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In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

The decomposition mechanisms of Fe(EDTA)− and Fe(EDTMP)− complexes, widely used in various industrial applications, were investigated through a theoretical approach. Despite their structural similarities, the phosphonic acid and carboxylic acid groups in these complexes lead to vastly different decomposition behaviors. Fe(EDTA)−, stabilized by delocalized π bonds in carboxylic acid groups, exhibited higher stability than that of Fe(EDTMP)−, which has only σ bonds in phosphonic acid groups. Interaction Region Indicator (IRI) analysis revealed that the steric hindrance of Fe(EDTMP)− was stronger than that of Fe(EDTA)−. Ab initio molecular dynamics simulations revealed that Fe(EDTMP)− undergoes rapid decomposition due to the ease of breaking P-C bonds and the repulsion between phosphonic acid groups. In contrast, Fe(EDTA)− decomposes more slowly. These findings suggest the incorporation of phosphonic acid groups for easier degradation pathways when designing organic acid molecules. Understanding these mechanisms provides a basis for developing strategies for wastewater treatment in industrial settings.

2,4-dinitrophenol (2,4-DNP) is a new kind of pollutant that is highly toxic and difficult to be biodegraded. In this study, the feasibility of using exogenous growth regulator salicylic acid (SA) to improve the purification ability of Salix matsudana (S. matsudana) seedlings to 2,4-DNP stress was investigated by a hydroponic simulation experiment. The main research results are as follows: (1) After adding exogenous SA, a high concentration of SA (1000 mg·L−1) inhibited the photosynthetic process and the normal physiological process of the chlorophyll fluorescence system of S. matsudana seedlings to a certain extent. When adding a low concentration of SA (10 mg·L−1) to treat S. matsudana seedlings, all exogenous treatment groups could alleviate the stress of 2,4-DNP on the photosynthetic system and chlorophyll fluorescence system of S. matsudana seedlings, and 10 mg·L−1 SA (DNP + S1) was the best. (2) The addition of exogenous SA could alleviate the damage of 2,4-DNP to S. matsudana seedlings by enhancing the activity of its antioxidant enzymes to remove excess reactive oxygen species (ROS) in the body and reducing the level of membrane lipid peroxidation and the size of membrane damage. The treatment with 10 mg·L−1 SA had the best effect. (3) Exogenous low concentration of SA (10 mg·L−1) could alleviate the decline of biomass index of S. matsudana seedlings under 2,4-DNP stress, but a high concentration of SA (1000 mg·L−1) could not alleviate the 2,4-DNP toxicity of S. matsudana seedlings leaves. Exogenous SA could effectively alleviate the growth damage caused by 2,4-DNP stress on S. matsudana seedlings and increase the tolerance threshold range of S. matsudana seedlings to 2,4-DNP (8.81–33.78 mg·L−1). (4) Exogenous addition of SA could increase the removal percentage of 2,4-DNP in Salix matsudana seedlings. Among them, the removal percentage of Salix matsudana was the highest at 10 mg·L−1 SA, which was 1.46 times (5 days) and 1.19 times (10 days) higher than that of the DNP treatment group, respectively. Overall, when SA reached 10 mg·L−1, the photosynthetic productivity of S. matsudana was the highest, and S. matsudana had the best purification effect on 2,4-DNP in wastewater.

X80 pipeline steel has played a vital role in oil and gas transportation in recent years. However, hydrogen-related issues frequently lead to pipeline failures during service, resulting in significant losses of properties and lives. Three heat treatment processes (furnace cooling (FC), air cooling (AC), and water cooling (WC)) were carried out to investigate the effect of different microstructures on hydrogen-induced cracking (HIC) susceptibility of X80 pipeline steel. The WC sample demonstrated the highest hydrogen embrittlement index, registering at 21.9%, while the AC and FC samples exhibited progressively lower values of 15.45% and 10.98%, respectively. Under equivalent hydrogen charging durations, crack dimensions with a maximum length exceeding 30 μm in the WC sample generally exceed those in the FC sample and AC sample. The variation is attributed to the difference in microstructures of the samples, predominantly lath bainite (LB) in water-cooled samples, granular bainite (GB) in air-cooled samples, and ferrite/pearlite (F/P) in FC samples. The research results demonstrate that the sensitivity of lath bainite (LB) to HIC is significantly higher than that of pearlite, ferrite, and granular bainite (GB). The presence of a large amount of martensite/austenite (M/A) constituents within bainite results in a multitude of hydrogen trap sites. HIC cracks in bainite generally propagate along the profiles of M/A constituents, showing both intergranular and transgranular cracking modes.

Given the widespread prevalence of viruses, there is an escalating demand for antimicrobial composites. Although the composite of dialdehyde cellulose and silver nanoparticles (DAC@Ag1) exhibits excellent antibacterial properties, its weak mechanical characteristics hinder its practical applicability. To address this limitation, cellulose nanofibers (CNFs) were initially ammoniated to yield N-CNF, which was subsequently incorporated into DAC@Ag1 as an enhancer, forming DAC@Ag1/N-CNF. We systematically investigated the optimal amount of N-CNF and characterized the DAC@Ag1/N-CNF using FT-IR, XPS, and XRD analyses to evaluate its additional properties. Notably, the optimal mass ratio of N-CNF to DAC@Ag1 was found to be 5:5, resulting in a substantial enhancement in mechanical properties, with a 139.8% increase in tensile elongation and a 33.1% increase in strength, reaching 10% and 125.24 MPa, respectively, compared to DAC@Ag1 alone. Furthermore, the inhibition zones against Escherichia coli and Staphylococcus aureus were significantly expanded to 7.9 mm and 15.9 mm, respectively, surpassing those of DAC@Ag1 alone by 154.8% and 467.9%, indicating remarkable improvements in antimicrobial efficacy. Mechanism analysis highlighted synergistic effects from chemical covalent bonding and hydrogen bonding in the DAC@Ag1/N-CNF, enhancing the mechanical and antimicrobial properties significantly. The addition of N-CNF markedly augmented the properties of the composite film, thereby facilitating its broader application in the antimicrobial field.

Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model.

