bluuebunny/crossref_metadata_2025 huggingface dataset:
Suppression of the Equivalent Magnetic Noise Caused by Electron Spin Polarization in a Xe Isotope Comagnetometer
Authors: Wu Zekun , Chai Zhen , Xiao Lan , Liu Zhanchao
Published: April 2023

The Xe isotope comagnetometer in the nuclear magnetic resonance regime can be used as a promising high-precision inertial measurement unit because of the absolute frequency measurement and high bandwidth. The fluctuation of the electron spin polarization leads to equivalent magnetic noise in the Xe isotope comagnetometer, which is one of the main factors limiting the stability of the comagnetometer. Here, we demonstrate systematic research of equivalent magnetic noise suppression and analyze the influence of the electron spin polarization on the Xe isotope comagnetometer. Based on the spin–exchange method between Xe isotopes and alkali metal atoms through the Fermi contact hyperfine interaction, the error equation of the Xe Larmor frequency is established. The equivalent magnetic noise can be suppressed by controlling the static magnetic field. This suppression method for Xe isotope comagnetometers improved the stability while maintaining high bandwidth. The experimental results show that this method can reduce the fluctuations of the 129Xe and 131Xe frequencies by 75% and 68.6%, respectively.
Versatile Tunning of Compact Microring Waveguide Resonator Based on Lithium Niobate Thin Films
Authors: Lin Qijing , Hu Yuanzhi , Li Yang , Chen Huajiang , Liu Runhao , Tian Gang , Qiu Wentao , Yang Tiefeng , Guan Heyuan , Lu Huihui
Published: April 2023

With the advancement of modulation technology and the requirement for device miniaturization and integration, lithium niobate on insulator (LNOI) can be a versatile platform for this pursuit, as it can confine the transmitted light at the nanoscale, leading to a strong light–matter interaction, which can sensitively capture external variations, such as electric fields and temperature. This paper presents a compact microring modulator with versatile tuning based on X-cut LNOI. The LNOI modulator equipped with electrodes with a coverage angle of 120∘ achieved a maximum electro-optic (EO) tuning efficiency of 13 pm/V and a maximum extinction ratio of 11 dB. The asymmetry in the static or quasi-static electro-optic tuning of the microring modulator was also analyzed. Furthermore, we measured the thermal-optic effect of the device with a sensitivity of 26.33 pm/∘C, which can potentially monitor the environment temperature or compensate for devices’ functional behavior. The demonstrated efficient and versatile compact microring modulator will be an important platform for on-chip active or passive photonic components, microring-based sensor arrays and integrated optics.
Influence of FFF Process Conditions on the Thermal, Mechanical, and Rheological Properties of Poly(hydroxybutyrate-co-hydroxy Hexanoate)
Authors: Caputo Maria Rosaria , Fernández Mercedes , Aguirresarobe Robert , Kovalcik Adriana , Sardon Haritz , Candal María Virginia , Müller Alejandro J.
Published: April 2023

Polyhydroxyalkanoates are natural polyesters synthesized by microorganisms and bacteria. Due to their properties, they have been proposed as substitutes for petroleum derivatives. This work studies how the printing conditions employed in fuse filament fabrication (FFF) affect the properties of poly(hydroxybutyrate-co-hydroxy hexanoate) or PHBH. Firstly, rheological results predicted the printability of PHBH, which was successfully realized. Unlike what usually happens in FFF manufacturing or several semi-crystalline polymers, it was observed that the crystallization of PHBH occurs isothermally after deposition on the bed and not during the non-isothermal cooling stage, according to calorimetric measurements. A computational simulation of the temperature profile during the printing process was conducted to confirm this behavior, and the results support this hypothesis. Through the analysis of mechanical properties, it was shown that the nozzle and bed temperature increase improved the mechanical properties, reducing the void formation and improving interlayer adhesion, as shown by SEM. Intermediate printing velocities produced the best mechanical properties.
Metal–Organic-Framework-Mediated Fast Self-Assembly 3D Interconnected Lignin-Based Cryogels in Deep Eutectic Solvent for Supercapacitor Applications
Authors: Lou Rui , Cao Qihang , Niu Taoyuan , Zhang Yiyi , Zhang Yanan , Wang Zhiwei , Zhang Xiao
Published: April 2023

