bluuebunny/crossref_metadata_2025 huggingface dataset:
Technologies for Fertilizers and Management Strategies of N-Fertilization in Coffee Cropping Systems to Reduce Ammonia Losses by Volatilization
Authors: Freitas Tainah , Bartelega Lucas , Santos César , Dutra Mateus Portes , Sarkis Leonardo Fernandes , Guimarães Rubens José , Dominghetti Anderson William , Zito Pauliana Cristina , Fernandes Tales Jesus , Guelfi Douglas
Published: December 2022

The aim of this study was to quantify NH3-N losses from conventional, stabilized, slow-release, and controlled-release N fertilizers in a coffee field. The N fertilizers analyzed were prilled urea, prilled urea dissolved in water, ammonium sulfate (AS), ammonium nitrate (AN), urea + Cu + B, urea + adhesive + CaCO3, and urea + NBPT (all with three split applications), as well as blended N fertilizer, urea + elastic resin, urea-formaldehyde, and urea + polyurethane (all applied only once). NH3-N losses (mean of two crop seasons) were statistically higher for urea + adhesive + CaCO3 (27.9% of applied N) in comparison with the other treatments. Loss from prilled urea (23.7%) was less than from urea + adhesive + CaCO3. Losses from urea + NBPT (14.5%) and urea + Cu + B (13.5%) were similar and lower than those from prilled urea. Urea dissolved in water (4.2%) had even lower losses than those treatments, and the lowest losses were observed for AS (0.6%) and AN (0.5%). For the single application fertilizers, higher losses occurred for urea + elastic resin (5.8%), blended N fertilizer (5.5%), and urea + polyurethane (5.2%); and urea-formaldehyde had a lower loss (0.5%). Except for urea + adhesive + CaCO3, all N-fertilizer technologies reduced NH3-N losses compared to prilled urea.
A Comprehensive Study on EMI Shielding Performance of Carbon Nanomaterials-Embedded CFRP or GFRP Composites
Authors: Jang Daeik , Kim Bum-Jun , Nam Il-Woo
Published: December 2022

The rapid advancement of electrical and telecommunication facilities has resulted in increasing requirements for the development of electromagnetic interference (EMI) shielding composites. Accordingly, an experimental study was conducted to evaluate the EMI shielding performance of carbon nanomaterial (CNM)-embedded carbon-fiber-reinforced polymer (CFRP) or glass-fiber-reinforced polymer (GFRP) composites. Nine combinations of CNMs and carbon or glass fibers were used to fabricate the composites. The synergistic effects of CNMs on the EMI shielding performance were systematically investigated. The results indicated that plate-type CNMs (i.e., graphene and graphite nanoplatelets) have more prominent effects than fiber-type CNMs (carbon nanofibers). The composites fabricated with CFRP afforded higher EMI shielding than the GFRP-based composites. Among the eighteen samples, 3% CNT-GNP in CFRP composites, which included plate-typed CNM, exhibited the best EMI shielding performances, showing 38.6 dB at 0.7 GHz. This study helps understand the shielding performance of CNM-embedded CFRP and GFRP composites in electrical and telecommunication facilities.
pH-Dependent Release of Vancomycin from Modularly Assembled Collagen Laminates
Authors: Kilb Michelle Fiona , Ritz Ulrike , Nickel Daniela , Schmitz Katja
Published: December 2022

To prevent surgical site infections, antibiotics can be released from carriers made of biomaterials, such as collagen, that support the healing process and are slowly degraded in the body. In our labs we have developed collagen laminates that can be easily assembled and bonded on-site, according to medical needs. As shown previously, the asymmetric assembly leads to different release rates at the major faces of the laminate. Since the pH changes during the wound healing and infection, we further examined the effect of an acidic and alkaline pH, in comparison to pH 7.4 on the release of vancomycin from different collagen samples. For this purpose, we used an additively manufactured sample holder and quantified the release by HPLC. Our results show that the pH value does not have any influence on the total amount of released vancomycin (atelocollagen sponge pH 5.5: 71 ± 2%, pH 7.4: 68 ± 8%, pH 8.5: 74 ± 3%, bilayer laminate pH 5.5: 61 ± 6%, pH 7.4: 69 ± 4% and pH 8.5: 67 ± 3%) but on the time for half-maximal release. At an acidic pH of 5.5, the swelling of the atelocollagen sponge is largely increased, leading to a 2–3 h retarded release, compared to the physiological pH. No changes in swelling were observed at the basic pH and the compound release was 1–2 h delayed. These effects need to be considered when choosing the materials for the laminate assembly.
Optimizing the Composition Design of Cement-Based Expanded-Polystyrene (EPS) Exterior Wall Based on Thermal Insulation and Flame Retardance
Authors: Shi Jicun , Zhao Lei , Zhang Yao , Han Hongxing , Zhou Lihuang , Wang Chenxi
Published: December 2022

