff, and tensile properties of the resultant rubber compounds. We found that the compound made with modified MFC at a DS of 0.67 (MFC16) resulted to the highest reinforcement, with a 350% increase in storage modulus, 180% increase in Young`s modulus, and 15% increase in tensile strength compared to the referenced silica-filled compounds. Our studies show that MFC in combination with silica can be used to reinforce SBR compound for tire tread applications.
Chondrogenic Potential of Human Adipose-Derived Mesenchymal Stromal Cells in Steam Sterilized Gelatin/Chitosan/Polyvinyl Alcohol Hydrogels
Authors: Pérez-Díaz Mario Alberto , Martínez-Colin Erick Jesús , González-Torres Maykel , Ortega-Sánchez Carmina , Sánchez-Sánchez Roberto , Delgado-Meza Josselin , Machado-Bistraín Fernando , Martínez-López Valentín , Giraldo David , Márquez-Gutiérrez Érik Agustín , Jiménez-Ávalos Jorge Armando , García-Carvajal Zaira Yunuen , Melgarejo-Ramírez Yaaziel
Published: September 2023

Cross-linked polymer blends from natural compounds, namely gelatin (Gel), chitosan (CS), and synthetic poly (vinyl alcohol) (PVA), have received increasing scrutiny because of their versatility, biocompatibility, and ease of use for tissue engineering. Previously, Gel/CS/PVA [1:1:1] hydrogel produced via the freeze-drying process presented enhanced mechanical properties. This study aimed to investigate the biocompatibility and chondrogenic potential of a steam-sterilized Gel/CS/PVA hydrogel using differentiation of human adipose-derived mesenchymal stromal cells (AD-hMSC) and cartilage marker expression. AD-hMSC displayed fibroblast-like morphology, 90% viability, and 69% proliferative potential. Mesenchymal profiles CD73 (98.3%), CD90 (98.6%), CD105 (97.0%), CD34 (1.11%), CD45 (0.27%), HLA-DR (0.24%); as well as multilineage potential, were confirmed. Chondrogenic differentiation of AD-hMSC in monolayer revealed the formation of cartilaginous nodules composed of glycosaminoglycans after 21 days. Compared to nonstimulated cells, hMSC-derived chondrocytes shifted the expression of CD49a from 2.82% to 40.6%, CD49e from 51.4% to 92.2%, CD54 from 9.66 to 37.2%, and CD151 from 45.1% to 75.8%. When cultured onto Gel/CS/PVA hydrogel during chondrogenic stimulation, AD-hMSC changed to polygonal morphology, and chondrogenic nodules increased by day 15, six days earlier than monolayer-differentiated cells. SEM analysis showed that hMSC-derived chondrocytes adhered to the surface with extended filopodia and abundant ECM formation. Chondrogenic nodules were positive for aggrecan and type II collagen, two of the most abundant components in cartilage. This study supports the biocompatibility of AD-hMSC onto steam-sterilized GE/CS/PVA hydrogels and its improved potential for chondrocyte differentiation. Hydrogel properties were not altered after steam sterilization, which is relevant for biosafety and biomedical purposes.
The Influence of Draft Tubes on the Mass Transfer and Mixing Performance of a Pilot-Scale Internal-Loop Airlift Reactor
Authors: Wang Zongliang , Cheng Zhenmin , Yuan Peiqing , Huang Zibin
Published: September 2023