A cost-effective and sustainable method is successfully developed to produce lignin-based cryogels with a mechanically robust 3D interconnected structure. A choline chloride–lactic acid (ChCl–LA)-based deep eutectic solvent (DES) is used as a cosolvent to promote the synthesis of lignin–resorcinol–formaldehyde (LRF) gels that can self-assemble a robust string-bead-like framework. The molar ratio of LA to ChCl in DES has a significant influence on the gelation time and properties of the ensuing gels. Moreover, it is discovered that doping the metal–organic framework (MOF) during the sol–gel process can greatly accelerate the gelation of lignin. It takes a mere 4 h to complete the LRF gelation process at a DES ratio of 1:5 combined with 5% MOF. This study yields LRF carbon cryogels doped with copper that exhibit 3D interconnected bead-like carbon spheres with a prominent micropore of 1.2 nm. A specific capacitance as high as 185 F g−1 can be obtained for the LRF carbon electrode at a current density of 0.5 A g−1, and it has an excellent long-term cycling stability. This study provides a novel method of synthesizing high-lignin-content carbon cryogels with promising potential for application in the field of energy storage devices.
Analytics in Extrusion-Based Bioprinting: Standardized Methods Improving Quantification and Comparability of the Performance of Bioinks
Authors: Strauß Svenja , Grijalva Garces David , Hubbuch Jürgen
Published: April 2023

Three-dimensional bioprinting and especially extrusion-based printing as a most frequently employed method in this field is constantly evolving as a discipline in regenerative medicine and tissue engineering. However, the lack of relevant standardized analytics does not yet allow an easy comparison and transfer of knowledge between laboratories regarding newly developed bioinks and printing processes. This work revolves around the establishment of a standardized method, which enables the comparability of printed structures by controlling for the extrusion rate based on the specific flow behavior of each bioink. Furthermore, printing performance was evaluated by image-processing tools to verify the printing accuracy for lines, circles, and angles. In addition, and complementary to the accuracy metrics, a dead/live staining of embedded cells was performed to investigate the effect of the process on cell viability. Two bioinks, based on alginate and gelatin methacryloyl, which differed in 1% (w/v) alginate content, were tested for printing performance. The automated image processing tool reduced the analytical time while increasing reproducibility and objectivity during the identification of printed objects. During evaluation of the processing effect of the mixing of cell viability, NIH 3T3 fibroblasts were stained and analyzed after the mixing procedure and after the extrusion process using a flow cytometer, which evaluated a high number of cells. It could be observed that the small increase in alginate content made little difference in the printing accuracy but had a considerable strong effect on cell viability after both processing steps.
Stimuli-Responsive Principles of Supramolecular Organizations Emerging from Self-Assembling and Self-Organizable Dendrons, Dendrimers, and Dendronized Polymers
Authors: Percec Virgil , Sahoo Dipankar , Adamson Jasper
Published: April 2023

All activities of our daily life, of the nature surrounding us and of the entire society and its complex economic and political systems are affected by stimuli. Therefore, understanding stimuli-responsive principles in nature, biology, society, and in complex synthetic systems is fundamental to natural and life sciences. This invited Perspective attempts to organize, to the best of our knowledge, for the first time the stimuli-responsive principles of supramolecular organizations emerging from self-assembling and self-organizable dendrons, dendrimers, and dendronized polymers. Definitions of stimulus and stimuli from different fields of science are first discussed. Subsequently, we decided that supramolecular organizations of self-assembling and self-organizable dendrons, dendrimers, and dendronized polymers may fit best in the definition of stimuli from biology. After a brief historical introduction to the discovery and development of conventional and self-assembling and self-organizable dendrons, dendrimers, and dendronized polymers, a classification of stimuli-responsible principles as internal- and external-stimuli was made. Due to the enormous amount of literature on conventional dendrons, dendrimers, and dendronized polymers as well as on their self-assembling and self-organizable systems we decided to discuss stimuli-responsive principles only with examples from our laboratory. We apologize to all contributors to dendrimers and to the readers of this Perspective for this space-limited decision. Even after this decision, restrictions to a limited number of examples were required. In spite of this, we expect that this Perspective will provide a new way of thinking about stimuli in all fields of self-organized complex soft matter.
LightGBM-Integrated PV Power Prediction Based on Multi-Resolution Similarity
Authors: Peng Yan , Wang Shichen , Chen Wenjin , Ma Junchao , Wang Chenxu , Chen Jingwei
Published: April 2023

