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The hydrothermal dolomitization, facilitated by basement fault activities, had an important impact on the Permian Maokou Formation dolomite in the Sichuan Basin, which experienced complex diagenesis and presented strong reservoir heterogeneity. The source and age of diagenetic fluids in this succession remain controversial. In this study, various analyses were implemented on samples collected from outcrops and wells near the No. 15 fault in the eastern Sichuan Basin to reconstruct the multi-stage fluid activity and analyze the impact on reservoir development, including petrology, micro-domain isotopes, rare earth elements, homogenization temperature of fluid inclusions, and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) U-Pb dating. The homogenization temperature of primary brine inclusions in fine-grained matrix dolomite and saddle dolomite is concentrated between 100 and 150 °C, which indicates that the impacts of abnormally high temperatures of other geological bodies. The δ13C and δ18O value and low 87Sr/86Sr value indicate that the diagenetic fluid of fine-grained matrix dolomite is mainly Permian seawater. The U-Pb ages of fine-grained matrix dolomite are ~260 Ma, which coincides with the age of the main magmatism of Emeishan Large Igneous Province (ELIP), and hydrothermal fluid provided a favorable high-temperature environment in the penecontemporaneous stage. While highly radiogenic 87Sr/86Sr compositions suggests those of saddle dolomite, the high-temperature Sr-rich formation fluid. The U-Pb ages of saddle dolomite are 245–250 Ma, which coincides with the age of the 255~251 Ma magmatism of ELIP. This indicates that those should be the diagenetic products of the ELIP hydrothermal fluid in the shallow burial stage. The U-Pb age of coarse-grained calcite is 190–220 Ma, and it should be the diagenetic product of the deep burial stage. Brine inclusions associated with primary methane inclusions were developed in coarse-grained calcite, with a homogenization temperature range of 140.8–199.8 °C, which indicates that the formation fluid activities were related to hydrocarbon charging. The Permian Maokou Formation dolomite was firstly formed in the penecontemporaneous shallow burial stage, and then it was subjected to further hydrothermal dolomitization due to the basement faulting and the abnormally high heat flow during the active period of ELIP. Hydrothermal dolomitization contributed to the formation and maintenance of intercrystalline and dissolution pores, whereas it also formed saddle dolomite to fill the pores, and reduce the pore space. The influence of deep fluid activities on reservoir evolution is further distinguished.

Low temperatures pose a significant threat to plant growth and development. Studies have shown that aquaporins (AQPs), as the main functional proteins on the cell membrane regulating water ingress and egress, play a vital role in maintaining dynamic water balance when plants face cold stress. Catalpa bungei, an important timber and ornamental tree species, has its cultivation range significantly limited by its poor cold tolerance. However, no study has been found aiming to identify its aquaporin gene family. This study aims to fill this gap using two C. bungei cultivars with differing cold tolerance as experimental material: “Qiuza 1”, which is less cold-tolerant, and “Qiuza 2”, which is more cold-tolerant. The plants were subjected to low-temperature stress at 4 °C for 24 h. Using high-throughput molecular sequencing technology, a transcriptome sequencing of the leaves was performed at 0, 6, 12, and 18 h of cold stress. Fifteen candidate aquaporin genes in C. bungei (CbAQP) were identified. Phylogenetic analysis showed that the CbAQP gene family is divided into five subfamilies: 5 PIPs, 4 TIPs, 3 NIPs, 2 SIPs, and 1 XIP. By analyzing AQPs related to cold stress in other plants and the expression patterns of CbAQP genes, 12 CbAQP genes related to cold stress were identified. The genes that responded positively include CbPIP2;5, CbPIP1;2, CbTIP4;1, and CbNIP2;1. The results provide a foundation for further analysis of the biological functions of candidate CbAQP genes related to cold tolerance and offer theoretical support for improving seedling quality, cold-resistant genetic breeding, and expanding its distribution range.

We address the problem of data augmentation in a rotating turbulence set-up, a paradigmatic challenge in geophysical applications. The goal is to reconstruct information in two-dimensional (2D) cuts of the three-dimensional flow fields, imagining spatial gaps present within each 2D observed slice. We evaluate the effectiveness of different data-driven tools, based on diffusion models (DMs), a state-of-the-art generative machine learning protocol, and generative adversarial networks (GANs), previously considered as the best-performing method both in terms of point-wise reconstruction and the statistical properties of the inferred velocity fields. We focus on two different DMs recently proposed in the specialized literature: (i) RePaint, based on a heuristic strategy to guide an unconditional DM for flow generation by using partial measurements data, and (ii) Palette, a conditional DM trained for the reconstruction task with paired measured and missing data. Systematic comparison shows that (i) DMs outperform the GAN in terms of the mean squared error and/or the statistical accuracy; (ii) Palette DM emerges as the most promising tool in terms of both point-wise and statistical metrics. An important property of DMs is their capacity for probabilistic reconstructions, providing a range of predictions based on the same measurements, enabling uncertainty quantification and risk assessment.

A gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear system; however, the randomness and large disturbances make it difficult to control the variables precisely. In order to solve the problem of precise process control for multi-input and multi-output coupled systems with flow, pressure, and temperature, this article conducts the following research: (1) Designing a secondary circuit for waste hot water and establishing a water-circulating gas turbine cooling system to improve the efficiency of waste heat utilization. (2) Identifying the coupled system model and establishing a mathematical model of the coupling relationship based on the characteristic data of input and output signals in the gas turbine cooling system. (3) Designing a coupled-system decoupling compensator to weaken the relationships between variables, realizing the decoupling between coupled variables. (4) An Opposition-based Learning Jumping Spider Optimization Algorithm is proposed to be combined with the PID control algorithm, and the parameters of the PID controller are adjusted to solve the intelligent control problems of heat exchanger water inlet flow rate, pressure, and temperature in the gas turbine cooling system. After simulation verification, the gas turbine cooling system based on an Opposition-based Learning Jumping Spider Optimization Algorithm can realize the constant inlet flow rate, with an error of no more than 1 m3/h, constant inlet water temperature, with an error of no more than 0.2 °C, and constant main-pipe pressure, with an error of no more than 0.01 MPa. Experimental results show that a gas turbine cooling system based on the Opposition-based Learning Jumping Spider Optimization Algorithm can accurately realize the internal variable controls. At the same time, it can provide a reference for decoupling problems in strongly coupled systems, the controller parameter optimization problems, and process control problems in complex systems.