The hydrodynamic characteristics, mass transfer, and mixing performance of three different reactors, a bubble column reactor (BCR), a single-stage internal-loop airlift reactor (SSALR), and a four-stage internal-loop airlift reactor (FSALR), were investigated systematically through cold model experiments to explore the influence of draft tube configurations on the pilot-scale internal-loop airlift reactor (ILAR). The findings indicated that the BCR yielded a higher gas holdup and mass transfer coefficient due to its longer bubble residence time. Segmenting the draft tube improved the gas holdup in both the riser and downcomer, and the overall gas holdup in the downcomer increased by 9%. Compared with the SSALR, the mass transfer coefficient of the FSALR in the riser and downcomer increased by 10.2% and 9.3% on average, respectively. In addition, a higher liquid circulating velocity was obtained with the ILARs due to a higher gas holdup difference between the riser and the downcomer. Specifically, the liquid circulating velocity of the FSALR was 134.1% higher than that of the BCR and 15.8% higher than that of the SSALR. The mixing time of the ILARs was reduced due to more intense overall circulation. The mixing effect of the FSALR was the best. The mixing time was reduced by 70.2% and 51.3% compared with the BCR and SSALR for UG ranging from 4.0 cm/s to 9.1 cm/s, respectively. Empirical correlations were proposed for the gas holdup, liquid circulating velocity, mass transfer coefficient, and mixing time on the superficial gas velocity, and agreement with experimental data was satisfactory.
Management of Agri-Food Waste Based on Thermochemical Processes towards a Circular Bioeconomy Concept: The Case Study of the Portuguese Industry
Authors: Berenguer Cristina V. , Perestrelo Rosa , Pereira Jorge A. M. , Câmara José S.
Published: September 2023

Sustainable biomass production has a significant potential for mitigating greenhouse gas emissions, providing an alternative to produce eco-friendly biofuels, biochemicals, and carbonaceous materials for biological, energetic, and environmental applications. Biomass from agroforestry and agricultural wastes is the richest natural carbon source and a sustainable option for woody biomass from a circular economic perspective. The European Union (EU) is estimated to produce 1.3 billion tons of agri-food waste annually. Portugal has a large supply of residual biomass, as well as other byproducts and wastes from forestry, agriculture, and the food industry, and has a high availability of residual biomass. By using biomass waste to create high-value products, Portugal envisages an improvement in its economic performance, while reducing its dependence on energy imports and fossil fuel use. This review explores the potential of agri-food waste obtained from Portuguese industries through thermochemical conversion technologies as a promising sustainable substitute for wood-based biomass for the development of eco-friendly biofuels, biochemicals, and high-value carbonaceous materials, and their applications. This strategy, based on the circular bioeconomy concept, can help reduce reliance on fossil fuels, reduce greenhouse gas emissions, fulfil the needs of the growing population, and offer a sustainable waste management solution.
Optimization of Drilling Parameters in Drilling of MWCNT-Reinforced GFRP Nanocomposites Using Fuzzy AHP-Weighted Taguchi-Based MCDM Methods
Authors: Fedai Yusuf
Published: September 2023

Many problems such as delamination, cracking, fiber tearing, ovality, and surface roughness are encountered in the drilling of glass-fiber-reinforced composite (GFRP) materials. In this study, the percentage of multi-walled carbon nano tube (MWCNT), cutting tool type, feed rate, and cutting speed were selected as control factors during the drilling of MWCNT-reinforced GFRP nanocomposites. The quality characteristics of the drilling process were determined as surface roughness, delamination, torque, and thrust force. The experiments were carried out in accordance with the Taguchi L27 orthogonal array. The lowest values obtained because of the experiments were Ra = 4.95 µm, Dm = 1.099, T = 14.78 N, and F = 44.24 N, respectively. However, since each of these outputs were obtained from different experimental trials, different multi-criteria decision-making (MCDM) methods were used to optimize all outputs at the same time. First, the criteria were weighted using the fuzzy AHP method, and then the outputs were optimized using multi-criteria decision-making methods (i.e., GRA, WASPAS and VIKOR). Very close optimal ranking was obtained in all three methods. The best results were obtained for Ra = 4.86 µm, Dm = 1.13, T = 55.57 N, and F = 48.00 N. In the next step, the performance values obtained from each MCDM method were re-optimized using the Taguchi S/N ratio method. By comparing between these models, a single optimal condition for drilling is proposed. Accordingly, A2B3C1D1 (Ra = 4.86 µm, Dm = 1.10, T = 17.47 N and F = 48.33 N) for FAHP-GRA and FAHP-WASPAS and A2B3C2D2 (Ra = 5.02 µm, Dm = 1.09, T = 37.19 N and F = 45.01 N) for FAHP-VIKOR were determined as the best performing experiments. Finally, validation tests were conducted to compare the performance of the experiments. As a result, the FAHP-GRA and FAHP-WASPAS optimization with Taguchi S/N gave an unweighted improvement of 82.9% and a weighted improvement of 10.04% compared to the results of the experiment with MCDM. Compared to the results of the experiments with MCDM, S/N FAHP-VIKOR provided an unweighted improvement of 52.75% and a weighted improvement of 8.19%. According to the results obtained, for this study, FAHP-GRA and FAHP-WASPAS are more effective optimization methods than FAHP-VIKOR.
Complex Flow Mechanism and Pressurization Effect of Liquid Nitrogen Jet Fracturing Formation Perforation Tunnel
Authors: Zou Zengxin , Cai Chengzheng , Wang Bo , Gao Yanan , Tao Zhixiang , Feng Yinrong
Published: September 2023