The use of thermal insulated decorative panel materials with low thermal conductivity and high flame retardance is a key step toward energy-saving buildings. However, traditional thermal insulation materials are always highly conductive and inflammable, which restricts their application for new buildings. This study aims to prepare the non-combustible, cement-based EPS mixtures with thermal conductivity lower than 0.045 and density less than 140 kg/m3 and characterize it with mechanical, thermal, and flame retardant properties. The effect of particle size, Silica coated and content of EPS on the physical, mechanical, thermal, and combustion performance are conducted in this paper. The comprehensive indoor tests including density, water absorbing, softening coefficient, compressive strength, tensile strength, moisture susceptibility, thermal conductivity, and scanning electron microscopy (SEM) along with combustion performance are reported to evaluate the effects of several variables on the investigated cement-based nonflammable EPS (CEPS)mixtures. The results show that small and gradation EPS particles significantly improve the comprehensive performance of mixtures. In addition, Silica coated ESP significantly improve the flame retardance of mixtures while reduce the mechanical characteristics slightly. These results contribute to the selection of appropriate materials to enhance the thermal insulation, flame retardance and mechanical properties of CEPS.
Low Power Consumption Hybrid-Integrated Thermo-Optic Switch with Polymer Cladding and Silica Waveguide Core
Authors: Xie Yuqi , Han Jiachen , Qin Tian , Ge Xuyang , Wu Xihan , Liu Lu , Wu Xubin , Yi Yunji
Published: December 2022

Taking advantage of the large thermo-optical coefficient of polymer materials, a hybrid-integrated thermo-optic switch was designed and simulated. It is also compatible with the existing silica-based planar light-wave circuit (PLC) platform. To further reduce the power consumption, we introduced the air trench structure and optimized the structural parameters of the heating region. This scheme is beneficial to solving the problem of the large driving power of silica-based thermo-optic switches at this stage. Compared with the switching power of all-silica devices, the power consumption can be reduced from 116.11 mW (TE) and 114.86 mW (TM) to 5.49 mW (TE) and 5.96 mW (TM), which is close to the driving power of the reported switches adopting polymer material as the core. For the TE mode, the switch’s rise and fall times were 121 µs and 329 µs. For the TM mode, the switch times were simulated to be 118 µs (rise) and 329 µs (fall). This device can be applied to hybrid integration fields such as array switches and reconfigurable add/drop multiplexing (ROADM) technology.
Enthalpic Interactions and Solution Behaviors of Solvent-Free Polymer Brushes
Authors: Chen Yi-Ju , Yu Hsiu-Yu
Published: December 2022

We performed molecular dynamics simulations to characterize the role of enthalpic interaction in impacting the static and dynamic properties of solvent-free polymer brushes. The intrinsic enthalpic interaction in the simulation was introduced using different attraction strengths between distinct species. Two model systems were considered: one consisting of binary brushes of two different polymer types and the other containing a mixture of homopolymer brushes and free molecules. In the first system, we observed that, when two originally incompatible polymers were grafted to opposing surfaces, the miscibility between them was significantly enhanced. A less favorable intrinsic enthalpic interaction in the brushes resulted in a more stretched chain configuration, a lower degree of inter-brush penetration, and faster segmental relaxation. In the second system, we characterized the solvent capacity of the homopolymer brushes from variations in the energy components of the system as a function of the number of free molecules. We determined that molecular absorption was driven by the release of the entropic frustration for the grafted chains in conjunction with the chemical affinity between the solutes and polymers. The solute distribution function within the inter-wall space showed that solute–polymer mixing in the middle of the gap occurred preferentially when the enthalpic interaction was more favorable. When this was not the case, absorption was predominantly localized near the grafting surface. From the mean square displacement of the solute, we found that the brush profiles restrained the molecular diffusion perpendicular to the grafting wall; the weaker the attraction from the brush, the higher the solute mobility.
Physicochemical Modifications on Thin Films of Poly(Ethylene Terephthalate) and Its Nanocomposite with Expanded Graphite Nanostructured by Ultraviolet and Infrared Femtosecond Laser Irradiation
Authors: Rodríguez-Beltrán René I. , Prada-Rodrigo Javier , Crespo Ana , Ezquerra Tiberio A. , Moreno Pablo , Rebollar Esther
Published: December 2022