Localized turbulence is common in geophysical flows, where the roles of rotation and stratification are paramount. In this study, we investigate the evolution of a stratified turbulent cloud under rotation. Recognizing that a turbulent cloud is composed of vortices of varying scales and shapes, we start our investigation with a single eddy using analytical solutions derived from a linearized system. Compared to an eddy under pure rotation, the stratified eddy shows the physical manifestation of a known potential vorticity mode, appearing as a static stable vortex. In addition, the expected shift from inertial waves to inertial-gravity waves is observed. In our numerical simulations of the turbulent cloud, carried out at a constant Rossby number over a range of Froude numbers, stratification causes columnar structures to deviate from vertical alignment. This deviation increases with increasing stratification, slowing the expansion rate of the cloud. The observed characteristics of these columnar structures are consistent with the predictions of linear theory, particularly in their tilt angles and vertical growth rates, suggesting a significant influence of inertial-gravity waves. Using Lagrangian particle tracking, we have identified regions where wave activity dominates over turbulence. In scenarios of milder stratification, these inertial-gravity waves are responsible for a significant energy transfer away from the turbulent cloud, a phenomenon that attenuates with increasing stratification.

Tremors affect pediatric and adult populations, with roughly 3% of people worldwide experiencing essential tremors. Treatments include medication, deep brain stimulation, occupational/physical therapy, or adaptive equipment. This unblinded experimental pre-test–post-test study was performed (April–September 2021) at Children’s Health of Orange County, evaluating the effectiveness of Move-D, a novel orthotic brace, on pediatric tremors. Ten participants (14–19 years old) experiencing upper extremity tremors (5 essential, 2 dystonic, 1 coarse, 1 postural, and 1 unspecified) were enrolled. Participants completed a usability survey and performance was measured utilizing the Bruininks–Oseretsky Test of Motor Proficiency, second edition, with and without the brace, using one-sided t-tests of mean differences. Move-D improved age-equivalent scores for fine motor precision by 20.5 months and upper limb coordination by 15.1 months. Manual coordination percentile rankings increased by 2.9%. Manual dexterity performance was unaffected. The usability survey revealed that 7/10 participants agreed or strongly agreed that they could move their arm freely while wearing the brace, the brace reduced their tremors, and they felt comfortable wearing the brace at home. Through standardized testing and findings from the usability survey, Move-D showed an improvement of functional abilities in a pediatric population with tremors.

Pipeline steel is a special type of steel used for transporting, for example, oil and natural gas. This study focuses on X80-grade pipeline steel modified with the addition of Nb and Nb-V at different cooling rates (air cooling or quenching) after hot rolling and subjecting it to quenching and tempering heat treatment. Based on multiscale characterization techniques, the effects of microalloying and the cooling rate after hot rolling on the microstructure, precipitation behavior, and strengthening mechanisms were studied. The results showed that the strengths of quenched steels were higher than those of air-cooled steels, and the increase in strength was more pronounced with the addition of Nb-V than with the addition of Nb alone in the steels. Under the same cooling condition, the strengths of Nb-V-added steels were larger than those of Nb-added steels. Additionally, the Nb-V addition promotes the formation of lath structures. The yield stress of the steels, calculated by using measured microstructural parameters following the linear addition of strengthening, is in good agreement with the measured values.

This work presents a contribution to the study of a new Ni-rich spinel cathode material, LiNiMnO4, for Li-ion batteries operating in the 5-V region. The LiNiMnO4 compound was synthesized by a sol-gel method assisted by ethylene diamine tetra-acetic acid (EDTA) as a chelator. Structural analyses carried out by Rietveld refinements and Raman spectroscopy, selected area electron diffraction (SAED) and X-ray photoelectron (XPS) spectroscopy reveal that the product is a composite (LNM@NMO), including non-stoichiometric LiNiMnO4-δ spinel and a secondary Ni6MnO8 cubic phase. Cyclic voltammetry and galvanostatic charge-discharge profiles show similar features to those of LiNi0.5Mn1.5O4 bare. A comparison of the electrochemical performances of 4-V spinel LiMn2O4 and 5-V spinel LiNi0.5Mn1.5O4 with those of LNM@NMO composite demonstrates the long-term cycling stability of this new Ni-rich spinel cathode. Due to the presence of the secondary phase, the LNM@NMO electrode exhibits an initial specific capacity as low as 57 mAh g−1 but shows an excellent electrochemical stability at 1C rate for 1000 cycles with a capacity decay of 2.7 × 10−3 mAh g−1 per cycle.

A crude oil spill in 2014 resulted in extensive soil contamination of the hyper arid Evrona Nature Reserve in Israel’s Negev Desert. The contaminated soils became highly hydrophobic, threatening the existence of plants in the habitat. We hypothesized that bioaugmenting the soil with indigenous biosurfactant-producing, hydrocarbon-degrading bacteria (HDB) would accelerate the reduction in the soil’s hydrophobicity. We aimed to isolate and characterize biosurfactant-producing HDBs from the desert-contaminated soil and test if they can be used for augmenting the soil. Twelve hydrocarbon-degrading strains were isolated, identified as Pseudomonas, and classified as biosurfactants “producing” and “nonproducing”. Inoculating 109 CFU/g of “producing” strains into the polluted soil resulted in a 99.2% reduction in soil hydrophobicity within seven days. At the same time, nonproducing strains reduced hydrophobicity by only 17%, while no change was observed in the untreated control. The microbial community in the inoculated soil was dominated by the introduced strains over 28 days, pointing to their persistence. Rhamnolipid biosynthesis gene rhlAB remained persistent in soil inoculated with biosurfactants, indicating in situ production. We propose that the success of the treatment is due to the use of inoculum enriched from the polluted soil.

Gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear, and uncertain MIMO system. In order to solve the control problem of pressure, flow, and temperature of the system, an intelligent approach is necessary and more appropriate. The current system control mainly depends on the experience of the staff, which exists problems such as high labor intensity, low work efficiency and low control accuracy. Lack of accurate models make parameters tune difficultly, and ordinary control methods are difficult to control complex gas turbine cooling system. In this paper, the system transfer function model is built based on the field data obtained under different working conditions and system identification method. The diagonal matrix decoupling method is used to weaken the correlation between variables and achieve independent control among variables. When optimizing the parameters of the controller, Sine Cosine Egret Swarm Optimization Algorithm is proposed. Egret Swarm Optimization Algorithm is composed of Sit-And-Wait strategy, random walk, and encirclement strategy. The sit-and-wait strategy is prone to premature convergence, which makes the optimized parameters unsuitable for gas turbine cooling system. Sine Cosine Algorithm is introduced to randomly use the sine-cosine function for the pseudo-gradient of the weights of the observation equation, thus expanding the search range of the population. Friedman tests prove that the deviation of SE-ESOA is within the allowable range. The results show that the result of Sine Cosine Egret Swarm Optimization Algorithm is more stable and accurate, and it is more suitable for gas turbine cooling system, which solve the pressure, flow, and temperature control problems of complex systems.

The gas turbine cooling system is a complex MIMO system with a strong coupling, nonlinear, time-varying and large disturbance amplitude. In order to automatically control the target flow, target temperature and pipeline pressure, in this paper, the decoupler and regulator of a gas turbine cooling system are designed. Firstly, the working principle of a gas turbine cooling system and the coupling between the controlled variables of the system are analyzed. The decoupler of the system is designed by using the diagonal matrix decoupling method. The transfer function models of the coupling system are built through system identification, and the decoupling matrix of the system is calculated according to the diagonal matrix decoupling method and transfer function models. Then, the engine cooling control system simulation model is constructed and an improved jumping spider optimization algorithm is proposed. The parameters of the controller are optimized by the improved jumping spider optimization algorithm. Finally, the control system simulation is done and compared with the jumping spider optimization algorithm and the particle swarm optimization algorithm. The simulation results show that the improved jumping spider optimization algorithm is more suitable for the multivariable strong coupling nonlinear engine cooling system. For the flow and pressure control, the transient time and overshoot are reduced, and the steady-state error is less than 1%. For the temperature control, the result of the improved jumping spider optimization algorithm is more smooth, without overshoot, and almost does not exceed the set inlet water temperature. The overshoot, steady-state errors and transient time of the system have been improved, which proves the feasibility and significance of the improved jumping spider optimization algorithm by comparing the control performance and optimization time.

Modeling pollutant transport in heterogeneous media is an important task of hydrology. Pollutant transport in a non-homogeneous environment typically exhibits non-local transport dynamics, whose efficient characterization requires a parsimonious model with the non-local feature. This study encapsulates the non-local transport characteristic of pollutants into the peridynamic differential operator (PDDO) and develops a PDDO-based model for quantifying the observed pollutant non-local transport behavior. The simulation results show that the proposed model can describe pollutant non-local transport behavior in various heterogeneous media. The non-local nature of pollutant transport can be adjusted by pre-defined weight function w(|ξ|) and horizon Hx. Applications show that the PDDO-based model can better capture pollutant non-local transport behavior than the classical advection–diffusion equation (ADE) model, especially for quantifying the tail of the experimental data late. Analyses further reveal that the PDDO-based model can characterize both normal (Fickian) and anomalous (Lévy) diffusion regimes.

Highway infrastructure construction is regarded as one of the effective policy tools used to promote the flow of production factors and upgrade the industrial structure in China, and it may also be an important precondition to improving Cultivated Land Use Efficiency (CLUE). This paper uses a slack-based model (SBM) based on provincial-level panel data from China from 2004 to 2017 to measure CLUE. Then a dynamic spatial Durbin model and a panel threshold regression model were established to analyze the spatial spillover effect and threshold effect of highway infrastructure construction on CLUE. The results showed that the CLUE of China has a fluctuating but overall rising trend. The dynamic spatial Durbin model demonstrated that the Chinese government’s policy of supporting highway infrastructure construction has played an important role in promoting CLUE by spatial spillover effects, and the driving effect of expressways and first–second highways on CLUE is particularly significant in this regard. More interestingly, the results of the panel threshold regression indicated that there is a single threshold effect in the influence of highway infrastructure construction on CLUE. This paper suggested that the spatial correlation between regions should be considered in the construction of regional highway infrastructure and land use planning to improve CLUE. Moreover, the planning of highway infrastructure construction should be balanced according to the actual demand of economic and agricultural development, so as to promote the full flow and reasonable allocation of cultivated land use factors among regions.

Elemental doping for substituting lithium or oxygen sites has become a simple and effective technique to improve the electrochemical performance of layered cathode materials. Compared with single-element doping, this work presents an unprecedented contribution to the study of the effect of Na+/F− co-doping on the structure and electrochemical performance of LiNi1/3Mn1/3Co1/3O2. The co-doped Li1-zNazNi1/3Mn1/3Co1/3O2-zFz (z = 0.025) and pristine LiNi1/3Co1/3Mn1/3O2 materials were synthesized via the sol–gel method using EDTA as a chelating agent. Structural analyses, carried out by X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy, revealed that the Na+ and F− dopants were successfully incorporated into the Li and O sites, respectively. The co-doping resulted in larger Li-slab spacing, a lower degree of cation mixing, and the stabilization of the surface structure, which substantially enhanced the cycling stability and rate capability of the cathode material. The Na/F co-doped LiNi1/3Mn1/3Co1/3O2 electrode delivered an initial specific capacity of 142 mAh g−1 at a 1C rate (178 mAh g−1 at 0.1C), and it maintained 50% of its initial capacity after 1000 charge–discharge cycles at a 1C rate.

Background. Interferon is a marker of host antiviral immunity, which is disordered in COVID-19 patients. ERV can affect the secretion of interferon through the cGAS-STING pathway. In this study, we explored whether IFN-I and HERV-K (HML-2) were activated in COVID-19 patients and whether there was an interaction between them. Methods. We collected blood samples from COVID-19 patients and healthy controls. We first detected the expression of HERV-K (HML-2) gag, env, and pol genes and IFN-I-related genes between patients and healthy people by qPCR, synchronously detected VERO cells infected with SARS-CoV-2. Then, the chromosome distributions of highly expressed HERV-K (HML-2) gag, env, and pol genes were mapped by the next-generation sequencing results, and GO analysis was performed on the related genes. Results. We found that the HERV-K (HML-2) gag, env, and pol genes were highly expressed in COVID-19 patients and VERO cells infected with SARS-CoV-2. The interferon-related genes IFNB1, ISG15, and IFIT1 were also activated in COVID-19 patients, and GO analysis showed that HERV-K (HML-2) can regulate the secretion of interferon. Conclusions. The high expression of HERV-K (HML-2) might activate the increase of interferon in COVID-19 patients, proving that HERV-K does not only play a negative role in the human body.