As an anhydrous fracturing method, liquid nitrogen jet fracturing technology is expected to become an efficient development method for shale gas resources. In order to explore the influence of the pressurization effect in the liquid nitrogen jet channel, the flow field in the perforation tunnel during the liquid nitrogen jet fracturing process was simulated by computational fluid dynamics, and the complex flow mechanism of liquid nitrogen in the perforation tunnel was analyzed. The pressurization effect of liquid nitrogen jet and water jet fracturing was compared, and the influence of various parameters on the pressurization effect of liquid nitrogen jet fracturing was studied. The research results indicate that under the same conditions, liquid nitrogen jets have a pressurization effect comparable to water jets, and the difference between the pressurization values of the liquid nitrogen jet and the water jet in the perforation tunnel is not more than 0.4 MPa under different nozzle pressure drop conditions. The larger the nozzle pressure drop and nozzle diameter, the greater the pressure increase value in the perforation tunnel of liquid nitrogen jet fracturing, which decreases with the increase in casing hole diameter. Further analysis shows that the pressurization effect is most affected by the two parameters of casing hole diameter and nozzle diameter. The essential reason for its influence on the pressurization value is the squeezing effect of the jet on the perforation tunnel fluid and the sealing effect of the shrinking part of the perforation tunnel on the backflow. The ambient pressure, the temperature of liquid nitrogen, and the diameter of the wellbore have no obvious effect on the pressurization effect. Therefore, through the reasonable combination of casing hole diameter and nozzle diameter, the sealing effect of the contraction part of the perforation tunnel on the fluid and the squeezing effect on the fluid in the perforation tunnel will be affected, which will significantly improve the pressurization effect of the liquid nitrogen jet in the perforation tunnel.
The Sound of One Hand Playing: The Sonic Environment of a Zen Training Temple
Authors: Joskovich Erez
Published: September 2023

Zen practice is often associated with aloof silence and introspection. However, any visitor to a Japanese Zen training temple will be surprised by the abundance and richness of the acoustic environment. In effect, every moment of the training schedule is accompanied, chronicled, and choreographed by percussions and chanting vocals. This paper will introduce this sonic environment and explain how different sounds construct the training experience. In particular, I will focus on sound instruments that coordinate daily activities, such as bells, gongs, drums, and the like, arguing that they are essential to Zen practice. The first part of the paper explains the unique characteristics of a Japanese Zen training temple, focusing on ritual sound instruments and their place within the monastic soundspace. The second part examines the meaning of these instruments as described in Zen canonical writing, focusing mainly on Baizang’s monastic regulation and different references to it by Japanese masters throughout history. The third part explores how contemporary monastics understand sound instruments and their function in regulating the body, conveying values, and evoking spiritual transformation.
Preliminary Results of Marine Gravity Recovery by Tiangong-2 Interferometric Imaging Radar Altimeter
Authors: Sun Meng , Zhang Yunhua , Dong Xiao , Shi Xiaojin
Published: September 2023