In this work, the formation of laser-induced periodic surface structures (LIPSS) on the surfaces of thin films of poly(ethylene terephthalate) (PET) and PET reinforced with expanded graphite (EG) was studied. Laser irradiation was carried out by ultraviolet (265 nm) and near-infrared (795 nm) femtosecond laser pulses, and LIPSS were formed in both materials. In all cases, LIPSS had a period close to the irradiation wavelength and were formed parallel to the polarization of the laser beam, although, in the case of UV irradiation, differences in the formation range were observed due to the different thermal properties of the neat polymer in comparison to the composite. To monitor the modification of the physicochemical properties of the surfaces after irradiation as a function of the laser wavelength and of the presence of the filler, different techniques were used. Contact angle measurements were carried out using different reference liquids to measure the wettability and the solid surface free energies. The initially hydrophilic surfaces became more hydrophilic after ultraviolet irradiation, while they evolved to become hydrophobic under near-infrared laser irradiation. The values of the surface free energy components showed changes after nanostructuring, mainly in the polar component. Additionally, for UV-irradiated surfaces, adhesion, determined by the colloidal probe technique, increased, while, for NIR irradiation, adhesion decreased. Finally, nanomechanical properties were measured by the PeakForce Quantitative Nanomechanical Mapping method, obtaining maps of elastic modulus, adhesion, and deformation. The results showed an increase in the elastic modulus in the PET/EG, confirming the reinforcing action of the EG in the polymer matrix. Additionally, an increase in the elastic modulus was observed after LIPSS formation.
Diversity and Metabolic Potential of a PAH-Degrading Bacterial Consortium in Technogenically Contaminated Haplic Chernozem, Southern Russia
Authors: Delegan Yanina , Sushkova Svetlana , Minkina Tatiana , Filonov Andrey , Kocharovskaya Yulia , Demin Konstantin , Gorovtsov Andrey , Rajput Vishnu D. , Zamulina Inna , Grigoryeva Tatiana , Dudnikova Tamara , Barbashev Andrey , Maksimov Aleksey
Published: December 2022

Polycyclic aromatic hydrocarbons (PAHs) are chemically recalcitrant carcinogenic and mutagenic compounds with primarily anthropogenic origin. The investigation of the effects of emissions from energy enterprises on soil microbiomes is of a high priority for modern soil science. In this study, metagenomic profiling of technogenic contaminated soils was carried out based on bioinformatic analysis of shotgun metagenome data with PAH-degrading genes identification. The use of prokaryotic consortia has been often used as one of the bio-remediation approaches to degrade PAHs with different molecular weight. Since the process of PAH degradation predominantly includes non-culturable or yet-to-be cultured species, metagenomic approaches are highly recommended for studying the composition and metabolic abilities of microbial communities. In this study, whole metagenome shotgun sequencing of DNA from two soils with varying PAH levels was performed. In the control site, the total content of 12 priority PAHs was 262 µg kg−1. The background soil levels in the polluted site for PAHs with 3 or more rings exceeded this, at 800 µg kg−1. The abundance of genes and taxa associated with PAH degradation in these two sites were estimated. Despite differences in PAH concentrations up to 1200 µg kg−1, individual and operon-organized PAH degradation genes were almost equally abundant and diverse in pristine and highly contaminated areas. The most numerous taxa in both spots were actinobacteria from Terrabacteria group. In addition to well-known PAH degraders such as Gordonia and Rhodococcus, genes corresponding to the PAH degradation were found in Azoarcus, Burkholderia and Variovorax. The data shows non-specificity and multifunctionality of metabolic pathways encoded in the genes of PAH-degrading microorganisms.
Study on the Solid Production Mechanism of the Fractured Granite Reservoirs-Example of YL Area in Qiongdongnan Basin
Authors: Zuo Xiongdi , Wang Tao , Shi Lanling , Lei Lei , Wu Jian , Dou Liangbin , Li Tiantai
Published: December 2022