A portion of gastric cancer patients with negative lymph node metastasis at an early stage eventually die from tumor recurrence or advanced metastasis. Occult lymph node metastasis (OLNM] is a potential risk factor for the recurrence and metastasis in these patients, and it is highly important for clinical prognosis. Positron emission tomography (PET)/computed tomography (CT) is used to assess lymph node metastasis in gastric cancer due to its advantages in anatomical and functional imaging and non-invasive nature. Among the major metabolic parameters of PET, the maximum standardized uptake value (SUVmax) is commonly used for examining lymph node status. However, SUVmax is susceptible to interference by a variety of factors. In recent years, the exploration of new PET metabolic parameters, new PET imaging agents and radiomics, has become an active research topic. This paper aims to explore the feasibility and predict the effectiveness of using PET/CT to detect OLNM. The current landscape and future trends of primary metabolic parameters and new imaging agents of PET are reviewed. For gastric cancer patients, the possibility to detect OLNM non-invasively will help guide surgeons to choose the appropriate lymph node dissection area, thereby reducing unnecessary dissections and providing more reasonable, personalized and comprehensive treatments.

The discrete Fourier transform (DFT) is the most commonly used signal processing method in modern digital sensor design for signal study and analysis. It is often implemented in hardware, such as a field programmable gate array (FPGA), using the fast Fourier transform (FFT) algorithm. The frequency resolution (i.e., frequency bin size) is determined by the number of time samples used in the DFT, when the digital sensor’s bandwidth is fixed. One can vary the sensitivity of a radio frequency receiver by changing the number of time samples used in the DFT. As the number of samples increases, the frequency bin width decreases, and the digital receiver sensitivity increases. In some applications, it is useful to compute an ensemble of FFT lengths; e.g., 2P−j for j=0, 1, 2, …, J, where j is defined as the spectrum level with frequency resolution 2j·Δf. Here Δf is the frequency resolution at j=0. However, calculating all of these spectra one by one using the conventional FFT method would be prohibitively time-consuming, even on a modern FPGA. This is especially true for large values of P; e.g., P≥20. The goal of this communication is to introduce a new method that can produce multi-resolution spectrum lines corresponding to sample lengths 2P−j for all J+1 levels, concurrently, while one long 2P-length FFT is being calculated. That is, the lower resolution spectra are generated naturally as by-products during the computation of the 2P-length FFT, so there is no need to perform additional calculations in order to obtain them.

Background and Objectives: The modification of histone acetylation plays a vital role in regulating tumor occurrence and development, but the interaction between histone acetylation modulator genes and the liver hepatocellular carcinoma (LIHC) microenvironment, as well as immunotherapy, has not been investigated. Materials and Methods: Analysis of all statistical data was carried out using R software (Version 4.2.0) and the online tool Sangerbox. Comprehensive bioinformatics analysis, including signature construction and validation, functional analyses, immune and genomic features analyses, and immunotherapy prediction analyses, were performed to explore the prognostic and therapeutic role of histone acetylation modulator genes in LIHC development and progression. Results: The LIHC cohort from The Cancer Genome Atlas (TCGA) database was selected as the training cohort; the GSE76427 cohort from the Gene Expression Omnibus (GEO) database and the LIRI-JP cohort from the International Cancer Genome Consortium (ICGC) database were selected as the validation cohorts. The histone acetylation modulator gene-based prognostic signature was constructed and validated successfully. Immune infiltration analysis showed that most immune cells and immune functions were enriched in patients with high histone acetylation risk scores (HARS). Additionally, high levels of checkpoint inhibitors (ICIs) and human leukocyte antigens (HLAs) were also observed in high HARS patients. Meanwhile, TIDE algorithm analysis was conducted to explore the relationship between HARS and immunotherapy response, and submap algorithm analysis was used for the verification of the results, from which we found that high HAPS patients were more likely to respond to immunotherapy. Conclusions: Our findings revealed that the histone acetylation modulator genes, particularly for KAT21, SIRT6, and HAT1, may have the potential to function as a new prognostic marker and therapeutic target for LIHC.

In the future, the pursuit of high-quality economic development and a focus on ecological environmental protection in China will inevitably result in significant conflicts between land use and ecological land use. Land use conflicts primarily occur in peri-urban areas characterized by prominent economic development and urban agglomeration. As a municipality situated in the Qin-Ba mountainous region, Ankang has been promoting ecological preservation for a span of two decades. The allocation of construction areas primarily focuses on the proximity of watersheds, while the northern and southern regions are predominantly characterized by mountainous and hilly terrain, thus exhibiting distinct regional attributes. Previous studies have mainly focused on analyzing the effects of land use on habitat quality during intense urbanization, with fewer studies focusing on integrating eco-regions with urbanization to assess changes in habitat quality. However, it is important to recognize that land pressure encompasses economic, ecological, and social aspects. Additionally, it aims to provide insights for the harmonious development of land use in eco-region-oriented cities. Using the SSPs-RCPs scenarios provided by the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper employs a system analysis method, the emerging Patch-Generating Land Use Simulation model (PLUS) model, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and Land Use Conflict Measurement model to dynamically simulate the future habitat quality and spatial conflict patterns of land use in Ankang City. The study explores the spatial coupling effect of both factors under different scenarios. The results indicate the following: (1) The overall development trend reveals an intensification of land use conflicts and a decrease in habitat quality. The expansion rate of construction land is increasing and exhibiting aggregation, while agricultural land area is expanding, and forest land area is continuously decreasing. (2) Land use intensity exhibits a significant positive correlation with land conflict levels, while land conflict levels demonstrate a significant negative correlation with habitat quality. (3) Under different future shared socioeconomic path scenarios, land use intensity, and land conflict levels follow the order of SSP585 (high forcing scenario), SSP370 (medium to high forcing scenario), SSP245 (medium forcing scenario), and SSP126 (low forcing scenario), with intensity and conflict decreasing accordingly. These findings suggest that land use has had some impact on the ecological environment, with indications of habitat degradation. Even in Ankang, where ecological development is highly valued, the city will gradually face conflicts between ecological protection and economic development in future scenarios. The city already has the environment at heart, so it will be worse in cities where the priority is economic development. Therefore, it is crucial to allocate sufficient space for economic development while simultaneously prioritizing ecological protection. The results of the study can provide a reference for analyzing the trade-offs between land development and habitat protection in eco-mountainous cities and for eco-cities to avoid falling into the conflict dilemma of economic cities.

In the field of multi-view SAR target location, the greater the difference in viewing angles, the higher the target location accuracy. However, this makes it difficult to match the same target between the SAR images with different viewing angles. The performance of traditional SAR image-matching algorithms will deteriorate or even fail to match the images correctly when the viewing angle is gradually increased. To solve this problem, a fast SAR matching method for the SAR images with large viewing angles based on inertial navigation information and neighborhood structure consensus (ININSC) is proposed in this paper. In this algorithm, the key targets are detected in the SAR images by using the maximum connected domain algorithm and the K-means clustering algorithm, and the connected domain centroid of the target is taken as a feature point. The approximate position of the key targets after the viewing angle change is found through inertial navigation information, and then accurate and fast matching is achieved by using the consensus of the neighborhood topological structure of the key targets. The measured data sets demonstrate that compared with traditional SAR image-matching algorithms, the proposed ININSC algorithm solves such a problem that SAR images cannot be accurately matched under the differences at large viewing angles and has better robustness and timeliness.