This paper presents for the first time the results of marine gravity recovery using the ocean observation data acquired by Tiangong-2 interferometric imaging radar altimeter (TG2 InIRA) which demonstrate not only the balanced accuracies of the north and east components of deflection of the vertical (DOV) as envisaged, but also the improved spatial resolutions of DOV compared with that by conventional altimeters (CAs). Moreover, much higher measurement efficiency owing to the wide-swath capability and the great potential in accuracy improvement of marine gravity field are also demonstrated. TG2 InIRA adopts the interferometry with short baseline and takes small incidence angles, by which wide-swath sea surface height (SSH) can be measured with high accuracy. Gravity recovery experiments in the Western Pacific area are conducted to demonstrate the performance, advantages and capability of TG2 InIRA. SSH data processing algorithms and DOV calculation have been designed by taking the wide-swath feature into account, based on which, the gravity anomalies are then calculated using the inverse Vening Meinesz formula. The derived gravity anomalies are compared with both the published gravity models and the shipborne gravity measurements. The results show that the accuracy of TG2 InIRA is equivalent to, or even a little better than, that of CAs. The fused gravity result using equal TG2 InIRA data and CAs data performs better than those using TG2 InIRA data alone or CAs data alone. Due to the signal bandwidth of TG2 InIRA is only 40 MHz which is much smaller than that of CAs, much higher accuracy can be hopefully achieved for future missions if larger signal bandwidth is used.
Global Satellite Monitoring of Exothermic Industrial Activity via Infrared Emissions
Authors: Elvidge Christopher D. , Zhizhin Mikhail , Sparks Tamara , Ghosh Tilottama , Pon Stephen , Bazilian Morgan , Sutton Paul C. , Miller Steven D.
Published: September 2023

This paper reports on the first daily global monitoring program for natural gas flaring and industrial sites producing waste heat based on satellite observed infrared emissions. The Visible Infrared Imaging Radiometer Suite (VIIRS) collects nightly global infrared data in spectral bands ranging from near infrared (NIR) to longwave infrared (LWIR), providing a unique capability to detect and characterize infrared emitters at night. The VIIRS nightfire (VNF) algorithm identifies infrared (IR) emitters with multiple spectral bands and calculates the temperature, source area, and radiant heat via Planck curve fitting and physical laws. VNF data are produced nightly and extend from 2012 to the present. The most common infrared emitter is biomass burning, which must be filtered out. Industrial IR emitters can be distinguished from biomass burning based on temperature and persistence. The initial filtering to remove biomass burning was performed with 15 arc second grids formed from eleven years of VIIRS data, spanning 2012–2022. The locations and shapes of the remaining features were used to guide the generation of super-resolution pixel center clouds. These data clouds were then analyzed to define bounding vectors for single emitters and to split larger clusters into multiple emitters. A total of nearly 20,000 IR emitters were identified; each was assigned an identification number, and the type of emitter was recorded. Nightly temporal profiles were produced for each site, revealing activity patterns back to 2012. Nightly temporal profiles were kept current with weekly updates. Temporal profiles from individual sites were aggregated by country to form monthly profiles extending back to 2012. The nightly and monthly temporal profiles were suitable for analyzing industrial production, identifying disruption events, and tracking recovery. The data could also be used in tracking progress in energy conservation and greenhouse gas emission inventories.
Comparative Analysis of Deep Learning and Swarm-Optimized Random Forest for Groundwater Spring Potential Identification in Tropical Regions
Authors: Nhu Viet-Ha , Hoa Pham Viet , Melgar-García Laura , Tien Bui Dieu
Published: September 2023

Identifying areas with high groundwater spring potential is crucial as it enables better decision-making concerning water supply, sustainable development, and the protection of sensitive ecosystems; therefore, it is necessary to predict the groundwater spring potential with highly accurate models. This study aims to assess and compare the effectiveness of deep neural networks (DeepNNs) and swarm-optimized random forests (SwarmRFs) in predicting groundwater spring potential. This study focuses on a case study conducted in the Gia Lai province, located in the Central Highland of Vietnam. To accomplish this objective, a comprehensive groundwater database was compiled, comprising 938 groundwater spring locations and 12 influential variables, namely land use and land cover (LULC), geology, distance to fault, distance to river, rainfall, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference water index (NDWI), slope, aspect, elevation, and curvature. The DeepNN model was trained and fine-tuned using the Adaptive Moment Estimation (ADAM) optimizer, while the SwarmRF model employed the Harris Hawks Optimizer (HHO) to search for optimal parameters. The results indicate that both the DeepNN model (accuracy = 77.9%, F-score = 0.783, kappa = 0.559, and AUC = 0.820) and the SwarmRF model (accuracy = 80.2%, F-score = 0.798, kappa = 0.605, and AUC = 0.854) exhibit robust predictive capabilities. The SwarmRF model displays a slight advantage over the DeepNN model in terms of performance. Among the 12 influential factors, geology emerges as the most significant determinant of groundwater spring potential. The groundwater spring potential maps generated through this research can offer valuable information for local authorities to facilitate effective water resource management and support sustainable development planning.
Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images
Authors: Gallo Ignazio , Boschetti Mirco , Rehman Anwar Ur , Candiani Gabriele
Published: September 2023