The granite buried hill gas reservoir in YL area of Qiongdongnan basin faces a serious problem of solid production, which seriously affects gas well production and reduces economic benefits; however, the solid production mechanism of fractured granite reservoirs is still unclear. In this study, the reasons for solid production were revealed and the mechanism was clarified based on the analysis of geological and mechanical characteristics of the granite buried hill reservoir. The solid production of fractured granite reservoirs can be divided into three modes, those being shedding of fracture filling and solid particles on the fracture wall, shear slip failure along the fracture, and shear failure of granite matrix. Take the YL area in the Qiongdongnan Basin as an example, the solid production of fractured granite reservoirs is mainly based on shedding of fracture filling and solid particles on the fracture wall and shear slip failure along the fracture, the possibility of shear failure of granite matrix is less. In addition, the closer the wellbore, the greater the risk of shedding of fracture filling and solid particles on the fracture wall. The high-angle fractures have a greater risk of shear slip failure. In addition, the direction of the minimum horizontal principal stress is higher risk of solid production. The research provides the basis and foundation for safe and efficient development of fractured granite reservoirs and for later measure selection and optimization.
Hierarchical Deep LSTM for Fault Detection and Diagnosis for a Chemical Process
Authors: Agarwal Piyush , Gonzalez Jorge Ivan Mireles , Elkamel Ali , Budman Hector
Published: December 2022

A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network (Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial plants. The proposed methodology has the ability to classify incipient faults that are difficult to detect and diagnose with traditional and many recent methods. Faults are grouped into different subsets according to the degree of difficulty to classify them accurately in the proposed hierarchical structure. External pseudo-random binary signals (PRBS) are injected in the system to enhance the identification of incipient faults. The approach is illustrated on the benchmark process (Tennessee Eastman Process) in order to compare across different methodologies. The efficacy of the proposed method is shown by a comprehensive comparison between many recent and traditional fault detection and diagnosis methods in the literature for Tennessee Eastman Process. The proposed work results in significant improvements in the classification of faults over both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.
Anxiety Sensitivity Social Concerns Predicts Electrodermal Activity during the Niacin Biological Challenge Paradigm
Authors: Saulnier Kevin G. , Volarov Marija , Allan Nicholas P.
Published: December 2022

Anxiety sensitivity social concerns (ASSC), or the fear of observable anxiety symptoms, is a risk factor for social anxiety. ASSC predicts anxiety following the niacin biological challenge, a paradigm in which niacin is used to manipulate facial flushing during a speech task. However, it remains unclear if ASSC predicts physiological arousal during this task. The current study was designed to examine the effects of ASSC on self-reported distress and electrodermal activity (EDA) during the niacin biological challenge in a sample of undergraduates (N = 36; M age = 18.9, SD = 0.84; 69.4% female). Participants were randomly assigned to one of four conditions in a 2 (100 mg niacin vs. 100 mg sugar) × 2 (instructional set) design. Participants completed a speech task in a virtual reality environment. Participants rated their distress halfway through the speech and EDA was averaged over four intervals. There was a main effect for ASSC on subjective distress. There was a significant ASSC by condition interaction predicting EDA, in that ASSC was related to EDA only in the niacin condition. ASSC also was more strongly related to EDA anticipating the speech. These findings highlight the role of ASSC in predicting anxiety and physiological arousal.
Paleoenvironmental Changes for the Last 3000 Cal Years BP in the Pueyrredón Lake Basin, Southern Patagonia, Argentina
Authors: Marcos Maria A. , Bamonte Florencia P. , Echeverria Marcos E. , Sottile Gonzalo D. , Mancini Maria V.
Published: December 2022

Patagonian shrub and ecotonal communities were sensitive to past environmental changes and thus may also be affected by future ones. Therefore, their paleoecological study constitutes a valuable tool to understand the way in which these plant communities respond to the forcings responsible for environmental variability. The aim of this paper is to reconstruct the vegetation dynamics of the Pueyrredón Lake area (47°25′55′′ S; 72°0.7′7′′ W) for the last 3000 cal yr BP and to contextualize these changes in a regional paleoclimatic framework. The results indicate that at the beginning of the 2900 cal yr BP, the vegetation in the northwest of Santa Cruz, Argentinian Patagonia, was represented by a grass-shrub steppe associated with forest–shrub steppe ecotonal elements. This information correlates with the larger-scale environmental inferences described for the period, which indicate an increase in moisture availability due to the weakening of the westerly winds. A marked change to arid conditions is indicated in the last 1050 cal yr BP, with the establishment and development of different shrub steppe communities and the lack of ecotonal elements. Although vegetation was sensitive to changes in moisture conditions related to the variability of the westerly winds, there is evidence of differences in the composition of shrub vegetation regarding the sequences analyzed. Variations in pollen proportions of the shrub steppes in the Pueyrredón Lake area suggest that changes in vegetation are not only due to climate variability but also local factors in the areas where shrub communities grow. The integration of the information with other Patagonian sequences allowed to frame these changes in a regional context. The results obtained provide useful information to understand the way vegetation changed in the past and the manner in which it may respond to future changes.
Explaining How Community Music Engagement Facilitates Social Cohesion through Ritualised Belonging
Authors: van der Merwe Liesl , Morelli Janelize
Published: December 2022