Current point-of-care (POC) screening of Coronavirus disease 2019 (COVID-19) requires further improvements to achieve highly sensitive, rapid, and inexpensive detection. Here we describe an immunoresistive sensor on a polyethylene terephthalate (PET) film for simple, inexpensive, and highly sensitive COVID-19 screening. The sensor is composed of single-walled carbon nanotubes (SWCNTs) functionalized with monoclonal antibodies that bind to the spike protein of SARS-CoV-2. Silver electrodes are silkscreen-printed on SWCNTs to reduce contact resistance. We determine the SARS-CoV-2 status via the resistance ratio of control- and SARS-CoV-2 sensor electrodes. A combined measurement of two adjacent sensors enhances the sensitivity and specificity of the detection protocol. The lower limit of detection (LLD) of the SWCNT assay is 350 genome equivalents/mL. The developed SWCNT sensor shows 100% sensitivity and 90% specificity in clinical sample testing. Further, our device adds benefits of a small form factor, simple operation, low power requirement, and low assay cost. This highly sensitive film sensor will facilitate rapid COVID-19 screening and expedite the development of POC screening platforms.

This work aimed at synthesizing MoO3 and MoO2 by a facile and cost-effective method using extract of orange peel as a biological chelating and reducing agent for ammonium molybdate. Calcination of the precursor in air at 450 °C yielded the stochiometric MoO3 phase, while calcination in vacuum produced the reduced form MoO2 as evidenced by X-ray powder diffraction, Raman scattering spectroscopy, and X-ray photoelectron spectroscopy results. Scanning and transmission electron microscopy images showed different morphologies and sizes of MoOx particles. MoO3 formed platelet particles that were larger than those observed for MoO2. MoO3 showed stable thermal behavior until approximately 800 °C, whereas MoO2 showed weight gain at approximately 400 °C due to the fact of re-oxidation and oxygen uptake and, hence, conversion to stoichiometric MoO3. Electrochemically, traditional performance was observed for MoO3, which exhibited a high initial capacity with steady and continuous capacity fading upon cycling. On the contrary, MoO2 showed completely different electrochemical behavior with less initial capacity but an outstanding increase in capacity upon cycling, which reached 1600 mAh g−1 after 800 cycles. This outstanding electrochemical performance of MoO2 may be attributed to its higher surface area and better electrical conductivity as observed in surface area and impedance investigations.

This paper presents the analytical solution of the radial consolidation of a prefabricated vertical drain (PVD) foundation under the unsaturated condition. In the proposed modeling, air and water phases in the foundation are thought to dissipate horizontally toward to the drain, and the smear effect, drain resistance and external time-dependent loading are fully considered. The analytical mathematical tools, namely the general integration method, Fourier series expansion method, decoupling method and the constant variation method, are utilized to solve the partial differential equations. Moreover, the current solutions are verified with existing solutions in the literature. Finally, a case study considering the ramp loading and exponential loading is conducted to investigate the consolidation patterns under various loading parameters. The results show that smear effect and drain resistance can significantly hinder the dissipation process of excess pore pressures, and different external loading types will lead to various dissipation characteristics (i.e., peak values).

Currently available test methods are not well-suited for the identification of chemicals that disturb hormonal processes involved in female reproductive development and function. This renders women’s reproductive health at increasing risk globally, which, coupled with increasing incidence rates of reproductive disorders, is of great concern. A woman’s reproductive health is largely established during embryonic and fetal development and subsequently matures during puberty. The endocrine system influences development, maturation, and function of the female reproductive system, thereby making appropriate hormone levels imperative for correct functioning of reproductive processes. It is concerning that the effects of human-made chemicals on the endocrine system and female reproductive health are poorly addressed in regulatory chemical safety assessment, partly because adequate test methods are lacking. Our EU-funded project FREIA aims to address this need by increasing understanding of how endocrine disrupting chemicals (EDCs) can impact female reproductive health. We will use this information to provide better test methods that enable fit-for-purpose chemical regulation and then share our knowledge, promote a sustainable society, and improve the reproductive health of women globally.

Surface coating is the simplest surface modification. However, bioactive molecules can not spread well on the commonly used polylactone-type skeletons; thus, the surface coatings of biomolecules are typically unstable due to the weak interaction between the polymer and the bioactive molecules. In this study, a special type of poly(lactide-co-glycolide) (PLGA)-based scaffold with a loosened skeleton was fabricated by phase separation, which allowed gelatin molecules to more readily diffuse throughout the structure. In this application, gelatin modified both the internal substrate and external surface. After cross-linking with glutaraldehyde, the surface layer gelatin was tightly bound to the diffused gelatin, thereby preventing the surface layer gelatin coating from falling off within 14 days. After gelatin modification, PLGA scaffold demonstrated enhanced hydrophilicity and improved mechanical properties (i.e., increased compression strength and elastic modulus) in dry and wet states. Furthermore, a sustained release profile of recombinant human bone morphogenetic protein-2 (rhBMP-2) was achieved in the coated scaffold. The coated scaffold also supported the in vitro attachment, proliferation, and osteogenesis of rabbit bone mesenchymal stem cells (BMSCs), indicating the bioactivity of rhBMP-2. These results collectively demonstrate that the cross-linked-gelatin-coated porous PLGA scaffold incorporating bioactive molecules is a promising candidate for bone tissue regeneration.

In this paper, we propose a novel systematic procedure to minimize the discrepancy between the numerically predicted and the experimentally measured fiber orientation results on an injection-molded part. Fiber orientation model parameters are optimized simultaneously using Latin hypercube sampling and kriging-based adaptive surrogate modeling techniques. Via an adequate discrepancy measure, the optimized solution possesses correct skin⁻shell⁻core structure and global orientation evolution throughout the considered center-gated disk. Some non-trivial interaction between these parameters and flow-fiber coupling effects as well as their quantitative importance are illustrated. The parametric fine-tuning of orientation models mostly leads to a better agreement in the skin and shell regions, while the coupling effect via a fiber-dependent viscosity improves prediction in the core.