OTUD6A is a deubiquitinase that plays crucial roles in various human diseases. However, the precise regulatory mechanism of OTUD6A remains unclear. In this study, we found that OTUD6A significantly inhibited the production of type I interferon. Consistently, peritoneal macrophages and bone marrow-derived macrophages from Otud6a−/− mice produced more type I interferon after virus infection compared to cells from WT mice. Otud6a−/−− mice also exhibited increased resistance to lethal HSV-1 and VSV infections, as well as LPS attacks due to decreased inflammatory responses. Mechanistically, mass spectrometry results revealed that UBC13 was an OTUD6A-interacting protein, and the interaction was significantly enhanced after HSV-1 stimulation. Taken together, our findings suggest that OTUD6A plays a crucial role in the innate immune response and may serve as a potential therapeutic target for infectious disease.

Eicosatetraenoic acid (ETA, 20:4, ω-3) is the desaturation product of dihomo-gamma linolenic acid (DGLA, 20:3, ω-6) catalyzed by delta-17 desaturase, which is considered as a healthy product that helps to lower risk of heart diseases. The oleaginous filamentous fungus, Mucor circinelloides, has been used for a long time as a model micro-organism for GLA production at industrial scales. However, M. circinelloides lacks the key enzymes to synthesize C20 polyunsaturated fatty acids (PUFAs). M. circinelloides could produce DGLA by overexpressing the D6E(GLELO) gene, which could be a useful tool to produce ETA due to the availability of established genetic manipulation tools. Therefore, in this study, delta-17 desaturase (PpD17 and PaD17) genes from Phytophthora parasitica and Pythium aphanidermatum, respectively, were introduced into M. circinelloides to construct an ETA-producing cell factory. Our results showed that the PaD17 and PpD17 overexpression strains’ biomass increased by 25.98 and 23.34 g/L (39.98 and 25.75%), respectively, compared with the control strain. Meanwhile, the lipid contents of the recombinant strains also increased and reached up to 28.88% in Mc-PaD17 and 30.95% in Mc-PpD17, respectively, compared with the control strain (23.38% in Mc-2076). The RT-qPCR results showed that overexpression of delta-17 desaturase genes promoted the expression of cme2, fas2, and D6E, thereby contributing to lipid biosynthesis in M. circinelloides. Meanwhile, the content of ETA reached up to 1.95%, and the yield of ETA was up to 114.69 mg/L in PpD17 overexpression mutants at 96 h. This study provided the first report on the construction of an ETA-producing cell factory by heterologous overexpression of the PpD17 gene in M. circinelloides, which established a new scope for further research in the production of ETA in oleaginous fungi.

The direct or indirect discharge of organic pollutants causes serious environmental problems and endangers human health. The high electron–hole recombination rate greatly limits the catalytic efficiency of traditional TiO2-based catalysts. Therefore, starting from low-cost activated carbon (AC), a photocatalyst (F-Si-TiO2/AC) comprising fluorine (F)- and silicon (Si)-doped TiO2 loaded on AC has been developed. F-Si-TiO2/AC has a porous structure. TiO2 nanoparticles were uniformly fixed on the surface or pores of AC, producing many catalytic sites. The band gap of F-Si-TiO2/AC is only 2.7 eV. In addition, F-Si-TiO2/AC exhibits an excellent adsorption capacity toward methyl orange (MO) (57%) in the dark after 60 min. Under the optimal preparation conditions, F-Si-TiO2/AC showed a significant photodegradation performance toward MO, reaching 97.7% after irradiation with visible light for 70 min. Even under the action of different anions and cations, its degradation efficiency is the lowest, at 64.0%, which has good prospects for practical application. At the same time, F-Si-TiO2/AC has long-term, stable, practical application potential and can be easily recovered from the solution. Therefore, this work provides new insights for the fabrication of low-cost, porous, activated, carbon-based photocatalysts, which can be used as high-performance photocatalysts for the degradation of organic pollutants.

Shikimate is a valuable chiral precursor for synthesizing oseltamivir (Tamiflu®) and other chemicals. High production of shikimate via microbial fermentation has attracted increasing attention to overcome the unstable and expensive supply of shikimate extracted from plant resources. The current cost of microbial production of shikimate via engineered strains is still unsatisfactory, and thus more metabolic strategies need to be investigated to further increase the production efficiency. In this study, we first constructed a shikimate E. coli producer through the application of the non-phosphoenolpyruvate: carbohydrate phosphotransferase system (non-PTS) glucose uptake pathway, the attenuation of the shikimate degradation metabolism, and the introduction of a mutant of feedback-resistant 3-deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) synthase. Inspired by the natural presence of bifunctional 3-dehydroquinate dehydratase (DHD)-shikimate dehydrogenase (SDH) enzyme in plants, we then designed an artificial fusion protein of DHD-SDH to decrease the accumulation of the byproduct 3-dehydroshikimate (DHS). Subsequently, a repressed shikimate kinase (SK) mutant was selected to promote shikimate accumulation without the supplementation of expensive aromatic substances. Furthermore, EsaR-based quorum sensing (QS) circuits were employed to regulate the metabolic flux distribution between cell growth and product synthesis. The final engineered strain dSA10 produced 60.31 g/L shikimate with a yield of 0.30 g/g glucose in a 5 L bioreactor.

Cultural relics conservation and prevention from bacterial deterioration are critical for our historical heritage. Thus far, the variations of the ecophysiological features of deteriorating bacterial communities along gradients of temperature and moisture remain unclear. In this study, we used high-throughput sequencing to investigate the changing pattern of bacterial communities on bricks at different positions along two such gradients in the Two Mausoleums of the Southern Tang Dynasty, which have more than 1000 years of history. We found that the tombs were inhabited by a phylogenetically and functionally diverse bacterial microbiomes. Herein, Proteobacteria (34.5%), Cyanobacteria (31.3%), Bacteroidetes (7.8%) and Actinobacteria (7.4%), as well as ‘Amino Acid Metabolism (11.2%)’ and ‘Carbohydrate Metabolism (10.5%)’ accounted for the majorities of their compositional and functional profiles related to biodeterioration. Non-metric scaling in combination with PERMANOVA tests indicated that shifts in bacterial community compositions were governed by temperature, followed by moisture. In addition, we found that tourism-related anthropogenic activities could have played non-negligible roles in community assembly, especially in the areas that account as attractions (i.e., back room of the Qinling Mausoleum). Collectively, this study advances the knowledge regarding the deteriorating microbiomes of cultural monuments, which is essential for the conservation of historical cultural relics.