The flourishing society envisioned by the South African government’s National Development Plan 2030 is based on nation-building and social cohesion. With the recent civil unrests, calls for healing a nation characterised by poverty, inequality and violence through social cohesion have again been made. Community music engagement is uniquely positioned to achieve social cohesion since the discipline engages disparities of power and privilege whilst aiming to cultivate an environment of unconditional welcoming. The purpose of this theoretical framework is to explain how community music engagement can facilitate social cohesion through community music engagement. Community music engagement promotes spiritual experiences since it fosters relationships. This relational theoretical framework will be derived from a thematic analysis of the 21 chapters in the book Ritualised Belonging: Musicing and Spirituality in the South African Context and related theories. Our findings indicate that joyful musicking rituals serve as the catalyst for hope. Hope, in turn, motivates people to engage in community musicking, which requires a bodily co-presence, fosters mutual focus of attention and promotes cooperation and trust. Musickers who share values, challenges, culture, and identity experience a joyful sense of belonging. Furthermore, joy is key to spirituality since it is self-expansive, self-transcendent and other-embracing and transcends different religions. Joy moves musickers to build bonding and bridging social capital. Social capital improves individuals’ and communities’ quality of life and ultimately promotes social cohesion.
Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery
Authors: Qin Xingli , Zhao Lingli , Yang Jie , Li Pingxiang , Wu Bingfang , Sun Kaimin , Xu Yubin
Published: December 2022

Airborne SAR is an important data source for crop mapping and has important applications in agricultural monitoring and food safety. However, the incidence-angle effects of airborne SAR imagery decrease the crop mapping accuracy. An active pairwise constraint learning method (APCL) is proposed for constrained time-series clustering to address this problem. APCL constructs two types of instance-level pairwise constraints based on the incidence angles of the samples and a non-iterative batch-mode active selection scheme: the must-link constraint, which links two objects of the same crop type with large differences in backscattering coefficients and the shapes of time-series curves; the cannot-link constraint, which links two objects of different crop types with only small differences in the values of backscattering coefficients. Experiments were conducted using 12 time-series images with incidence angles ranging from 21.2° to 64.3°, and the experimental results prove the effectiveness of APCL in improving crop mapping accuracy. More specifically, when using dynamic time warping (DTW) as the similarity measure, the kappa coefficient obtained by APCL was increased by 9.5%, 8.7%, and 5.2% compared to the results of the three other methods. It provides a new solution for reducing the incidence-angle effects in the crop mapping of airborne SAR time-series images.
Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network
Authors: Xiao Xiayang , Jia Hecheng , Xiao Penghao , Wang Haipeng
Published: December 2022

Due to the unique imaging mechanism of synthetic aperture radar (SAR), targets in SAR images often shows complex scattering characteristics, including unclear contours, incomplete scattering spots, attitude sensitivity, etc. Automatic aircraft detection is still a great challenge in SAR images. To cope with these problems, a novel approach called adaptive deformable network (ADN) combined with peak feature fusion (PFF) is proposed for aircraft detection. The PFF is designed for taking full advantage of the strong scattering features of aircraft, which consists of peak feature extraction and fusion. To fully exploit the strong scattering features of the aircraft in SAR images, peak features are extracted via the Harris detector and the eight-domain pixel detection of local maxima. Then, the saliency of aircraft under multiple imaging conditions is enhanced by multi-channel blending. All the PFF-preprocessed images are fed into the ADN for training and testing. The core components of ADN contain an adaptive spatial feature fusion (ASFF) module and a deformable convolution module (DCM). ASFF is utilized to reconcile the inconsistency across different feature scales, raising the characterization capabilities of the feature pyramid and improving the detection performance of multi-scale aircraft further. DCM is introduced to determine the 2-D offsets of feature maps adaptively, improving the geometric modeling abilities of aircraft in various shapes. The well-designed ADN is established by combining the two modules to alleviate the problems of the multi-scale targets and attitude sensitivity. Extensive experiments are conducted on the GaoFen-3 (GF3) dataset to demonstrate the effectiveness of the PFF-ADN with an average precision of 89.34%, as well as an F1-score of 91.11%. Compared with other mainstream algorithms, the proposed approach achieves state-of-the-art performance.
Extrinsic Calibration for LiDAR–Camera Systems Using Direct 3D–2D Correspondences
Authors: Yi Hao , Liu Bo , Zhao Bin , Liu Enhai
Published: December 2022