Improving the accuracy of PV power prediction is conducive to PV participation in economic dispatch and power market transactions in the distribution network, as well as safe dispatch and operation of the grid. Considering that the selection of highly correlated historical data can improve the accuracy of PV power prediction, this study proposes an integrated PV power prediction method based on a multi-resolution similarity consideration that considers both trend similarity and detail similarity. Firstly, using irradiance as the similarity variable, similar-days were selected using grey correlation analysis to form a set of similar data to control the similarity, with the overall trend of the day to be predicted at a macro level. Using irradiance to calculate the similarity at each specific point in time via Euclidean distance, similar-times were identified to form another set of similar data to consider the degree of similarity in detail. The above approach enables the selection of similarity data for both resolutions. Then, a 1DCNN-LSTM prediction model that considers the feature correlation of different variables and the temporal dependence of a single variable was proposed. Three important features were selected by a random forest model as inputs to the prediction model, and two similar data training models with different resolutions were used to generate a photovoltaic power prediction model based on similar-days and similar-times. Ultimately, the learning of the two predictions integrated with LightGBM compensate for each other, generating highly accurate predictions that combine the advantages of multi-resolution similarity considerations. Actual operation data of a PV power station was used for verification. The simulation results show that the prediction effect of ensemble learning was better than that of the single 1DCNN-LSTM model. The proposed method was compared with other commonly used PV power prediction models. In the data case of this study, it was found that the proposed method reduced the prediction error rate by 1.48%, 11.4%, and 6.45%, compared to the LSTM, CNN, and BP, respectively. Experiments show that model prediction results considering the selection of similar data at multiple resolutions can provide more extensive information to an ensemble learner and reduce the deviation in model predictions. Therefore, the proposed method can provide a reference for PV integration into the grid and participation in market-based electricity trading, which is of great significance.
Liquid–Liquid Phase Separation of Two Non-Dissolving Liquids—A Mini Review
Authors: Dimitrijević Dragana , Bösenhofer Markus , Harasek Michael
Published: April 2023

The separation of immiscible liquids is critical in many industrial processes, such as water treatment, different extraction processes, the petroleum industry, food production, and medicine. This work provides an overview of present research on the separation of liquid mixtures. A brief summary of the thermodynamic basis is provided, covering phase equilibrium, phase diagrams, and thermodynamic properties of phases. Additionally, the fundamentals of dispersion, necessary for discussing liquid–liquid separation, are presented. Subsequently, different liquid–liquid separation methods are discussed, highlighting their advantages and limitations. These methods include decanters, coalescers, centrifugal separators, membranes and electro-coalescers for liquid–liquid separation. Phase properties, dispersion formation, and time and space constraints specify the most efficient separation method. Phase recycling is also briefly discussed as a method to reduce the environmental impact of liquid–liquid extraction with subsequent phase separation. In summary, liquid–liquid separation methods are compared and future perspectives of liquid–liquid separation are discussed.
Research on the Effect of Crucial Parameters on Ice Borehole Deformations Using a Proposed Computation Model
Authors: Wang Yafei , Zhong Xiuping , Nie Shuaishuai , Zhu Ying , Chen Chen
Published: April 2023

This paper proposes a model for determining deformation in the upper 50% of an ice borehole. Based on this model, the borehole deformation of DK-1 at Dome A was studied. Furthermore, the effects of surface temperature, temperature gradient, surface snow density, and drilling fluid density on borehole deformation were investigated. The results showed that borehole shrinking, expansion, and mixed existence occurred simultaneously in DK-1. Borehole deformation increased with increasing surface temperature, while temperature gradient had a minimal effect. Borehole deformation also increased with increasing surface snow density and decreasing drilling fluid density when the borehole shrank; however, the situation was reversed when the borehole expanded. The influence of ice temperature was dominant in deformation. However, when depth exceeded 1200 m, the sensitivity of ice borehole deformation in the direction of the minimum principal stress increased with an increase in drilling fluid density. This study provides meaningful guidance for polar-drilling engineering.
Predicting Final PCDD/F Emissions by Coupling Adsorption Model of Activated Carbon and Relationships of PCDD/Fs and Flue Gas Compositions
Authors: Cai Jianjun , Zhu Lingxia , Huang Da , Luo Ming , Tang Xingying , Liu Wangqi
Published: April 2023