The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration.
Spatiotemporal Evolutions of the Suspended Particulate Matter in the Yellow River Estuary, Bohai Sea and Characterized by Gaofen Imagery
Authors: Yu Zhifeng , Zhang Jun , Chen Zheyu , Hu Yuekai , Shum C. K. , Ma Chaofei , Song Qingjun , Yuan Xiaohong , Wang Ben , Zhou Bin
Published: September 2023

Suspended particulate matter is a crucial component in estuaries and coastal oceans, and a key parameter for evaluating their water quality. The Bohai Sea, a huge marginal sea covering an expanse of 77,000 km² and constantly fed by numerous sediment-laden rivers, has maintained a high level of total suspended particulate matter (TSM). Despite the widespread development and application of TSM retrieval algorithms using commonly available satellite data like Landsat, Sentinel, and MODIS, developing TSM retrieval algorithms for China’s Gaofen (GF) series (GF-6 and GF-1) in the Bohai Sea is still a great challenge, mainly due to the limited applicability of empirical algorithms. In this study, 259 in situ measured-TSM samples were collected for algorithm development. The remote sensing reflectance (Rrs) curve demonstrates prominent peaks between 550 and 580 nm. Through conversion to remote sensing reflectance, it was found that single-band data had a weak correlation with TSM, reaching a maximum correlation of 0.44. However, by combining bands of band ratio calculations, the correlation was enhanced. Particularly, the blue and green band equivalent Rrs ratio had a correlation coefficient of 0.81 with TSM, and the proposed TSM inversion exponential algorithm developed based on this factor obtained an R-squared (R²) value of 0.76 and a mean relative error (MRE) of 32.24%. Analysis results indicated that: (1) there are spatial variations in the TSM within the Bohai Sea, Laizhou Bay, and the Yellow River estuary, with higher levels near the coast and lower levels in open waters. The Yellow River estuary experiences seasonal fluctuations higher TSM during spring and winter, and lower variations during summer and autumn, and (2) the dynamics of TSM are affected by Yellow River runoff, with increased runoff leads to higher TSM levels and expanded turbid zones. This study proposes a new algorithm to quantify TSM evolutions and distributions in the Bohai Sea and adjacent regions using China’s Gaofen imageries.
A Transformer Model for Coastline Prediction in Weitou Bay, China
Authors: Yang Zhihai , Wang Guangjun , Feng Lei , Wang Yuxian , Wang Guowei , Liang Sihai
Published: September 2023

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management.
Electrical Properties of Taste Sensors with Positively Charged Lipid Membranes Composed of Amines and Ammonium Salts
Authors: Watanabe Kentaro , Watanabe Tatsukichi , Kimura Shunsuke , Ikezaki Hidekazu , Toko Kiyoshi
Published: September 2023

Currently, taste sensors utilizing lipid polymer membranes are utilized to assess the taste of food products quantitatively. During this process, it is crucial to identify and quantify basic tastes, e.g., sourness and sweetness, while ensuring that there is no response to tasteless substances. For instance, suppression of responses to anions, like tasteless NO3− ions contained in vegetables, is essential. However, systematic electrochemical investigations have not been made to achieve this goal. In this study, we fabricated three positively charged lipid polymer membranes containing oleylamine (OAm), trioctylemethylammonium chloride (TOMACl), or tetradodecylammonium bromide (TDAB) as lipids, and sensors that consist of these membranes to investigate the potential change characteristics of these sensors in solutions containing different anions (F−, Cl−, Br−, NO3−, I−). The ability of each anion solution to reduce the positive charge on membranes and shift the membrane potential in the negative direction was in the following order: I− > NO3− > Br− > Cl− > F−. This order well reflected the order of size of the hydrated ions, related to their hydration energy. Additionally, the OAm sensor displayed low ion selectivity, whereas the TOMACl and TDAB sensors showed high ion selectivity related to the OAm sensor. Such features in ion selectivity are suggested to be due to the variation in positive charge with the pH of the environment and packing density of the OAm molecule in the case of the OAm sensor and due to the strong and constant positive charge created by complete ionization of lipids in the case of TOMACl and TDAB sensors. Furthermore, it was revealed that the ion selectivity varies by changing the lipid concentration in each membrane. These results contribute to developing sensor membranes that respond to different anion species selectively and creating taste sensors capable of suppressing responses to tasteless anions.
A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar
Authors: Lin Junyu , Hu Jun , Xie Zhiyuan , Zhang Yulan , Huang Guangjia , Chen Zengping
Published: September 2023

Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range–time–Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.
Dataset Condensation via Expert Subspace Projection
Authors: Ma Zhiheng , Gao Dezheng , Yang Shaolei , Wei Xing , Gong Yihong
Published: September 2023

The rapid growth in dataset sizes in modern deep learning has significantly increased data storage costs. Furthermore, the training and time costs for deep neural networks are generally proportional to the dataset size. Therefore, reducing the dataset size while maintaining model performance is an urgent research problem that needs to be addressed. Dataset condensation is a technique that aims to distill the original dataset into a much smaller synthetic dataset while maintaining downstream training performance on any agnostic neural network. Previous work has demonstrated that matching the training trajectory between the synthetic dataset and the original dataset is more effective than matching the instantaneous gradient, as it incorporates long-range information. Despite the effectiveness of trajectory matching, it suffers from complex gradient unrolling across iterations, which leads to significant memory and computation overhead. To address this issue, this paper proposes a novel approach called Expert Subspace Projection (ESP), which leverages long-range information while avoiding gradient unrolling. Instead of strictly enforcing the synthetic dataset’s training trajectory to mimic that of the real dataset, ESP only constrains it to lie within the subspace spanned by the training trajectory of the real dataset. The memory-saving advantage offered by our method facilitates unbiased training on the complete set of synthetic images and seamless integration with other dataset condensation techniques. Through extensive experiments, we have demonstrated the effectiveness of our approach. Our method outperforms the trajectory matching method on CIFAR10 by 16.7% in the setting of 1 Image/Class, surpassing the previous state-of-the-art method by 3.2%.
Comparison of Experienced and Novice Drivers’ Visual and Driving Behaviors during Warned or Unwarned Near–Forward Collisions
Authors: Navarro Jordan , Reynaud Emanuelle , Ouimet Marie Claude , Schnebelen Damien
Published: September 2023

Forward collision warning systems (FCWSs) monitor the road ahead and warn drivers when the time to collision reaches a certain threshold. Using a driving simulator, this study compared the effects of FCWSs between novice drivers (unlicensed drivers) and experienced drivers (holding a driving license for at least four years) on near-collision events, as well as visual and driving behaviors. The experimental drives lasted about six hours spread over six consecutive weeks. Visual behaviors (e.g., mean number of fixations) and driving behaviors (e.g., braking reaction times) were collected during unprovoked near-collision events occurring during a car-following task, with (FCWS group) or without FCWS (No Automation group). FCWS presence reduced the number of near-collision events drastically and enhanced visual behaviors during those events. Unexpectedly, brake reaction times were observed to be significantly longer with FCWS, suggesting a cognitive cost associated with the warning process. Still, the FCWS showed a slight safety benefit for novice drivers attributed to the assistance provided for the situation analysis. Outside the warning events, FCWS presence also impacted car-following behaviors. Drivers took an extra safety margin, possibly to prevent incidental triggering of warnings. The data enlighten the nature of the cognitive processes associated with FCWSs. Altogether, the findings support the general efficiency of FCWSs observed through a massive reduction in the number of near-collision events and point toward the need for further investigations.
A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection
Authors: Nazir Anjum , Memon Zulfiqar , Sadiq Touseef , Rahman Hameedur , Khan Inam Ullah
Published: September 2023

The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.
The Use of Embedded IMU Insoles to Assess Gait Parameters: A Validation and Test-Retest Reliability Study
Authors: Riglet Louis , Nicol Fabien , Leonard Audrey , Eby Nicolas , Claquesin Lauranne , Orliac Baptiste , Ornetti Paul , Laroche Davy , Gueugnon Mathieu
Published: September 2023