Recent advances in the fields of driverless cars, intelligent robots and remote-sensing measurement have shown that the use of LiDAR fused with cameras can provide more comprehensive and reliable sensing of surroundings. However, since it is difficult to extract features from sparse LiDAR data to create 3D–2D correspondences, finding a method for accurate external calibration of all types of LiDAR with cameras has become a research hotspot. To solve this problem, this paper proposes a method to directly obtain the 3D–2D correspondences of LiDAR–camera systems to complete accurate calibration. In this method, a laser detector card is used as an auxiliary tool to directly obtain the correspondences between laser spots and image pixels, thus solving the problem of difficulty in extracting features from sparse LiDAR data. In addition, a two-stage framework from coarse to fine is designed in this paper, which not only can solve the perspective-n-point problem with observation errors, but also requires only four LiDAR data points and the corresponding pixel information for more accurate external calibration. Finally, extensive simulations and experimental results show that the effectiveness and accuracy of our method are better than existing methods.
EMO-MVS: Error-Aware Multi-Scale Iterative Variable Optimizer for Efficient Multi-View Stereo
Authors: Zhou Huizhou , Zhao Haoliang , Wang Qi , Lei Liang , Hao Gefei , Xu Yusheng , Ye Zhen
Published: December 2022

Efficient dense reconstruction of objects or scenes has substantial practical implications, which can be applied to different 3D tasks (for example, robotics and autonomous driving). However, because of the expensive hardware required and the overall complexity of the all-around scenarios, efficient dense reconstruction using lightweight multi-view stereo methods has received much attention from researchers. The technological challenge of efficient dense reconstruction is maintaining low memory usage while rapidly and reliably acquiring depth maps. Most of the current efficient multi-view stereo (MVS) methods perform poorly in efficient dense reconstruction, this poor performance is mainly due to weak generalization performance and unrefined object edges in the depth maps. To this end, we propose EMO-MVS, which aims to accomplish multi-view stereo tasks with high efficiency, which means low-memory consumption, high accuracy, and excellent generalization performance. In detail, we first propose an iterative variable optimizer to accurately estimate depth changes. Then, we design a multi-level absorption unit that expands the receptive field, which efficiently generates an initial depth map. In addition, we propose an error-aware enhancement module, enhancing the initial depth map by optimizing the projection error between multiple views. We have conducted extensive experiments on challenging datasets Tanks and Temples and DTU, and also performed a complete visualization comparison on the BlenedMVS validation set (which contains many aerial scene images), achieving promising performance on all datasets. Among the lightweight MVS methods with low-memory consumption and fast inference speed, our F-score on the online Tanks and Temples intermediate benchmark is the highest, which shows that we have the best competitiveness in terms of balancing the performance and computational cost.
A New Magnetic Target Localization Method Based on Two-Point Magnetic Gradient Tensor
Authors: Liu Gaigai , Zhang Yingzi , Wang Chen , Li Qiang , Li Fei , Liu Wenyi
Published: December 2022

The existing magnetic target localization methods are greatly affected by the geomagnetic field and exist approximation errors. In this paper, a two-point magnetic gradient tensor localization model is established by using the spatial relation between the magnetic target and the observation points derived from magnetic gradient tensor and tensor invariants. Based on the model, the equations relating to the position vector of magnetic target are constructed. Solving the equations, a new magnetic target localization method using only a two-point magnetic gradient tensor and no approximation errors is achieved. To accurately evaluate the localization accuracy of the method, a circular trajectory that varies in all three directions is proposed. Simulation results show that the proposed method is almost error-free in the absence of noise. After adding noise, the maximum relative error percentage is reduced by 28.4% and 2.21% compared with the single-point method and the other two-point method, respectively. Furthermore, the proposed method is not affected by the variation in the distance between two observation points. At a detection distance of 20 m, the maximum localization error is 1.86 m. In addition, the experiments also verify that the new method can avoid the influence of the geomagnetic field and the variation in the distance, and achieve high localization accuracy. The average relative error percentage in the y-direction is as low as 3.78%.
Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
Authors: Li Chendong , Hancock Craig M. , Vadakke Veettil Sreeja , Zhao Dongsheng , Hamm Nicholas A. S.
Published: December 2022