The hardware-accelerated time–frequency distribution calculation is one of the commonly used methods to analyze and present the information from intercepted radio frequency signals in modern ultra-wideband digital receiver (DRX) designs. In this paper, we introduce the piecewise constant window blocking FFT (PCW-BFFT) method. The purpose of this work is to show the generation of spectrograms (formed by a number of spectrum lines) using a very large number of samples (N) in an FFT frame for each spectrum line calculation. In the PCW-BFFT, the N samples are grouped into K consecutive time slots, and each slot has M number of samples. As soon as the M samples in the current time slot are available from a high-speed analog-to-digital convertor (ADC), the frequency information will be obtained using K M-point FFTs. Since each time the FFT frame hops one time slot for the next spectrum line calculation, the frequency information obtained from a time slot will be reused in many spectrum line calculations, as long as these spectrum lines share those samples in the time slot. Although the use of the time domain PCW introduces spikes in the frequency spectrum of the window, the levels of those spikes are still much lower than the first side lobe level of a rectangular window. Using a Gaussian window as an example, the highest spike level can be lower than the main lobe level by at least 38 dB. The PCW-BFFT method allows a DRX to produce multiple spectrograms concurrently with different analysis window widths when the time domain samples become available continuously from the ADC. This paper presents the detailed derivation process of the PCW-BFFT method and demonstrates the use of the method with simulation results. The hardware implementation process will be reported in another paper. The computer simulation results show that long signals with slowly changing frequencies over time can be depicted on the spectrograms with wide analysis windows, and short pulses and signals with rapidly changing instantaneous frequencies can be captured in the narrow analysis window spectrograms.

The interaction between polyphenols and polysaccharides plays an important role in increasing the turbidity stability of fruit juice and improving unpleasant sensory experiences. The binding adsorption behavior between hawthorn pectin (HP) and polyphenols (epicatechin and chlorogenic acid) accorded with the monolayer adsorption behavior driven by chemical action and were better fitted by pseudo-second order dynamic equation and Langmuir model. The HP binding sites (Qm) and adsorption capacity (Qe) to epicatechin were estimated at 75.188 and 293.627 μg/mg HP, respectively, which was about nine and twelve times higher than that of chlorogenic acid. The interaction between HP and polyphenols exhibited higher turbidity characteristics, particle size and lower zeta potential than epicatechin and chlorogenic acid alone. Meanwhile, according to Fourier Transform Infrared Spectroscopy (FT-IR) analysis, it could be speculated that the interaction between HP and polyphenols resulted in chemical combination. Moreover, ΔH < 0 and TΔS < 0, which indicated that the interaction between HP and polyphenols was mainly driven by hydrogen bonds and van der Waals forces.

Pyemotes zhonghuajia Yu, Zhang & He (Prostigmata: Pyemotidae), discovered in China, has been demonstrated as a high-efficient natural enemy in controlling many agricultural and forestry pests. This mite injects toxins into the host (eggs, larvae, pupae, and adults), resulting in its paralyzation and then gets nourishment for reproductive development. These toxins have been approved to be mammal-safe, which have the potential to be used as biocontrol pesticides. Toxin proteins have been identified from many insects, especially those from the orders Scorpions and Araneae, some of which are now widely used as efficient biocontrol pesticides. However, toxin proteins in mites are not yet understood. In this study, we assembled the genome of P. zhonghuajia using PacBio technology and then identified toxin-related genes that are likely to be responsible for the paralytic process of P. zhonghuajia. The genome assembly has a size of 71.943 Mb, including 20 contigs with a N50 length of 21.248 Mb and a BUSCO completeness ratio of 90.6% (n = 1367). These contigs were subsequently assigned to three chromosomes. There were 11,183 protein coding genes annotated, which were assessed with 91.2% BUSCO completeness (n = 1066). Neurotoxin and dermonecrotic toxin gene families were significantly expanded within the genus of Pyemotes and they also formed several gene clusters on the chromosomes. Most of the genes from these two families and all of the three agatoxin genes were shown with higher expression in the one-day-old mites compared to the seven-day-pregnant mites, supporting that the one-day-old mites cause paralyzation and even death of the host. The identification of these toxin proteins may provide insights into how to improve the parasitism efficiency of this mite, and the purification of these proteins may be used to develop new biological pesticides.

Linolenic acid (LA) is gaining more interest within the scientific community. This is because it has a potential medical role in reducing the risk of inflammation, carcinogenesis, atherosclerosis and diabetes and is a valuable nutraceutical for human health. The oleaginous fungus Mucor circinelloides produces a high lipid content (36%), including valuable polyunsaturated fatty acids (PUFAs). However, the critical step in which oleic acid (OA) is converted into LA is not efficient at supplying enough substrates for PUFA synthesis. Hence, we propose a method to increase LA production based on genetic engineering. The overexpression of the Δ12-desaturase gene from M. circinelloides and Mortierella alpina increased the LA content and improved the lipid accumulation (from 14.9% to 21.6% in the Δ12-desaturase gene of the M. circinelloides overexpressing strain (Mc-D12MC) and from 14.9% to 18.7% in the Δ12-desaturase gene of M. alpina overexpressing strain (Mc-D12MA)). Additionally, the up-regulated expression levels of these genes targeted the genes involved in NADPH production, implying that the elevated Δ12-desaturase gene may function as a critical regulator of NADPH and lipid synthesis in M. circinelloides. This study provides the first evidence to support the design of metabolic engineering related to LA and PUFA production in M. circinelloides for potential industrial applications.