Due to their low concentration and complex nature, the low-cost online and real-time monitoring of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) is a challenge. In this paper, based on a coupling adsorption model of activated carbon and the relationship of PCDD/Fs and flue gas, an online monitoring system with real-time control of final PCDD/F emissions was built for the tracing and control of PCDD/F emissions in municipal solid waste incinerators (MSWIs). According to the online monitoring system, the effects of activated carbon concentration, the specific surface area of activated carbon (AC), operating temperature, contact time, fly ash concentration, residence carbon concentration of fly ash, and the lime concentration of final PCDD/F emissions were discussed. These MSWI operating parameters for reaching standards of 0.1 ng international toxic equivalents (TEQ)/Nm3 and 0.05 ng TEQ/Nm3 were summarized by the predicting model based on several published models.
Influence of Chemical Grafting Method on the Performance of SiO2 Nanocomposite Pour Point Depressant
Authors: Sun ZhengNan , Yan Biao , Jing GuoLin , Yang YiHai , Li HongJing , Zhang FuNing
Published: April 2023

Differences in the synthesis methods can affect the performance of nanocomposite materials. The synthesis methods of SiO2-based nanocomposite pour point depressants synthesized by chemical hybrid are mostly in situ polymerization and chemical grafting. However, there are still some gaps in the synthesis of nanocomposites using chemical grafting. In this paper, the amino-modified Nano-SiO2 and octamethyl methacrylate-maleic anhydride copolymer was used to prepare PM18-g-NSiO2 by an amidation reaction, and the product was compared with PM18/SiO2 prepared by the solution blending method. The characterization results of FT-IR and SEM showed that the chemical bond formed between PM18 and NSiO2 and PM18-g-NSiO2 showed a more regular morphology structure. The results of rheological tests showed that the optimal concentration of PM18-g-NSiO2 and PM18/SiO2 is 300 mg·kg−1, but the decrease of PM18-g-NSiO2 is 19 °C, while the decrease of PM18/SiO2 is only 13 °C. The lowest viscosity of model oil added PM18-g-NSiO2 was only 214 mPa·s while PM18/SiO2 reached 453 mPa·s. The wax crystal structure after adding PM18-g-NSiO2 is also more regular and smaller compared to PM18/SiO2. We concluded that differences in the synthesis methods may lead to the different coverage of the polymer on the surface of the nanoparticle.
An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization
Authors: Liu Qing , Kosarirad Houman , Meisami Sajad , Alnowibet Khalid A. , Hoshyar Azadeh Noori
Published: April 2023

Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.
Sustainability Evaluation of Chinese Capital Cities Based on Urban Geographic Environment Index
Authors: Ning Xiaogang , Zhang Hanchao , Shao Zhenfeng , Huang Xiao , Wang Hao , Zhang Ruiqian , Hao Minghui
Published: April 2023

Environmental assessments are important tasks for the long-term, sustainable development of cities. With the rapid urbanization in China, it is crucial to establish a City Sustainability Index (CSI) and evaluate the environmental conditions in major cities. However, most of the existing major sustainability indices/indicators are not able to assess cities at diverse levels of development using common axes of evaluation. In this work, we incorporate urban built-up areas extracted from high-resolution remote sensing images as indicators to measure the degree of urban development in city sustainability evaluation and propose a comprehensive index, i.e., the Urban Geographic Environment Index (UGEI). In order to eliminate the impact of urban development levels, UGEI mainly consists of area-averaged indices which are calculated from original indices and urban built-up areas. We adopt a comprehensive weighting method by using the analytic hierarchy process (AHP) method to weigh the high-level indicators and using the entropy weighting method to weigh low-level indicators. We evaluate the environmental conditions of 30 China’s provincial capitals from the aspects of pressure, state, response, and overall status. In addition, we analyze how diverse types of indicators affect the values of UGEIs. The major findings are as follows: (1) About half of the provincial capitals in China have poor sustainability in environmental conditions, and more environmental protection measures should be taken in developing cities; (2) the environmental conditions of the provincial capitals in China present a four-region distribution pattern, namely, the northeastern coastal region, northwest inland region, southwest region, and southeast region; (3) and indices based on urban built-up extents can be common axes of evaluation for cities at diverse levels of development. The proposed UGEI can serve as an effective and reliable index for sustainability evaluation in environmental conditions.
Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?
Authors: Sellers Hana L. , Vargas Zesati Sergio A. , Elmendorf Sarah C. , Locher Alexandra , Oberbauer Steven F. , Tweedie Craig E. , Witharana Chandi , Hollister Robert D.
Published: April 2023