Ionospheric scintillation is one of the main error sources of Global Navigation Satellite System (GNSS) positioning. The presence of scintillation may result in cycle slips, measurement errors or even losses of lock on satellites, eventually leading to complete failure of positioning. Typically, scintillation parameters S4 and σϕ are used to characterize amplitude and phase scintillation, respectively. However, the scintillation parameters can only be generated from data with a frequency of at least 1 Hz. Rate of change of total electron content index (ROTI) is often used as a proxy for scintillation parameters, which can be obtained from 1/30 Hz data. However, previous research has shown the inefficiency of ROTI to represent scintillation. Therefore, the multipath parameter (MP) has been proposed as another proxy for scintillation parameters, which can also be obtained from 1/30 Hz data. In this paper, both MP and ROTI (standard parameters) were used to mitigate scintillation effects on precise point positioning (PPP). To evaluate the effectiveness of MP and ROTI in mitigating scintillation effects, S4 and σϕ were also used for comparison and validation. Three strategies are proposed: (1) remove all observations from the satellite that is most affected by scintillation; (2) remove the scintillation-affected observations; (3) weight the measurement noise matrix in the Kalman Filter (KF) process. The results show that the observation removal and weighting strategies are considerably more effective than the satellite removal strategy. The results also show that the improvement of PPP outputs reaches 93.1% and the performance of standard parameters is comparable to that of scintillation parameters in the observation removal and weighting strategies.
Traffic Sign Recognition Based on the YOLOv3 Algorithm
Authors: Gong Chunpeng , Li Aijuan , Song Yumin , Xu Ning , He Weikai
Published: December 2022

Traffic sign detection is an essential component of an intelligent transportation system, since it provides critical road traffic data for vehicle decision-making and control. To solve the challenges of small traffic signs, inconspicuous characteristics, and low detection accuracy, a traffic sign recognition method based on improved (You Only Look Once v3) YOLOv3 is proposed. The spatial pyramid pooling structure is fused into the YOLOv3 network structure to achieve the fusion of local features and global features, and the fourth feature prediction scale of 152 × 152 size is introduced to make full use of the shallow features in the network to predict small targets. Furthermore, the bounding box regression is more stable when the distance-IoU (DIoU) loss is used, which takes into account the distance between the target and anchor, the overlap rate, and the scale. The Tsinghua–Tencent 100K (TT100K) traffic sign dataset’s 12 anchors are recalculated using the K-means clustering algorithm, while the dataset is balanced and expanded to address the problem of an uneven number of target classes in the TT100K dataset. The algorithm is compared to YOLOv3 and other commonly used target detection algorithms, and the results show that the improved YOLOv3 algorithm achieves a mean average precision (mAP) of 77.3%, which is 8.4% higher than YOLOv3, especially in small target detection, where the mAP is improved by 10.5%, greatly improving the accuracy of the detection network while keeping the real-time performance as high as possible. The detection network’s accuracy is substantially enhanced while keeping the network’s real-time performance as high as possible.
Mueller Matrix Microscopy for In Vivo Scar Tissue Diagnostics and Treatment Evaluation
Authors: Jütte Lennart , Roth Bernhard
Published: December 2022

Scars usually do not show strong contrast under standard skin examination relying on dermoscopes. They usually develop after skin injury when the body repairs the damaged tissue. In general, scars cause multiple types of distress such as movement restrictions, pain, itchiness and the psychological impact of the associated cosmetic disfigurement with no universally successful treatment option available at the moment. Scar treatment has significant economic impact as well. Mueller matrix polarimetry with integrated autofocus and automatic data registration can potentially improve scar assessment by the dermatologist and help to make the evaluation of the treatment outcome objective. Polarimetry can provide new physical parameters for an objective treatment evaluation. We show that Mueller matrix polarimetry can enable strong contrast for in vivo scar imaging. Additionally, our results indicate that the polarization stain images obtained form there could be a useful tool for dermatology. Furthermore, we demonstrate that polarimetry can be used to monitor wound healing, which may help prevent scarring altogether.
Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks
Authors: Darwish Abdel , Halkon Benjamin , Oberst Sebastian
Published: December 2022

Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.
SLAM Back-End Optimization Algorithm Based on Vision Fusion IPS
Authors: Xia Yu , Cheng Jingdi , Cai Xuhang , Zhang Shanjun , Zhu Junwu , Zhu Liucun
Published: December 2022