Luminescent and UV-shielding bio-polymers have seldom been reported. Zinc oxide quantum dots (ZnOQD) (~3.2 nm) was synthesized by a short-term sol-gel method. ZnOQD possessed a crystal lattice spacing of 0.28 nm and a hexagonal wurtzite structure. Luminescent and UV-shielding ZnOQD/carboxymethylcellulose sodium (CMC) nanocomposite polymer films were successfully fabricated by incorporating ZnOQD into a CMC matrix through a solution casting method. Thermal analysis demonstrated that the ZnOQD reduce the thermal decomposition rate of CMC, and a large number of ZnOQD can promote the catalytic degradation of ZnOQD/CMC nanocomposites. Furthermore, ZnOQD/CMC hybrid polymer films exhibited photoluminescence with maximum emission wavelength at 525 nm. More significantly, ZnOQD/CMC showed prominent UV-absorbing capability. Such ZnOQD/CMC nanocomposite polymer films are promising in UV-shielding and optical applications.

This study aimed to explore the response of archaeal communities and antibiotic-resistance genes (ARGs) to ciprofloxacin (CIP, 0.05–40 mg/L) and copper (Cu, 3 mg/L) combined pollution during stress- and post-effect periods in an activated sludge system. With the increase in the CIP concentration, the diversity of archaea decreased, but the richness increased under the stress of 10 mg/L CIP. Under stress and post effects, the change in unknown archaeal community structure was more significant than that of the known archaea. The relative abundance of unknown archaea was significantly reduced with the increase in CIP concentration. Meanwhile, there were certain archaea that belonged to abundant and rare taxa with different resistance and recovery characteristics. Among them, Methanosaeta (49.15–83.66%), Methanoculleus (0.11–0.45%), and Nitrososphaera (0.03–0.36%) were the typical resistant archaea to combined pollution. And the resistance of the abundant taxa to combined pollution was significantly higher than that of the rare taxa. Symbiotic and competitive relationships were observed between the known and the unknown archaea. The interactions of abundant known taxa were mainly symbiotic relationships. While the rare unknown taxa were mainly competitive relationships in the post-effect period. Rare archaea showed an important ecological niche under the stress-effect. Some archaea displayed positive correlation with ARGs and played important roles as potential hosts of ARGs during stress- and post-periods. Methanospirillum, Methanosphaerula, Nitrososphaera and some rare unknown archaea also significantly co-occurred with a large number of ARGs. Overall, this study points out the importance of interactions among known and unknown archaeal communities and ARGs in a wastewater treatment system under the stress of antibiotics and heavy metal combined pollution.

(1) Background: Laizhou Bay is an important aquaculture area in the north of China. Oplegnathus punctatus is one of the species with high economic benefits. In recent years, the water environment of Laizhou Bay has reached a mild eutrophication level, while microorganisms are an important group between the environment and species. In this study, we evaluated alterations in environmental elements, microbial populations, and antibiotic resistance genes (ARGs) along with their interconnections during Oplegnathus punctatus net culture. (2) Methods: A total of 142 samples from various water layers were gathered for metagenome assembly analysis. Mariculture increases the abundance of microorganisms in this culture area and makes the microbial community structure more complex. The change had more significant effects on sediment than on seawater. (3) Results: Certain populations of cyanobacteria and Candidatus Micrarchaecta in seawater, and Actinobacteria and Thaumarchaeota in sediments showed high abundance in the mariculture area. Antibiotic resistance genes in sediments were more sensitive to various environmental factors, especially oxygen solubility and salinity. (4) Conclusions: These findings highlight the complex and dynamic nature of microorganism–environment–ARG interactions, characterized by regional specificity and providing insights for a more rational use of marine resources.

A genomic study was conducted to uncover the selection signatures in sheep that show extremely significant differences in growth traits under the same breed, age in months, nutrition level, and management practices. Hu sheep from Gansu Province and Gangba sheep from the Tibet Autonomous Region in China were selected. We collected whole-genome data from 40 sheep individuals (24 Hu sheep and 16 Gangba sheep), through whole-genome sequencing. Selection signals were analyzed using parameters such as FST, π ratio, and Tajima’s D. We have identified several candidate genes that have undergone strong selection, particularly those associated with growth traits. Specifically, five growth-related genes were identified in both the Hu sheep group (HDAC1, MYH7B, LCK, ACVR1, GNAI2) and the Gangba sheep group (RBBP8, ACSL3, FBXW11, PLAT, CRB1). Additionally, in a genomic region strongly selected in both the Hu and Gangba sheep groups (Chr 22: 51,425,001-51,500,000), the growth-associated gene CYP2E1 was identified, further highlighting the genetic factors influencing growth characteristics in these breeds. This study analyzes the genetic basis for significant differences in sheep phenotypes, identifies candidate genes related to sheep growth traits, lays the foundation for molecular genetic breeding in sheep, and accelerates the genetic improvement in livestock.

Voice behavior has been widely recognized as having various benefits for organizations, while researchers seldom notice its consequences in construction projects. Against this context, this study empirically explores the impact path of team voice on project performance in construction projects from a multi-team system (MTS) perspective. Considering the essence of voice behavior is to provide information, this study introduces the input–process–outcome (IPO) framework to verify how team voice can affect project performance by influencing project learning and project reflexivity. The online survey method, monetary incentive method, and chain-referral sampling method are adopted to distribute the designed questionnaire. A total of 184 data points from completed construction projects in China supported the research conclusion by adopting the partial least squares structural equation modeling (PLS-SEM) technique. Project learning and project reflexivity positively mediate the relationship between team voice and project performance in construction projects. Specifically, team voice can first positively influence project learning and project reflexivity, which both have a positive relationship with project performance. For theory, this study opens the black box between team voice and project performance in the context of construction projects by unveiling the mediating path of project learning and project reflexivity, which also enriches the literature on voice behavior and expands its application. Furthermore, this study provides one new perspective for MTS knowledge by adopting the IPO framework to explore the underlying mechanism between the focal team and the overall team performance of the MTS. For practice, this study has a directive function for construction project management and MTS management, while providing reminders for policymakers, researchers, and practitioners about the significance of project learning and project reflexivity on project performance.