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.
Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study
Authors: Noori Hoshyar Azadeh , Rashidi Maria , Yu Yang , Samali Bijan
Published: April 2023

Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.
A Multi-Frame Superposition Detection Method for Dim-Weak Point Targets Based on Optimized Clustering Algorithm
Authors: Xu Chenyu , Zhao Erwei , Zheng Wei , Yang Zhen , Peng Xiaodong , Niu Wenlong
Published: April 2023

Long-range surveillance and early warning of space targets are significant factors in space security. Under remote observation conditions, the energy performance of the target is weak and subject to environmental and imaging process contamination. Most detection methods are aimed at targets with a high signal-to-noise ratio (SNR) or local contrast, and the detection performance for dim-weak small targets is poor; therefore, the target signal is often enhanced by energy accumulation. However, owing to the interference caused by the instability of the imaging system, energy accumulation errors occur in the target, resulting in the dispersion of the target energy, making detection a challenge. To solve the above problem, this study proposed a multi-frame superposition detection method for dim-weak point targets based on an optimized clustering algorithm by combining the clustering method with the inherent features of the target and using the difference between the target and noise energy distribution for detection. First, we simulated the multi-frame imaging process of the target post-disturbance and established an optical imaging system model of the dim-weak target. Subsequently, we used data dimension reduction and outlier removal to extract the target potential area. Finally, the data were sent to the clustering model for calculation and judgment. Given that the accuracy rate reaches 87.1% when the SNR is 1 dB, the experimental results show that the detection method proposed in this paper can effectively detect dim-weak targets with low SNR. In addition, there is a significant improvement in the detection performance of the receiver characteristic curve compared with other algorithms in the real scene, which further proves the superiority of the method in this paper.
Road-Side Individual Tree Segmentation from Urban MLS Point Clouds Using Metric Learning
Authors: Wang Pengcheng , Tang Yong , Liao Zefan , Yan Yao , Dai Lei , Liu Shan , Jiang Tengping
Published: April 2023

As one of the most important components of urban space, an outdated inventory of road-side trees may misguide managers in the assessment and upgrade of urban environments, potentially affecting urban road quality. Therefore, automatic and accurate instance segmentation of road-side trees from urban point clouds is an important task in urban ecology research. However, previous works show under- or over-segmentation effects for road-side trees due to overlapping, irregular shapes and incompleteness. In this paper, a deep learning framework that combines semantic and instance segmentation is proposed to extract single road-side trees from vehicle-mounted mobile laser scanning (MLS) point clouds. In the semantic segmentation stage, the ground points are filtered to reduce the processing time. Subsequently, a graph-based semantic segmentation network is developed to segment road-side tree points from the raw MLS point clouds. For the individual tree segmentation stage, a novel joint instance and semantic segmentation network is adopted to detect instance-level roadside trees. Two complex Chinese urban point cloud scenes are used to evaluate the individual urban tree segmentation performance of the proposed method. The proposed method accurately extract approximately 90% of the road-side trees and achieve better segmentation results than existing published methods in both two urban MLS point clouds. Living Vegetation Volume (LVV) calculation can benefit from individual tree segmentation. The proposed method provides a promising solution for ecological construction based on the LVV calculation of urban roads.
Research on Scale Improvement of Geochemical Exploration Based on Remote Sensing Image Fusion
Authors: Ding Haifeng , Jing Linhai , Xi Mingjie , Bai Shi , Yao Chunyan , Li Lu
Published: April 2023