SLAM (Simultaneous Localization and Mapping) is mainly composed of five parts: sensor data reading, front-end visual odometry, back-end optimization, loopback detection, and map building. And when visual SLAM is estimated by visual odometry only, cumulative drift will inevitably occur. Loopback detection is used in classical visual SLAM, and if loopback is not detected during operation, it is not possible to correct the positional trajectory using loopback. Therefore, to address the cumulative drift problem of visual SLAM, this paper adds Indoor Positioning System (IPS) to the back-end optimization of visual SLAM, and uses the two-label orientation method to estimate the heading angle of the mobile robot as the pose information, and outputs the pose information with position and heading angle. It is also added to the optimization as an absolute constraint. Global constraints are provided for the optimization of the positional trajectory. We conducted experiments on the AUTOLABOR mobile robot, and the experimental results show that the localization accuracy of the SLAM back-end optimization algorithm with fused IPS can be maintained between 0.02 m and 0.03 m, which meets the requirements of indoor localization, and there is no cumulative drift problem when there is no loopback detection, which solves the problem of cumulative drift of the visual SLAM system to some extent.
Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals
Authors: Zambrana-Vinaroz David , Vicente-Samper Jose Maria , Manrique-Cordoba Juliana , Sabater-Navarro Jose Maria
Published: December 2022

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.
Towards New Generation, Biologically Plausible Deep Neural Network Learning
Authors: Apparaju Anirudh , Arandjelović Ognjen
Published: December 2022

Artificial neural networks in their various different forms convincingly dominate machine learning of the present day. Nevertheless, the manner in which these networks are trained, in particular by using end-to-end backpropagation, presents a major limitation in practice and hampers research, and raises questions with regard to the very fundamentals of the learning algorithm design. Motivated by these challenges and the contrast between the phenomenology of biological (natural) neural networks that artificial ones are inspired by and the learning processes underlying the former, there has been an increasing amount of research on the design of biologically plausible means of training artificial neural networks. In this paper we (i) describe a biologically plausible learning method that takes advantage of various biological processes, such as Hebbian synaptic plasticity, and includes both supervised and unsupervised elements, (ii) conduct a series of experiments aimed at elucidating the advantages and disadvantages of the described biologically plausible learning as compared with end-to-end backpropagation, and (iii) discuss the findings which should serve as a means of illuminating the algorithmic fundamentals of interest and directing future research. Among our findings is the greater resilience of biologically plausible learning to data scarcity, which conforms to our expectations, but also its lesser robustness to additive, zero mean Gaussian noise.
Determinants of Refugee Children’s Social Integration: Evidence from Lebanon, Turkey, and Australia
Authors: Hammoud Mohammad , Shuayb Maha , Crul Maurice
Published: December 2022

This paper investigates the determinants of refugee students’ social integration in Lebanon, Turkey, and Australia. This paper seeks to understand how legal status and the corresponding length of refugee asylum shape refugee children’s social integration. The three host countries offer refugees different legal statuses ranging from short-term in Lebanon, medium-term in Turkey, and long-term in Australia. Therefore, our data collection covers a sample of 1298 middle school refugee students from all three countries. Our probit regression analysis sheds light on the importance of micro-level factors related to individual and household characteristics and meso-level factors related to school factors shaping refugee students’ social integration. The statistical dominance of meso-level factors indicates that the within-country differences are stronger than the between-country differences, yet it does not rule out the importance of macro policies that indirectly influence refugee students’ social integration by shaping provisions at the micro and meso levels.
Study on Evolutionary Game of Rural E-Commerce Entrepreneurship Ecosystem with Governmental Participation
Authors: Wang Yanling , Xu Junqian , Zhang Guangsheng , Wang Xiao
Published: December 2022

Rural e-commerce has become an effective measure for rural economic development under the background of digital transformation. Governmental participation is an important driving force to encourage the formation of rural e-commerce entrepreneurial ecosystems. From the perspective of bounded rationality, this paper establishes a tripartite evolutionary game model of a rural e-commerce entrepreneurial ecosystem among e-commerce platform, entrepreneurs and government, and systematically analyzes the evolutionary stability of each participant’s strategic choice. The initial intention of the three parties and the coefficient of governmental reward and punishment are introduced into the model design. Through numerical simulation, the influence of different parameters on the strategic selection of the three parties and the evolution path are analyzed, further implying the complexity of the strategic selection process in the reality. The results show that subsidies for e-commerce platforms are more conducive to the evolution of entrepreneurs and e-commerce platforms in a positive direction than direct subsidies for entrepreneurs by the government. In the early stage of development of the rural e-commerce entrepreneurial ecosystem, the government behavior strategy evolved to be one of active participation. After the system matured, the government behavior turned toward the negative direction. Compared with governmental reward and punishment, the initial willingness of each subject has more significant influence on the evolution of the tripartite behavior strategy. Finally, this paper proposes some countermeasures and suggestions to optimize the game relationship among the main bodies in the rural e-commerce entrepreneurial ecosystem from the perspective of a government subsidy mode, effective government intervention and the promotion of the initial willingness of the three parties. The research ideas and conclusions of this paper are enlightening to the academic research and corporate practice related to rural e-commerce.