Due to its noninvasive nature, site-confined irradiation, and high tissue penetrating capabilities, ultrasound (US)-driven sonodynamic treatment (SDT) has been proven to have broad application possibilities in neoplastic and non-neoplastic diseases. However, the inefficient buildup of sonosensitizers in the tumor site remarkably impairs SDT efficiency. The present work proposes a deep-penetrating sonochemistry nanoplatform (Pp18-lipos@SRA737&DOX, PSDL) comprising Pp18 liposomes (Pp18-lipos, Plipo), SRA737 (a CHK1 inhibitor), and doxorubicin (DOX) for the controlled formation of reactive oxygen species (ROS) and release of DOX and SRA737 upon US activation, therefore increasing chemotherapeutic effectiveness and boosting SDT efficacy. Therein, the antitumor activities of DOX have been attributed to its intercalation into the nucleus DNA and induction of cell apoptosis. CHK1 evolved to respond to DNA damage and repair the damage via cell cycle progression. SRA737 is a potent and orally bioavailable clinical drug candidate for inhibiting CHK1, demonstrating adjuvant anticancer effect in vitro and in vivo. It was interesting to find that SRA737 carried into Plipo@DOX could significantly alleviate G2/M cell cycle arrest and aggravate DNA double-strand injuries, resulting in significant cell death. The developed US-switchable nanosystem provides a promising strategy for augmenting sono-chemotherapy against breast cancer controllably and precisely.

Adenosine deaminase (ADA) is an enzyme distributed in a wide variety of organisms that cleaves adenosine into inosine. Since inosine plays an important role in nitrogen metabolism, ADA may have a critical function in the regulation of fatty acid synthesis. However, the role of ADA in oleaginous fungi has not been reported so far. Therefore, in this study, we identified one ada gene encoding ADA (with ID scaffold0027.9) in the high lipid-producing fungus, Mucor circinelloides WJ11, and investigated its role in cell growth, lipid production, and nitrogen metabolism by overexpressing and knockout of this gene. The results showed that knockout of the ada altered the efficiency of nitrogen consumption, which led to a 20% increment in the lipid content (25% of cell dry weight) of the engineered strain, while overexpression of the ada showed no significant differences compared with the control strain at the final growth stage; however, interestingly, it increased lipid accumulation at the early growth stage. Additionally, transcriptional analysis was conducted by RT-qPCR and our findings indicated that the deletion of ada activated the committed steps of lipid biosynthesis involved in acetyl-CoA carboxylase (acc1 gene), cytosolic malic acid enzyme (cme1 gene), and fatty acid synthases (fas1 gene), while it suppressed the expression of AMP-activated protein kinase (ampk α1 and ampk β genes), which plays a role in lipolysis, whereas the ada-overexpressed strain displayed reverse trends. Conclusively, this work unraveled a novel role of ADA in governing lipid biosynthesis and nitrogen metabolism in the oleaginous fungus, M. circinelloides.

The uniaxial piezoelectric sensor was developed to overcome the problem of reflecting the output charge of the piezoelectric element as a combination of vectors in the three stress directions. The work performance of the uniaxial piezoelectric sensor under varying load patterns and load rates was investigated. The sensor was embedded in concrete to monitor stress, and its elastic modulus was used as the intermediate bridge to establish the correlation between the embedded sensor and the external sensor. Furthermore, a correction factor for the charge transformation strain was suggested to overcome the mismatching of the sensor’s medium and the concrete. Considering related circumstances, a new stress monitoring method based on a uniaxial piezoelectric sensor was proposed, which can achieve stress whole-process monitoring in concrete and confining stress monitoring in the reinforced concrete column. The results reveal that through the proposed method, the output charge curve of the sensor has a substantial overlap with the stress waveform and high fitting linearity. The work performance of the sensor was stable, and its sensitivity was not affected by loading rate and load pattern. The sensor was embedded in concrete and can coordinate with the concrete deformation. The correction factor of strain obtained by the sensor embedded in concrete was 1.07. The relationship between the charge produced by the embedded sensor and its external calibration sensitivity may be used to implement the whole process of stress monitoring in concrete.

The light brown apple moth, Epiphyas postvittana is an invasive, polyphagous pest of horticultural systems around the world. With origins in Australia, the pest has subsequently spread to New Zealand, Hawaii, California and Europe, where it has been found on over 500 plants, including many horticultural crops. We have produced a genomic resource, to understand the biological basis of the polyphagous and invasive nature of this and other lepidopteran pests. The assembled genome sequence encompassed 598 Mb and has an N50 of 301.17 kb, with a BUSCO completion rate of 97.9%. Epiphyas postvittana has 34% of its assembled genome represented as repetitive sequences, with the majority of the known elements made up of longer DNA transposable elements (14.07 Mb) and retrotransposons (LINE 17.83 Mb). Of the 31,389 predicted genes, 28,714 (91.5%) were assigned to 11,438 orthogroups across the Lepidoptera, of which 945 were specific to E. postvittana. Twenty gene families showed significant expansions in E. postvittana, including some likely to have a role in its pest status, such as cytochrome p450s, glutathione-S-transferases and UDP-glucuronosyltransferases. Finally, using a RAD-tag approach, we investigated the population genomics of this pest, looking at its likely patterns of invasion.

The surface defects of a hot-rolled strip will adversely affect the appearance and quality of industrial products. Therefore, the timely identification of hot-rolled strip surface defects is of great significance. In order to improve the efficiency and accuracy of surface defect detection, a lightweight network based on coordinate attention and self-interaction (CASI-Net), which integrates channel domain, spatial information, and a self-interaction module, is proposed to automatically identify six kinds of hot-rolled steel strip surface defects. In this paper, we use coordinate attention to embed location information into channel attention, which enables the CASI-Net to locate the region of defects more accurately, thus contributing to better recognition and classification. In addition, features are converted into aggregation features from the horizontal and vertical direction attention. Furthermore, a self-interaction module is proposed to interactively fuse the extracted feature information to improve the classification accuracy. The experimental results show that CASI-Net can achieve accurate defect classification with reduced parameters and computation.

This study discusses how to facilitate the barrier-free circulation of energy big data among multiple entities and how to balance the energy big data ecosystem under government supervision using dynamic game theory. First, we define the related concepts and summarize the recent studies and developments of energy big data. Second, evolutionary game theory is applied to examine the interaction mechanism of complex behaviors between power grid enterprises and third-party enterprises in the energy big data ecosystem, with and without the supervision of government. Finally, a sensitivity analysis is conducted on the main factors affecting co-opetition, such as the initial participation willingness, distribution of benefits, free-riding behavior, government funding, and punitive liquidated damages. The results show that both government supervision measures and the participants’ own will have an impact on the stable evolution of the energy big data ecosystem in the dynamic evolution process, and the effect of parameter changes on the evolution is more significant under the state of no government supervision. In addition, the effectiveness of the developed model in this work is verified by simulated analysis. The present model can provide an important reference for overall planning and efficient operation of the energy big data ecosystem.