Wireless wearable insoles are interesting tools to collect gait parameters during daily life activities. However, studies have to be performed specifically for each type of insoles on a big data set to validate the measurement in ecological situations. This study aims to assess the criterion validity and test-retest reliability of gait parameters from wearable insoles compared to motion capture system. Gait of 30 healthy participants was recorded using DSPro® insoles and a motion capture system during overground and treadmill walking at three different speeds. Criterion validity and test-retest reliability of spatio-temporal parameters were estimated with an intraclass correlation coefficient (ICC). For both systems, reliability was found higher than 0.70 for all variables (p < 0.001) except for minimum toe clearance (ICC < 0.50) with motion capture system during overground walking. Regardless of speed and condition of walking, Speed, Cadence, Stride Length, Stride Time and Stance Time variables were validated (ICC > 0.90; p < 0.001). During walking on treadmill, loading time was not validated during slow speed (ICC < 0.70). This study highlights good criterion validity and test-retest reliability of spatiotemporal gait parameters measurement using wearable insoles and opens a new possibility to improve care management of patients using clinical gait analysis in daily life activities.
FoGGAN: Generating Realistic Parkinson’s Disease Freezing of Gait Data Using GANs
Authors: Peppes Nikolaos , Tsakanikas Panagiotis , Daskalakis Emmanouil , Alexakis Theodoros , Adamopoulou Evgenia , Demestichas Konstantinos
Published: September 2023

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson’s Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier’s performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.
An Overview of Image Generation of Industrial Surface Defects
Authors: Zhong Xiaopin , Zhu Junwei , Liu Weixiang , Hu Chongxin , Deng Yuanlong , Wu Zongze
Published: September 2023

Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary.
Digital Classification of Chilean Pelagic Species in Fishing Landing Lines
Authors: Caro Fuentes Vincenzo , Torres Ariel , Luarte Danny , Pezoa Jorge E. , Godoy Sebastián E. , Torres Sergio N. , Urbina Mauricio A.
Published: September 2023

Fishing landings in Chile are inspected to control fisheries that are subject to catch quotas. The control process is not easy since the volumes extracted are large and the numbers of landings and artisan shipowners are high. Moreover, the number of inspectors is limited, and a non-automated method is utilized that normally requires months of training. In this work, we propose, design, and implement an automated fish landing control system. The system consists of a custom gate with a camera array and controlled illumination that performs automatic video acquisition once the fish landing starts. The imagery is sent to the cloud in real time and processed by a custom-designed detection algorithm based on deep convolutional networks. The detection algorithm identifies and classifies different pelagic species in real time, and it has been tuned to identify the specific species found in landings of two fishing industries in the Biobío region in Chile. A web-based industrial software was also developed to display a list of fish detections, record relevant statistical summaries, and create landing reports in a user interface. All the records are stored in the cloud for future analyses and possible Chilean government audits. The system can automatically, remotely, and continuously identify and classify the following species: anchovy, jack mackerel, jumbo squid, mackerel, sardine, and snoek, considerably outperforming the current manual procedure.
Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation
Authors: Jamieson Alexander , Murray Laura , Stankovic Vladimir , Stankovic Lina , Buis Arjan
Published: September 2023

This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route across a variety of terrains in the vicinity of their homes. Their physical activity data were clustered to extract ‘unique’ groupings in a low-dimension feature space in an unsupervised learning approach, and an algorithm was created to automatically distinguish such activities. After testing three dimensionality reduction methods—namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP)—we selected tSNE due to its performance and stable outputs. Cluster formation of activities via DBSCAN only occurred after the data were reduced to two dimensions via tSNE and contained only samples for a single individual. Additionally, through analysis of the t-SNE plots, appreciable clusters in walking-based activities were only apparent with ground walking and stair ambulation. Through a combination of density-based clustering and analysis of cluster distance and density, a novel algorithm inspired by the t-SNE plots, resulting in three proposed and validated hypotheses, was able to identify cluster formations that arose from ground walking and stair ambulation. Low dimensional clustering of activities has thus been found feasible when analyzing individual sets of data and can currently recognize stair and ground walking ambulation.