Kittens are prone to intestinal health problems as their intestines are not completely developed. Seaweed is rich in plant polysaccharides and bioactive substances that are highly beneficial to gut health. However, the effects of seaweed on cat gut health have not been assessed. This study compared the effects of dietary supplementation with enzymolysis seaweed powder and Saccharomyces boulardii on the intestinal health of kittens. In total, 30 Ragdoll kittens (age: 6 months; weight: 1.50 ± 0.29 kg) were assigned to three treatment groups for a 4-week feeding trial. The dietary treatment given was as follows: (1) basal diet (CON); (2) CON + enzymolysis seaweed powder (20 g/kg of feed) mixed evenly with the diet (SE); and (3) CON + Saccharomyces boulardii (2 × 1010 CFU/kg of feed) mixed evenly with the diet (SB). Compared with the CON and SB groups, dietary supplementation with the enzymolysis seaweed powder improved the immune and antioxidant capacity and also reduced the intestinal permeability and inflammation levels of kittens. The relative abundance of Bacteroidetes, Lachnospiraceae, Prevotellaceae, and Faecalibacterium in the SE group was higher than those in the CON and SB groups (p ≤ 0.05), while the relative abundance of Desulfobacterota, Sutterellaceae, and Erysipelatoclostridium in the SB group was lower than that in the SE group (p ≤ 0.05). Moreover, enzymolysis seaweed powder did not alter the level of intestinal SCFAs in kittens. Conclusively, supplementing kitten diet with enzymolysis seaweed powder can promote intestinal health by enhancing the gut barrier function and optimizing the microbiota composition. Our findings provide new perspectives on the application of enzymolysis seaweed powder.

The interaction between gut microbiota and the health of the host has gained increasing attention. Chitosan is a natural alkaline polysaccharide with a wide range of beneficial effects. However, rare studies have been observed on the effects of dietary chitosan supplementation on intestinal health in cats. A total of 30 cats with mild diarrhea were divided into three groups, receiving a basic diet with 0 (CON), 500 (L-CS) or 2000 (H-CS) mg/kg chitosan. Samples of blood and feces were collected and analyzed for serology and gut microbiota composition. The results demonstrated that chitosan alleviated symptoms of diarrhea, with enhanced antioxidant capability and decreased inflammatory biomarker levels in serum. Chitosan reshaped the composition of gut microbiota in cats that the beneficial bacteria Allobaculum was significantly increased in the H-CS group. Acetate and butyrate contents in feces were significantly higher in the H-CS group in comparison to the CON group (p < 0.05). In conclusion, the addition of dietary chitosan in cats enhanced intestinal health by modulating their intestinal microbes and improved microbiota-derived SCFA production. Our results provided insights into the role of chitosan in the gut microbiota of felines.

Food allergies can cause intestinal damage that can exacerbate allergic symptoms, and gut microbiota have been shown to influence allergic development. This study was intended to investigate the effects of Avenanthramide (AVA) on colonic damage induced by food allergy and its mechanism. In Exp. 1, AVA administrations alleviated colonic inflammation in mice challenged with ovalbumin, as shown by decreased concentrations of TNF-α, IL-25 and IL-33. Additionally, the AVA supplementations improved intestinal barrier damage by elevating occludin, ZO-1 and claudin-1 levels. Moreover, AVA inhibited NF-κB phosphorylation and enhanced heat shock protein 70 (Hsp70) expression in the colon. In Exp. 2, apoptozole as a Hsp70 inhibitor was used to explore the Hsp70-NF-κB signaling contribution to AVA function. The AVA additions increased the productions of acetate and butyrate, but decreased propionate. Notably, AVA reduced the colonic abundance of propionate-producing microbes such as Muribaculaceae, but elevated butyrate-producing microbes including Roseburia, Blautia, and Lachnospiraceae_NK4A136_group. Microbial alteration could be responsible for the increased butyrate, and thus the up-regulated Hsp70. However, apoptozole treatment eliminated the effects of AVA. Our study revealed that AVA improved colonic injury and inflammation induced by food allergies, and this mechanism may be mediated by the increased microbial-derived butyrate and involved in the Hsp70-NF-κB signaling.

A food allergy is caused by an abnormal immune reaction and can induce serious intestinal inflammation and tissue damage. Currently, the avoidance of food allergens is still the most effective way to prevent or reduce allergic symptoms, so the development of new strategies to treat allergies is important. Avenanthramide (AVA) is a bioactive polyphenol derived from oats with a wide range of biological activities; however, it is still not clear whether or how AVA alleviates intestinal damage under allergic situations. The aim of this study was to explore the effect of AVA on the small intestinal damage in an ovalbumin (OVA)-induced food allergy model and its mechanism. In experiment 1, 10 mg/kg bw and 20 mg/kg bw doses of AVA both decreased the serum levels of OVA-specific IgE, histamine, and prostaglandin D induced by OVA. The AVA administration relieved inflammation indicated by the lower serum concentrations of pro-inflammatory cytokines including interleukin-1β, IL-6, and tumor necrosis factor-α. The levels of tight junction proteins including Claudin-1, ZO-1, and Occludin in the jejunum were elevated after AVA administration, accompanied by the improved intestinal morphology. Furthermore, AVA elevated the protein expression of heat shock protein 70 (Hsp70) and inhibited the phosphorylation of nuclear factor kappa-B (NF-κB), thus the apoptozole, which a Hsp70 inhibitor, was applied in experiment 2 to assess the contribution of Hsp70-NF-κB signaling to the effects of AVA. In the experiment 2, the inhibition of Hsp70 signaling treatment abolished the beneficial effects of AVA on the small intestinal damage and other allergic symptoms in mice challenged with OVA. Taken together, our results indicated that AVA exerted an intestinal protection role in the OVA-induced allergy, the mechanism of which was partly mediated by the Hsp70-NF-κB signaling.

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF.