Both remote sensing and geochemical exploration technologies are effective tools for detecting target objects. Although information on anomalous geochemical elemental abundances differs in terms of professional attributes from remote sensing data, both are based on geological bodies or phenomena on the Earth’s surface. Therefore, exploring the use of remote sensing data with high spatial resolution to improve the accuracy of small-scale geochemical data, and fusing them to obtain large-scale geochemical layers could provide new data for geological and mineral exploration through inversion. This study provides a method of fusing remote sensing images with small-scale geochemical data based on a linear regression model that improves the resolution of geochemical elemental layers and provides reference data for mineral exploration in areas lacking large-scale geochemical data. In the Xianshuigou area of Northwest China, a fusion study was conducted using 200,000 geochemical and remote sensing data. The method provides fused large-scale regional chemical data in well-exposed areas where large-scale geochemical data are lacking and could provide potential data sources for regional mineral exploration.
Reconstructing the Geometry of the Yushu Fault in the Tibetan Plateau Using TLS, GPR and Trenching
Authors: Zhang Di , Li Jiacun , Wu Zhonghai , Ren Lili
Published: April 2023

Although geomorphic evidence and shallow geometry of active faults are significant for the understanding and assessing of fault activity and seismic hazards, it is challenging to acquire high-resolution topographic data and shallow geometry of the Yushu fault by conventional methods. Here, we present a case study to reconstruct the detailed surficial and subsurface geometry of the Yushu fault using terrestrial laser scanning (TLS), multi-frequency ground penetrating radar (GPR) and trenching. TLS was suitable for measuring the high-resolution three-dimensional (3D) topographic data of the fault. GPR surveys with different frequency antennas (25 MHz, 100 MHz, 250 MHz and 500 MHz) were conducted to image the shallow geometry of active faults at different depths and spatial resolutions. The typical groove landscape, parallel to surface traces of the fault, was clearly observed on the TLS-derived data. A ~40 m width narrow fault system and three faults were identified on the different frequency GPR profiles. Furthermore, faults F1 and F2 were supposed to be boundary faults but were sinistral-lateral strike-slip faults with a normal component, while fault F3 was inferred as the secondary fault. The western trench section, despite the limited investigation depth (~2 m), was well consistent with the 500 MHz GPR result, especially in the location of fault F2. Finally, a 3D surficial and subsurface model was established from the TLS-derived data and GPR data offering multi-sensor and multi-view spatial data to characterize and understand the fault’s kinematics and characteristics. In addition, the shallow geometry of the fault on the GPR results would be better interpreted with the help of the corresponding surficial data. The study results demonstrate that a combination of TLS, multi-frequency GPRs and trenching can be successfully used for reconstructing a detailed surficial and subsurface geometry of the Yushu fault. It will play an increasing role in comprehensive understanding and assessing fault behavior and seismic hazards, especially on the Tibetan Plateau and the adjacent area.
The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam
Authors: Liang Min-Chih , Chen Hung-En , Tfwala Samkele S. , Lin Yu-Feng , Chen Su-Chin
Published: April 2023

Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there are hardly any monitoring methods that use wireless underground transmission to monitor the water content inside earth dams. Real-time monitoring of changes in the soil moisture content can more directly determine the water level of seepage. Wireless transmission of sensors buried underground requires signal transmission through the soil medium, which is more complex than traditional air transmission. Henceforth, this study establishes a wireless underground transmission sensor that overcomes the distance limitation of underground transmission through a hop network. A series of feasibility tests were conducted on the wireless underground transmission sensor, including peer-to-peer transmission tests, multi-hop underground transmission tests, power management tests, and soil moisture measurement tests. Finally, field seepage tests were conducted to apply wireless underground transmission sensors to monitor the internal seepage water level before an earth dam failure. The findings show that wireless underground transmission sensors can achieve the monitoring of seepage water levels inside earth dams. In addition, the results supersede those of a conventional water level gauge. This could be crucial in early warning systems during the era of climate change, which has caused unprecedented flooding events.
Investigation of Self-Powered IoT Sensor Nodes for Harvesting Hybrid Indoor Ambient Light and Heat Energy
Authors: Xiao Heng , Qi Nanjian , Yin Yajiang , Yu Shijie , Sun Xiangzheng , Xuan Guozhe , Liu Jie , Xiao Shanpeng , Li Yuan , Li Yizheng
Published: April 2023