High repetition-rate X-ray free-electron lasers (XFELs) enable the study of fast dynamics on microsecond time scales. Split-delay lines (SDLs) further bring the time scale down to femtoseconds by splitting and delaying the XFEL pulses. Crystals and multilayers are two common types of optical elements in SDLs, offering either long delay ranges or high temporal accuracy. In this work, we introduce the design of a hybrid SDL for the coherent diffraction endstation of Shanghai High Repetition Rate XFEL and Extreme Light Facility (SHINE). It uses crystals for the first branch and multilayers for the second one, thus simultaneously offering a relatively long delay range and high temporal accuracy. Moreover, a third branch can be installed to switch the SDL to the all-crystal configuration for longer delay ranges.

The growth of energy consumption has led to the depletion of fossil energy and the increasing greenhouse effect. In this case, low carbonization has become an important trend in the world’s energy development, in which clean energy occupies an important position. The uncertainties brought by the large-scale integration of wind power, photovoltaic and other renewable energy sources into the grid pose a serious challenge to system dispatch. The participation of demand response (DR) resources can flexibly cooperate with renewable energy, optimizing system dispatch and promoting renewable energy consumption. Thus, we propose a flexible DR scheduling strategy based on multiple response modes in this paper. We first present a DR resource operation model based on multivariate response modes. Then, the uncertainties are considered and dealt with by scenario generation and reduction technology. Finally, a day-head dispatch strategy considering flexible DR operation and wind power uncertainties is established. The simulation results show that the proposed strategy promotes wind power consumption and reduces system operation costs.

This article presents the hardware implementation and a behavioral model-based RF system modeling and simulation (M&S) study of compressive sensing (CS) based 2D angle-of-arrival (AoA) measurement system for 2–18 GHz radar electronic support measures (RESM). A 6-channel ultra-wideband RF digital receiver was first developed using a PXIe-based multi-channel digital receiver paired with a 6-element random-spaced 2D cavity-backed-spiral-antenna array. Then the system was tested in an open lab environment. The measurement results showed that the system can measure AoA of impinging signals from 2–18 (GHz) with overall RMSE of estimation at 3.60, 2.74, 1.16, 0.67 and 0.56 (deg) in L, S, C, X and Ku bands, respectively. After that, using the RF high-fidelity M&S (RF HF-M&S) approach, a 6-channel AoA measurement system behavioral model was also developed and studied using a radar electronic warfare (REW) engagement scenario. The simulation result showed that the airborne AoA measurement system could successfully measure an S-band ground-based target acquisition radar signal in the dynamic REW environment. Using the RF HF-M&S model, the applicability of the system in other frequencies within 2–18 (GHz) was also studied. The simulation results demonstrated that the airborne AoA measurement system can be used for 2–18 GHz RESM applications.

Theoretical or numerical progressive collapse analysis is necessary for important civil structures in case of unforeseen accidents. However, currently, most analytical research is carried out under the assumption of material elasticity for problem simplification, leading to the deviation of analysis results from actual situations. On this account, a progressive collapse analysis procedure for truss structures is proposed, based on the assumption of elastoplastic materials. A plastic importance coefficient was defined to express the importance of truss members in the entire system. The plastic deformations of members were involved in the construction of local and global stiffness matrices. The conceptual removal of a member was adopted, and the impact of the member loss on the truss system was quantified by bearing capacity coefficients, which were subsequently used to calculate the plastic importance coefficients. The member failure occurred when its bearing capacity arrived at the ultimate value, instead of the elastic limit. The extra bearing capacity was embodied by additional virtual loads. The progressive collapse analysis was performed by iterations until the truss became a geometrically unstable system. After that, the critical progressive collapse path inside the truss system was found according to the failure sequence of the members. Lastly, the proposed method was verified against both analytical and experimental truss structures. The critical progressive collapse path of the experimental truss was found by the failure sequence of damaged members. The experimental observation agreed well with the corresponding analytical scenario, proving the method feasibility.

In this paper, a novel signal processing algorithm for mitigating the radar blind speed problem of moving target indication (MTI) for frequency modulated continuous wave (FMCW) multi-target tracking radars is proposed. A two-phase staggered pulse repetition interval (PRI) solution is introduced to the FMCW radar system. It is implemented as a time-varying MTI filter using twice the hardware resources as compared to a uniform PRI MTI filter. The two-phase staggered PRI FMCW waveform is still periodic with a little more than twice the period of the uniform PRI radar. We also propose a slow time signal integration scheme for the radar detector using the post-fast Fourier transformation Doppler tracking loop. This scheme introduces 4.77 dB of extra signal processing gain to the signal before the radar detector compared with the original uniform PRI FMCW radar. The validation of the algorithm is done on the field programmable logic array in the loop test bed, which accurately models and emulates the target movement, line of sight propagation and radar signal processing. A simulation run of tracking 16 s of the target movement near or at the radar blind speed shows that the total degradation from the raw post-fast Fourier transformation received signal to noise ratio is about 2 dB. With a 20 dB post-processing signal to noise ratio of the proposed algorithm for the moving target at around a 20 km range and with about a −3.5 dB m2 radar cross section at a 1.5 GHz carrier frequency, the tracking errors of the two-dimensional angles with a 4×4 digital phased array are less than 0.2 degree. The range tracking error is about 28 m.

Vehicle re-identification plays an important role in cross-camera tracking and vehicle search in surveillance videos. Large variance in the appearance of the same vehicle captured by different cameras and high similarity of different vehicles with the same model poses challenges for vehicle re-identification. Most existing methods use a center proxy to represent a vehicle identity; however, the intra-class variance leads to great difficulty in fitting images of the same identity to one center feature and the images with high similarity belonging to different identities cannot be separated effectively. In this paper, we propose a sampling strategy considering different viewpoints and a multi-proxy constraint loss function which represents a class with multiple proxies to perform different constraints on images of the same vehicle from different viewpoints. Our proposed sampling strategy contributes to better mine samples corresponding to different proxies in a mini-batch using the camera information. The multi-proxy constraint loss function pulls the image towards the furthest proxy of the same class and pushes the image from the nearest proxy of different class further away, resulting in a larger margin between decision boundaries. Extensive experiments on two large-scale vehicle datasets (VeRi and VehicleID) demonstrate that our learned global features using a single-branch network outperforms previous works with more complicated network and those that further re-rank with spatio-temporal information. In addition, our method is easy to plug into other classification methods to improve the performance.

Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed. However, the point spread function (PSF) in the radar images is sometimes spatially variant and affects the performance of the CNN. We propose the spatial-variant convolutional neural network (SV-CNN) aimed at this problem. It will also perform well in other conditions when there are spatially variant features. The convolutional kernels of the CNN can detect motifs with some distinctive features and are invariant to the local position of the motifs. This makes the convolutional neural networks widely used in image processing fields such as image recognition, handwriting recognition, image super-resolution, and semantic segmentation. They also perform well in radar image enhancement. However, the local position invariant character might not be good for radar image enhancement, when features of motifs (also known as the point spread function in the radar imaging field) vary with the positions. In this paper, we proposed an SV-CNN with spatial-variant convolution kernels (SV-CK). Its function is illustrated through a special application of enhancing the radar images. After being trained using radar images with position-codings as the samples, the SV-CNN can enhance the radar images. Because the SV-CNN reads information of the local position contained in the position-coding, it performs better than the conventional CNN. The advance of the proposed SV-CNN is tested using both simulated and real radar images.

Recent studies show that the loss of stability for a voltage-source converter (VSC) in weak-grid connection is largely related to its synchronization unit, i.e., the phase-locked loop (PLL). This paper studies the synchronization stability of a system comprised by two VSCs in parallel connection to a weak grid. A reduced transfer function based small-signal model, which can allow for the interactions between PLL and converter outer power controls, is first proposed. Then, an improved net damping criterion is used to analyze the damping and stability characters of such system under various operating conditions and different controller configurations. Compared to the conventional net damping criterion, the used criterion has wider applicability in terms of stability judgment. Case studies show that the studied system tends to be unstable at weak-grid or heavy-loading conditions. The instability can be in the form of oscillations or monotonic divergence, in which, the latter is more likely to occur for the converters without grid voltage regulation capabilities. Besides, the net damping-based sensitivity studies can provide guidance on control tuning or design for stability enhancement. Detailed model-based time domain simulations are conducted to verify the analysis results.

Steel sleeve grouting connections are widely used in prefabricated concrete structures. It is well known that insufficient grouting increases the chance of structural failure. As such, it is critical to monitor the density and compactness of grouting sleeve during the construction process, which however remains significant challenges as it is deeply buried in the beam and column. In this study, a lead zirconate titanate (PZT)-based sleeve grouting compactness detection method was systematically investigated. Five grouting sleeves samples with different degrees of compactness were prepared and four PZT transducers were surface-bonded on opposite sides of the outer wall of each sleeve. Two acts as actuators to generate stress wave signals, and the other two operate as sensors to receive the signals. The wavelet packet energy and Hilbert−Huang transform methods were applied to process the stress wave signals, and with the chosen characteristic parameters, the correction model of the grouting compactness was established. Experimental results show that the wavelet packet total energy values and the Hilbert energy peak values are related to the grouting compactness, indicating the feasibility of using PZT sensors to detect the compactness of grouting sleeves.

First-principles calculations based on the density functional theory (DFT) were carried out to study the atomic structure and electronic structure of LiAl2(OH)6Cl, the only material in the layered double hydroxide family in which delithiation was found to occur. Ab initio molecular dynamics (AIMD) simulations were used to explore the evolution of the structure of LiAl2(OH)6Cl during a thermally induced delithiation process. The simulations show that this process occurs due to the drastic dynamics of Li+ at temperatures higher than ~450 K, in which the [Al2(OH)6] host layers remain stable up to 1100 K. The calculated large value of the Li+ diffusion coefficient D, ~ 3.13 × 10 − 5 c m 2 / s , at 500 K and the high stability of the [Al2(OH)6] framework suggest a potential technical application of the partially-delithiated Li1-xAl2(OH)6Cl1-x (0 < x < 1) as a superionic conductor at high temperatures.

Studies of the spatial extent of surface urban heat island (SUHI or UHISurf) effects require precise determination of the footprint (FP) boundary. Currently available methods overestimate or underestimate the SUHI FP boundary, and can even alter its morphology, due to theoretical limitations on the ability of their algorithms to accurately determine the impacts of the shape, topography, and landscape heterogeneity of the city. The key to determining the FP boundary is identifying background temperatures in reference rural regions. Due to the instability of remote sensing data, these background temperatures should be determined automatically rather than manually, to eliminate artificial bias. To address this need, we developed an algorithm that adequately represents the decay of land surface temperature (LST) from the urban center to surrounding rural regions, and automatically calculates thresholds for reference rural LSTs in all directions based on a logistic curve. In this study, we applied this algorithm with data from the Aqua Moderate Resolution Imaging Spectroradiometer (Aqua/MODIS) 8-day level 3 (L3) LST global grid product to delineate precise SUHI FPs for the Beijing metropolitan area during the summers of 2004−2018 and determine the interannual and diurnal variations in FP boundaries and their relationship with SUHI intensity.

More renewable energy resources have been connected to the grid with the promotion of global energy strategies, which presents new opportunities for the current electricity market. However, the growing integration of renewable energy also brings more challenges, such as power system reliability and the participants’ marketable behavior. Thus, how to coordinate integrated renewable resources in the electricity market environment has gained increasing interest. In this paper, a bilevel bidding model for load serving entities (LSEs) considering grid-level energy storage (ES) and virtual power plant (VPP) is established in the day-ahead (DA) market. Then, the model is extended by considering contingencies in the intraday (ID) market. Also, according to the extended bidding model, a remedial strategic rescheduling approach for LSE’s daily profit is proposed. It provides a quantitative assessment of LSE’s loss reduction based on contingency forecasting, which can be applied to the power system dispatch to help LSEs deal with coming contingencies. Simulation results verify the correctness and effectiveness of the proposed method.

Impervious surface mapping incorporating high-resolution remote sensing imagery has continued to attract increasing interest, as it can provide detailed information about urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping yields better performance than the use of high-resolution imagery alone. However, due to LiDAR data’s high cost of acquisition, it is difficult to obtain LiDAR data that was acquired at the same time as the high-resolution imagery in order to conduct impervious surface mapping by multi-sensor remote sensing data. Consequently, the occurrence of real landscape changes between multi-sensor remote sensing data sets with different acquisition times results in misclassification errors in impervious surface mapping. This issue has generally been neglected in previous works. Furthermore, observation differences that were generated from multi-sensor data—including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images—also present obstacles to achieving the final mapping result in the fusion of LiDAR data and high-resolution images. In order to resolve these issues, we propose an improved impervious surface-mapping method incorporating both LiDAR data and high-resolution imagery with different acquisition times that consider real landscape changes and observation differences. In the proposed method, multi-sensor change detection by supervised multivariate alteration detection (MAD) is employed to identify the changed areas and mis-registered areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution image are extracted via independent classification based on the corresponding single-sensor data. Finally, an object-based post-classification fusion is proposed that takes advantage of both independent classification results while using single-sensor data and the joint classification result using stacked multi-sensor data. The impervious surface map is subsequently obtained by combining the landscape classes in the accurate classification map. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and unambiguously improve the performance of impervious surface mapping.

Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity.

Tropospheric delay is a major error source that affects the performance of the Global Navigation Satellite Systems (GNSS) Real Time Kinematic (RTK) positioning especially for the medium/long-range baseline. Although high precision tropospheric delay can be estimated by GNSS carrier phase measurement, together with position and ambiguity, a relatively long period of convergence time is necessary. In this study, we develop a new GPS/BDS RTK algorithm constrained with a tropospheric delay parameters from Numerical weather prediction (NWP) model for medium/long-range baselines. The accuracy of the tropospheric delays derived from NWP is assessed through comparisons with the results of GAMIT (GNSS at MIT). The positioning performance with standard GPS RTK, standard GPS/BDS RTK, the developed NWP-constrained GPS RTK and NWP-constrained GPS/BDS RTK over medium/long-range baselines are compared in terms of the initialization time and the positioning accuracy. Experiment results show that the mean differences between the NWP and GAMIT zenith tropospheric delay (ZTD) are between −5.50 mm and 5.60 mm, and the RMS values of the NWP ZTD residuals are from 24.02 mm to 32.62 mm. A reduction in the initialization time of over 41% and 58% for medium- and long-range baselines can be achieved with the NWP-constrained RTK (both GPS alone and GPS/BDS RTK solutions) compared to the standard RTK solution, respectively. An improvement of over 30% can be found with the GPS/BDS RTK compared with that of the GPS alone RTK for both standard and the NWP-constrained modes. The positioning precision of NWP-constrained GPS/BDS RTK is better than 3 cm in the horizontal direction and better than 5 cm in the vertical direction, which satisfies the requirement of the precise positioning service.

Fusing multiple change detection results has great potentials in dealing with the spectral variability in multitemporal very high-resolution (VHR) remote sensing images. However, it is difficult to solve the problem of uncertainty, which mainly includes the inaccuracy of each candidate change map and the conflicts between different results. Dempster–Shafer theory (D–S) is an effective method to model uncertainties and combine multiple evidences. Therefore, in this paper, we proposed an urban change detection method for VHR images by fusing multiple change detection methods with D–S evidence theory. Change vector analysis (CVA), iteratively reweighted multivariate alteration detection (IRMAD), and iterative slow feature analysis (ISFA) were utilized to obtain the candidate change maps. The final change detection result is generated by fusing the three evidences with D–S evidence theory and a segmentation object map. The experiment indicates that the proposed method can obtain the best performance in detection rate, false alarm rate, and comprehensive indicators.

With the wide deployment of the video sensor network and the rapid development of video spatialization technology, the large volume of complex GeoVideo data necessitates improvements in the application efficiency of the GeoVideo database and GeoVideo surveillance system. Traditional storage management approaches focus on the optimization of access to the GeoVideo stream. However, they suffer from poor management of the diverse movement processes contained within it; for example, they cannot support associative queries or comprehensive analysis of multi-type GeoVideo data in complex geographic environments. This paper takes physical movement process in GeoVideo as a new type of object and carries out an objectified organization of the heterogeneous GeoVideo data around it (including the video stream, spatial references, interpretations of the video data, and the overall scene) in a Not only SQL-Structured Query Language (NoSQL-SQL) hybrid GeoVideo database. This paper systematically explores the hybrid spatiotemporal indexes and multi-modal retrieval methods around movement processes, which enrich the query modes of the GeoVideo data. A prototype implementation and experimental analysis are presented to prove the feasibility and effectiveness of this organization and retrieval approach.

This paper discusses the Muskingum model as a novel parameter estimation method. Sixty representative floods over the past four decades serve as research objects; a linear Muskingum model and Pigeon-inspired optimization (PIO) algorithm are used to obtain the parameters of each flood. The proposed “in-process type” dynamic parameter estimation (IP-DPE) method is used to establish the characteristic attributes set of 50 floods. The characteristic attributes set refers to a set of parameters that could describe the shape, magnitude, and duration of the flood before flood peak; they are the input, whereas parameters K and x of each flood are the output to establish a Neural Network model. Then we input flood characteristic attributes to obtain flood parameters when estimating flood parameters practically. Ten floods were used to test the parameter estimation and flood routing efficacy. The results show that the IP-DPE method can quickly identify parameters and facilitate accurate river flood forecasting.

Electrode materials and electrolytes play a vital role in device-level performance of rechargeable Li-ion batteries (LIBs). However, electrode structure/component degeneration and electrode-electrolyte sur-/interface evolution are identified as the most crucial obstacles in practical applications. Thanks to its congenital advantages, atomic layer deposition (ALD) methodology has attracted enormous attention in advanced LIBs. This review mainly focuses upon the up-to-date progress and development of the ALD in high-performance LIBs. The significant roles of the ALD in rational design and fabrication of multi-dimensional nanostructured electrode materials, and finely tailoring electrode-electrolyte sur-/interfaces are comprehensively highlighted. Furthermore, we clearly envision that this contribution will motivate more extensive and insightful studies in the ALD to considerably improve Li-storage behaviors. Future trends and prospects to further develop advanced ALD nanotechnology in next-generation LIBs were also presented.

With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks, sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors, especially in public security and smart city domains, are becoming challenging issues. The traditional geographic information system (GIS) mostly manages time-discretized geospatial data by means of the Structured Query Language (SQL) database management system (DBMS) and emphasizes query and retrieval of massive historical geospatial data on disk. This limits its capability for on-the-fly access of real-time geospatial data for online analysis in real time. This paper proposes a hybrid database organization and management approach with SQL relational databases (RDB) and not only SQL (NoSQL) databases (including the main memory database, MMDB, and distributed files system, DFS). This hybrid approach makes full use of the advantages of NoSQL and SQL DBMS for the real-time access of input data and structured on-the-fly analysis results which can meet the requirements of increased spatio-temporal big data linking analysis. The MMDB facilitates real-time access of the latest input data such as the sensor web and IoT, and supports the real-time query for online geospatial analysis. The RDB stores change information such as multi-modal features and abnormal events extracted from real-time input data. The DFS on disk manages the massive geospatial data, and the extensible storage architecture and distributed scheduling of a NoSQL database satisfy the performance requirements of incremental storage and multi-user concurrent access. A case study of geographic video (GeoVideo) surveillance of public security is presented to prove the feasibility of this hybrid organization and management approach.