An accurate ego-motion estimation solution is vital for autonomous vehicles. LiDAR is widely adopted in self-driving systems to obtain depth information directly and eliminate the influence of changing illumination in the environment. In LiDAR odometry, the lack of descriptions of feature points as well as the failure of the assumption of uniform motion may cause mismatches or dilution of precision in navigation. In this study, a method to perform LiDAR odometry utilizing a bird’s eye view of LiDAR data combined with a deep learning-based feature point is proposed. Orthographic projection is applied to generate a bird’s eye view image of a 3D point cloud. Thereafter, an R2D2 neural network is employed to extract keypoints and compute their descriptors. Based on those keypoints and descriptors, a two-step matching and pose estimation is designed to keep these feature points tracked over a long distance with a lower mismatch ratio compared to the conventional strategy. In the experiment, the evaluation of the proposed algorithm on the KITTI training dataset demonstrates that the proposed LiDAR odometry can provide more accurate trajectories compared with the handcrafted feature-based SLAM (Simultaneous Localization and Mapping) algorithm. In detail, a comparison of the handcrafted descriptors is demonstrated. The difference between the RANSAC (Random Sample Consensus) algorithm and the two-step pose estimation is also demonstrated experimentally. In addition, the data collected by Velodyne VLP-16 is also evaluated by the proposed solution. The low-drift positioning RMSE (Root Mean Square Error) of 4.70 m from approximately 5 km mileage shown in the result indicates that the proposed algorithm has generalization performance on low-resolution LiDAR.

Although Unmanned Aerial Vehicles (UAVs)-aided wireless sensor networks (WSNs) have gained many applications, it is not for long that research works have been produced to define effective algorithms and protocols. In this article, we address the UAV-enabled WSN (U-WSN), explore the performance and the capability of the UAV, define the UAV functionalities as a communication node, and describe the architectures and the relevant typical technologies that emerge from this new paradigm. Furthermore, this article also identifies the main factors which influence the U-WSN design and analyzes the open issues and challenges in U-WSN. These insights may serve as motivations and guidelines for future designs of UAV-enabled WSNs.

The flexible job shop scheduling problem has always been the focus of research in the manufacturing field. However, most of the previous studies focused more on efficiency and ignored energy consumption. Energy, especially non-renewable energy, is an essential factor affecting the sustainable development of a country. To this end, this paper designs a flexible job shop scheduling problem model with energy consideration more in line with the production field. Except for the processing stage, the energy consumption of the transport, set up, unload, and idle stage are also included in our model. The weight property of jobs is also considered in our model. The heavier the job, the more energy it consumes during the transport, set up, and unload stage. Meanwhile, this paper invents an adaptive population non-dominated sorting genetic algorithm III (APNSGA-III) that combines the dual control strategy with the non-dominated sorting genetic algorithm III (NSGA-III) to solve our flexible job shop scheduling problem model. Four flexible job shop scheduling problem instances are formulated to examine the performance of our algorithm. The results achieved by the APNSGA-III method are compared with five classic multi-objective optimization algorithms. The results show that our proposed algorithm is efficient and powerful when dealing with the multi-objective flexible job shop scheduling problem model that includes energy consumption.

Recently, ice with stacking disorder structure, consisting of random sequences of cubic ice (Ic) and hexagonal ice (Ih) layers, was reported to be more stable than pure Ih/Ic. Due to a much lower free energy barrier of heterogeneous nucleation, in practice, the freezing process of water is controlled by heterogeneous nucleation triggered by an external medium. Therefore, we carry out molecular dynamic simulations to explore how ice polymorphism depends on the lattice structure of the crystalline substrates on which the ice is grown, focusing on the primary source of atmospheric aerosols, carbon materials. It turns out that, during the nucleation stage, the polymorph of ice nuclei is strongly affected by graphene substrates. For ice nucleation on graphene, we find Ih is the dominant polymorph. This can be attributed to structural similarities between graphene and basal face of Ih. Our results also suggest that the substrate only affects the polymorph of ice close to the graphene surface, with the preference for Ih diminishing as the ice layer grows.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

Vision-based sensors such as LiDAR (Light Detection and Ranging) are adopted in the SLAM (Simultaneous Localization and Mapping) system. In the 16-beam LiDAR aided SLAM system, due to the difficulty of object detection by sparse laser data, neither the grid-based nor feature point-based solution can avoid the interference of moving objects. In an urban environment, the pole-like objects are common, invariant and have distinguishing characteristics. Therefore, it is suitable to bring more robust and reliable positioning results as auxiliary information in the process of vehicle positioning and navigation. In this work, we proposed a scheme of a SLAM system using a GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and LiDAR sensor using the position of pole-like objects as the features for SLAM. The scheme combines a traditional preprocessing method and a small scale artificial neural network to extract the pole-like objects in environment. Firstly, the threshold-based method is used to extract the pole-like object candidates from the point cloud, and then, the neural network is applied for training and inference to obtain pole-like objects. The result shows that the accuracy and recall rate are sufficient to provide stable observation for the following SLAM process. After extracting the poles from the LiDAR point cloud, their coordinates are added to the feature map, and the nonlinear optimization of the front end is carried out by utilizing the distance constraints corresponding to the pole coordinates; then, the heading angle and horizontal plane translation are estimated. The ground feature points are used to enhance the elevation, pitch and roll angle accuracy. The performance of the proposed navigation system is evaluated through field experiments by checking the position drift and attitude errors during multiple two-min mimic GNSS outages without additional IMU motion constrain such as NHC (Nonholonomic Constrain). The experimental results show that the performance of the proposed scheme is superior to that of the conventional feature point grid-based SLAM with the same back end, especially in congested crossroads where slow-moving vehicles are surrounded and pole-like objects are rich in the environment. The mean plane position error during two-min GNSS outages was reduced by 38.5%, and the root mean square error was reduced by 35.3%. Therefore, the proposed pole-like feature-based GNSS/IMU/LiDAR SLAM system can fuse condensed information from those sensors effectively to mitigate positioning and orientation errors, even in a short-time GNSS denied environment.

In this paper, we proposed a multi-sensor integrated navigation system composed of GNSS (global navigation satellite system), IMU (inertial measurement unit), odometer (ODO), and LiDAR (light detection and ranging)-SLAM (simultaneous localization and mapping). The dead reckoning results were obtained using IMU/ODO in the front-end. The graph optimization was used to fuse the GNSS position, IMU/ODO pre-integration results, and the relative position and relative attitude from LiDAR-SLAM to obtain the final navigation results in the back-end. The odometer information is introduced in the pre-integration algorithm to mitigate the large drift rate of the IMU. The sliding window method was also adopted to avoid the increasing parameter numbers of the graph optimization. Land vehicle tests were conducted in both open-sky areas and tunnel cases. The tests showed that the proposed navigation system can effectually improve accuracy and robustness of navigation. During the navigation drift evaluation of the mimic two-minute GNSS outages, compared to the conventional GNSS/INS (inertial navigation system)/ODO integration, the root mean square (RMS) of the maximum position drift errors during outages in the proposed navigation system were reduced by 62.8%, 72.3%, and 52.1%, along the north, east, and height, respectively. Moreover, the yaw error was reduced by 62.1%. Furthermore, compared to the GNSS/IMU/LiDAR-SLAM integration navigation system, the assistance of the odometer and non-holonomic constraint reduced vertical error by 72.3%. The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term. Therefore, the proposed GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM.