Sensor nodes are critical components of the Internet of Things (IoT). Traditional IoT sensor nodes are typically powered by disposable batteries, making it difficult to meet the requirements for long lifetime, miniaturization, and zero maintenance. Hybrid energy systems that integrate energy harvesting, storage, and management are expected to provide a new power source for IoT sensor nodes. This research describes an integrated cube-shaped photovoltaic (PV) and thermal hybrid energy-harvesting system that can be utilized to power IoT sensor nodes with active RFID tags. The indoor light energy was harvested using 5-sided PV cells, which could generate 3 times more energy than most current studies using single-sided PV cells. In addition, two vertically stacked thermoelectrical generators (TEG) with a heat sink were utilized to harvest thermal energy. Compared to one TEG, the harvested power was improved by more than 219.48%. In addition, an energy management module with a semi-active configuration was designed to manage the energy stored by the Li-ion battery and supercapacitor (SC). Finally, the system was integrated into a 44 mm × 44 mm × 40 mm cube. The experimental results showed that the system was able to generate a power output of 192.48 µW using indoor ambient light and the heat from a computer adapter. Furthermore, the system was capable of providing stable and continuous power for an IoT sensor node used for monitoring indoor temperature over a prolonged period.
An Enhanced Offset Tracking Method: Providing Auxiliary Information for DInSAR Phase Filtering in Urban Areas
Authors: Liu Qingyu , Lv Xiaoqi , Huang Pingping , Xu Wei
Published: April 2023

In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results.
Clinical Evaluation in Parkinson’s Disease: Is the Golden Standard Shiny Enough?
Authors: Kanellos Foivos S. , Tsamis Konstantinos I. , Rigas Georgios , Simos Yannis V. , Katsenos Andreas P. , Kartsakalis Gerasimos , Fotiadis Dimitrios I. , Vezyraki Patra , Peschos Dimitrios , Konitsiotis Spyridon
Published: April 2023

Parkinson’s disease (PD) has become the second most common neurodegenerative condition following Alzheimer’s disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson’s disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients’ quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients’ quality of life.
Video Process Mining and Model Matching for Intelligent Development: Conformance Checking
Authors: Chen Shuang , Zou Minghao , Cao Rui , Zhao Ziqi , Zeng Qingtian
Published: April 2023

Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (GED_NAR). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs.
An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem
Authors: Meng Leilei , Cheng Weiyao , Zhang Biao , Zou Wenqiang , Fang Weikang , Duan Peng
Published: April 2023

In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated.
Robot Pose Estimation and Normal Trajectory Generation on Curved Surface Using an Enhanced Non-Contact Approach
Authors: Shah Syed Humayoon , Lin Chyi-Yeu , Tran Chi-Cuong , Ahmad Anton Royanto
Published: April 2023

The use of robots for machining operations has become very popular in the last few decades. However, the challenge of the robotic-based machining process, such as surface finishing on curved surfaces, still persists. Prior studies (non-contact- and contact-based) have their own limitations, such as fixture error and surface friction. To cope with these challenges, this study proposes an advanced technique for path correction and normal trajectory generation while tracking a curved workpiece’s surface. Initially, a key-point selection approach is used to estimate a reference workpiece’s coordinates using a depth measuring tool. This approach overcomes the fixture errors and enables the robot to track the desired path, i.e., where the surface normal trajectory is needed. Subsequently, this study employs an attached RGB-D camera on the end-effector of the robot for determining the depth and angle between the robot and the contact surface, which nullifies surface friction issues. The point cloud information of the contact surface is employed by the pose correction algorithm to guarantee the robot’s perpendicularity and constant contact with the surface. The efficiency of the proposed technique is analyzed by carrying out several experimental trials using a 6 DOF robot manipulator. The results reveal a better normal trajectory generation than previous state-of-the-art research, with an average angle and depth error of 1.8 degrees and 0.4 mm.
Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions
Authors: Meno Laura , Escuredo Olga , Abuley Isaac K. , Seijo M. Carmen
Published: April 2023

Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic fungicides, moving away from a sustainable production system. In support of integrated pest management practices, machine learning algorithms were proposed as tools to forecast aerobiological risk level (ARL) of Phytophthora infestans (>10 sporangia/m3) as inoculum to new infections. For this, meteorological and aerobiological data were monitored during five potato crop seasons in Galicia (northwest Spain). Mild temperatures (T) and high relative humidity (RH) were predominant during the foliar development (FD), coinciding with higher presence of sporangia in this phenological stage. The infection pressure (IP), wind, escape or leaf wetness (LW) of the same day also were significantly correlated with sporangia according to Spearman’s correlation test. ML algorithms such as random forest (RF) and C5.0 decision tree (C5.0) were successfully used to predict daily sporangia levels, with an accuracy of the models of 87% and 85%, respectively. Currently, existing late blight forecasting systems assume a constant presence of critical inoculum. Therefore, ML algorithms offer the possibility of predicting critical levels of Phytophthora infestans concentration. The inclusion of this type of information in forecasting systems would increase the exactitude in the estimation of the sporangia of this potato pathogen.
Design of Wideband High-Gain Patch Antenna Array for High-Temperature Applications
Authors: Li Ruibo , Li Peng , Rocca Paolo , Salas Sánchez Aarón Ángel , Song Liwei , Li Xinghua , Xu Wanye , Fan Zijiao
Published: April 2023

A low-profile, wideband, and high-gain antenna array, based on a novel double-H-shaped slot microstrip patch radiating element and robust against high temperature variations, is proposed in this work. The antenna element was designed to operate in the frequency range between 12 GHz and 18.25 GHz, with a 41.3% fractional bandwidth (FBW) and an obtained peak gain equal to 10.2 dBi. The planar array, characterized by a feed network with a flexible 1 to 16 power divider, comprised 4 × 4 antenna elements and generated a pattern with a peak gain of 19.1 dBi at 15.5 GHz. An antenna array prototype was fabricated, and the measurements showed good agreement with the numerical simulations as the manufactured antenna operated in the range of 11.4–17 GHz, with a 39.4% FBW, and the peak gain at 15.5 GHz was 18.7 dBi. The high-temperature simulated and experimental results, performed in a temperature chamber, demonstrated that the array performance was stable in a wide temperature range, from −50 °C to 150 °C.
Hydrogen Gas Sensing Properties of Mixed Copper–Titanium Oxide Thin Films
Authors: Mańkowska Ewa , Mazur Michał , Domaradzki Jarosław , Mazur Piotr , Kot Małgorzata , Flege Jan Ingo
Published: April 2023

Hydrogen is an efficient source of clean and environmentally friendly energy. However, because it is explosive at concentrations higher than 4%, safety issues are a great concern. As its applications are extended, the need for the production of reliable monitoring systems is urgent. In this work, mixed copper–titanium oxide ((CuTi)Ox) thin films with various copper concentrations (0–100 at.%), deposited by magnetron sputtering and annealed at 473 K, were investigated as a prospective hydrogen gas sensing material. Scanning electron microscopy was applied to determine the morphology of the thin films. Their structure and chemical composition were investigated by X-ray diffraction and X-ray photoelectron spectroscopy, respectively. The prepared films were nanocrystalline mixtures of metallic copper, cuprous oxide, and titanium anatase in the bulk, whereas at the surface only cupric oxide was found. In comparison to the literature, the (CuTi)Ox thin films already showed a sensor response to hydrogen at a relatively low operating temperature of 473 K without using any extra catalyst. The best sensor response and sensitivity to hydrogen gas were found in the mixed copper–titanium oxides containing similar atomic concentrations of both metals, i.e., 41/59 and 56/44 of Cu/Ti. Most probably, this effect is related to their similar morphology and to the simultaneous presence of Cu and Cu2O crystals in these mixed oxide films. In particular, the studies of surface oxidation state revealed that it was the same for all annealed films and consisted only of CuO. However, in view of their crystalline structure, they consisted of Cu and Cu2O nanocrystals in the thin film volume.