In this work, we study data collection in multiple unmanned aerial vehicle (UAV)-aided mobile wireless sensor networks (WSNs). The network topology is changing due to the mobility of the UAVs and the sensor nodes, so the design of efficient data collection protocols is a major concern. We address such high dynamic network and propose two mechanisms: prioritized-based contact-duration frame selection mechanism (PCdFS), and prioritized-based multiple contact-duration frame selection mechanisms (PMCdFS) to build collision-free scheduling and balance the nodes between the multi-UAV respectively. Based on the two mechanisms, we proposed a Balance algorithm to conduct the collision-free communication between the mobile nodes and the multi-UAVs. Two key design ideas for a Balance algorithm are: (a) no need of higher priority for those nodes that have lower transmission rate between them and the UAV and (b) improve the communication opportunity for those nodes that have shorter contact duration with the UAVs. We demonstrate the performance of proposed algorithms through extensive simulations, and real experiments. These experiments using 15 mobile nodes at a path with 10 intersections and 1 island, present that network fairness is efficiently enhanced. We also confirm the applicability of proposed algorithms in a challenging and realistic scenario through numerous experiments on a path at Tongji campus in Shanghai, China.

With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution.

The debilitating effects of lower back pain are a major health issue worldwide. A variety of factors contribute to this, and oftentimes intervertebral disk degeneration (IDD) is an underlying cause of this disorder. Inflammation contributes to IDD, and inflammatory cytokines such as tumor necrosis factor (TNF)-α and interleukin (IL)-1β, play key roles in the pathology of IDD. Therefore, the development of treatments that inhibit the expression and/or effects of TNF-α and IL-1β in IDD patients should be a promising therapeutic approach to consider. This study characterized the potential to suppress inflammatory cytokine production in degenerative intervertebral disc (NP) cells by treatment with IL-10 and TGF-β in a canine model of IDD. IDD was induced surgically in six male beagles, and degenerative NP cells were isolated and cultured for in vitro studies on cytokine production. Cultured degenerative NP cells were divided into four experimental treatment groups: untreated control, IL-10-treated, TGF-β-treated, and IL-10- plus TGF-β-treated cells. Cultured normal NP cells served as a control group. TNF-α expression was evaluated by fluorescence activated cell sorting (FACS) analysis and enzyme-linked immunosorbent assay (ELISA); moreover, ELISA and real-time PCR were also performed to evaluate the effect of IL-10 and TGF-β on NP cell cytokine expression in vitro. Our results demonstrated that IL-10 and TGF-β treatment suppressed the expression of IL-1β and TNF-α and inhibited the development of inflammatory responses. These data suggest that IL-10 and TGF-β should be evaluated as therapeutic approaches for the treatment of lower back pain mediated by IDD.

A Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/Light Detection and Ranging (LiDAR)-Simultaneous Localization and Mapping (SLAM) integrated navigation system based on graph optimization is proposed and implemented in this paper. The navigation results are obtained by the information fusion of the GNSS position, Inertial Measurement Unit (IMU) preintegration result and the relative pose from the 3D probability map matching with graph optimizing. The sliding window method was adopted to ensure that the computational load of the graph optimization does not increase with time. Land vehicle tests were conducted, and the results show that the proposed GNSS/INS/LiDAR-SLAM integrated navigation system can effectively improve the navigation positioning accuracy compared to GNSS/INS and other current GNSS/INS/LiDAR methods. During the simulation of one-minute periods of GNSS outages, compared to the GNSS/INS integrated navigation system, the root mean square (RMS) of the position errors in the North and East directions of the proposed navigation system are reduced by approximately 82.2% and 79.6%, respectively, and the position error in the vertical direction and attitude errors are equivalent. Compared to the benchmark method of GNSS/INS/LiDAR-Google Cartographer, the RMS of the position errors in the North, East and vertical directions decrease by approximately 66.2%, 63.1% and 75.1%, respectively, and the RMS of the roll, pitch and yaw errors are reduced by approximately 89.5%, 92.9% and 88.5%, respectively. Furthermore, the relative position error during the GNSS outage periods is reduced to 0.26% of the travel distance for the proposed method. Therefore, the GNSS/INS/LiDAR-SLAM integrated navigation system proposed in this paper can effectively fuse the information of GNSS, IMU and LiDAR and can significantly mitigate the navigation error, especially for cases of GNSS signal attenuation or interruption.

The gravity disturbance vector is one of the major error sources in high-precision and long-term inertial navigation applications. Specific to the inertial navigation systems (INSs) with high-order horizontal damping networks, analyses of the error propagation show that the gravity-induced errors exist almost exclusively in the horizontal channels and are mostly caused by deflections of the vertical (DOV). Low-frequency components of the DOV propagate into the latitude and longitude errors at a ratio of 1:1 and time-varying fluctuations in the DOV excite Schuler oscillation. This paper presents two gravity compensation methods using the Earth Gravitational Model 2008 (EGM2008), namely, interpolation from the off-line database and computing gravity vectors directly using the spherical harmonic model. Particular attention is given to the error contribution of the gravity update interval and computing time delay. It is recommended for the marine navigation that a gravity vector should be calculated within 1 s and updated every 100 s at most. To meet this demand, the time duration of calculating the current gravity vector using EGM2008 has been reduced to less than 1 s by optimizing the calculation procedure. A few off-line experiments were conducted using the data of a shipborne INS collected during an actual sea test. With the aid of EGM2008, most of the low-frequency components of the position errors caused by the gravity disturbance vector have been removed and the Schuler oscillation has been attenuated effectively. In the rugged terrain, the horizontal position error could be reduced at best 48.85% of its regional maximum. The experimental results match with the theoretical analysis and indicate that EGM2008 is suitable for gravity compensation of the high-precision and long-term INSs.