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α-L-Rhamnosidases with desirable activity and thermostability profiles could be used for the biocatalytic production of the flavonoid glucoside isoquercetin from natural rutin for functional food. Herein, to improve the catalytic activity of GH78 α-L-rhamnosidase BtRha78A from Bacteroides thetaiotaomicron VPI-5482, a list of residues located at the conserved general acid motif were selected for targeted mutagenesis by the sequence alignment of BtRha78A with homologous α-L-rhamnosidases. Ala-scanning mutagenesis and site-directed mutagenesis based on sequence alignment were performed, and the relative activity on rutin was evaluated. Furthermore, the reaction time curves and enzyme kinetics of better mutants were determined. The results indicate that the conversion rates of mutants V338A, V338I, S340A, and G341A were increased by 21.3%, 20.1%, 13.2%, and 1.6%, respectively, compared with the wild type when using whole-cell biotransformation. Moreover, the catalytic efficiency kcat/KM value of mutant V338A was 1.3-fold higher than that of the wild type. The best mutant, V338A, was employed for the enzymatic preparation of isoquercetin via the biotransformation of rutin at a concentration of 2 mM, and 1.80 g of isoquercetin was obtained. The identification of the best mutant V338A lays the foundation for the efficient preparation of isoquercetin via the biotransformation of rutin, which in turn provides theoretical guidance for its large-scale production.

Microbial keratitis (MK) is the fourth leading cause of blindness globally, imposing a substantial burden on the healthcare system. This study aims to determine the cost composition of MK patients and explore factors influencing these expenses. We analyzed the demographics, clinical features, and costs of 602 MK patients treated at Beijing Tongren Hospital from June 2021 to October 2023. The analysis revealed the average total cost of treating MK was USD 1646.8, with a median of USD 550.3 (IQR: 333.3–1239.1). Patients with Acanthamoeba keratitis (AK) incurred the highest median total costs at USD 706.2 (IQR: 399.2–3370.2). Additionally, AK patients faced the highest costs for ophthalmic exams and laboratory tests (both p < 0.001), while patients with fungal keratitis (FK) and viral keratitis (VK) experienced higher medication costs. Costs varied significantly with the severity of MK, especially for outpatients at severity level 4, which was markedly higher than levels 1–3 (USD 1520.1 vs. USD 401.0, p < 0.001). Delayed presentation also resulted in increased costs (USD 385.2 vs. USD 600.3, p < 0.001). Our study highlights the financial burden associated with MK treatment and underscores the importance of timely and accurate diagnosis and intervention.

Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map.

Accurate and early diagnosis of monkeypox virus (MPXV) is crucial for controlling epidemics and treating affected individuals promptly. This study aimed to assess the analytical and clinical performance of the MolecisionTM Monkeypox Virus qPCR Assay, Biorain Monkeypox Virus ddPCR Assay, and MAGLUMI® Monkeypox Virus Ag (chemiluminescence immunoassay, CLIA) Assay. Additionally, it aimed to compare the clinical application of antigen and nucleic acid assays to offer insights into using commercial monkeypox assay kits. Specimens from 117 clinical patients, serial diluted virus cell culture supernatant, and artificially created positive samples were tested to evaluate the performance of these assay kits for MPXV diagnostics. The Biorain Monkeypox Virus ddPCR Assay had a limit of detection (LoD) of 3.89 CCID50/mL, while the MolecisionTM Monkeypox Virus qPCR Assay had an LoD of 15.55 CCID50/mL. The MAGLUMI® Monkeypox Virus Ag (CLIA) Assay had an LoD of 0.500 pg/mL. The accuracy of the MolecisionTM Monkeypox Virus qPCR Assay was comparable to the Biorain Monkeypox Virus ddPCR Assay, and the MAGLUMI® Monkeypox Virus Ag (CLIA) Assay demonstrated high sensitivity. The specificity of all three MPXV diagnostic assays for clinical specimens with potential cross-reacting substances was 100%. In conclusion, this study provides valuable insights into the clinical application of monkeypox assays, supporting efforts to mitigate and control the spread of monkeypox.

As the synthesis, modularization, and integration of avionics systems increase, the interconnections between systems and equipment within subsystems become increasingly complex, posing risks to the safety and reliability of the integrated avionics system. To address the risk of fault propagation due to functional cascade failures in integrated avionics systems, this paper proposes a discrete dynamic fault propagation analysis method, which was applied to an all-electric braking system to assess its feasibility. First, the architectural features of the Distributed Integrated Modular Avionics system are summarized. Subsequently, the constructed system layer model is described, establishing the function–resource hierarchical architecture. Subsequently, the behavior of cascading failure propagation in discrete dynamic systems is analyzed by integrating the cascading failure analysis method from SAE ARP 4761A and considering the coupling characteristics between system properties and functions comprehensively. This approach facilitates the development of a cascading failure propagation model for DIMA based on discrete dynamic systems. Finally, by using the all-electric braking system under DIMA architecture as a case study, key Core Processing Modules and failure-prone functions are identified. The findings reveal that within this system, CPM2 and CPM6 are particularly susceptible to failure propagation, and the automatic brake function is notably vulnerable. Data show that the system’s failure rate escalates markedly after 2×104 h of operation. Performing maintenance before reaching this threshold can further mitigate risks. This practice aligns with current international aircraft maintenance time regulations. The method proposed in this paper can be applied early in the allocation of DIMA resources to enhance security and support DIMA design.

Electrostatic capacitors, with the advantages of high-power density, fast charging–discharging, and outstanding cyclic stability, have become important energy storage devices for modern power electronics. However, the insulation performance of the dielectrics in capacitors will significantly deteriorate under the conditions of high temperatures and electric fields, resulting in limited capacitive performance. In this paper, we report a method to improve the high-temperature energy storage performance of a polymer dielectric for capacitors by incorporating an extremely low loading of 0.5 wt% carbon quantum dots (CQDs) into a fluorene polyester (FPE) polymer. CQDs possess a high electron affinity energy, enabling them to capture migrating carriers and exhibit a unique Coulomb-blocking effect to scatter electrons, thereby restricting electron migration. As a result, the breakdown strength and energy storage properties of the CQD/FPE nanocomposites are significantly enhanced. For instance, the energy density of 0.5 wt% CQD/FPE nanocomposites at room temperature, with an efficiency (η) exceeding 90%, reached 9.6 J/cm3. At the discharge energy density of 0.5 wt%, the CQD/FPE nanocomposites remained at 4.53 J/cm3 with an efficiency (η) exceeding 90% at 150 °C, which surpasses lots of reported results.

Recent research has revealed that the deep neural network (DNN)-based synthetic-aperture radar (SAR) automatic target recognition (ATR) techniques are vulnerable to adversarial examples, which poses significant security risks for their deployment in real-world systems. At the same time, the adversarial examples often exhibit transferability across DNN models, whereby when they are generated on the surrogate model they can also attack other target models. As the significant property in black-box scenarios, transferability has been enhanced by various methods, among which input transformations have demonstrated excellent effectiveness. However, we find that existing transformations suffer from limited enhancement of transferability due to the unique imaging mechanism and scattering characteristics of SAR images. To overcome this issue, we propose a novel method called intra-class transformations and inter-class nonlinear fusion attack (ITINFA). It enhances transferability from two perspectives: intra-class single image transformations and inter-class multiple images fusion. The intra-class transformations module utilizes a series of diverse transformations that align with the intrinsic characteristics of SAR images to obtain a more stable gradient update direction and prevent the adversarial examples from overfitting the surrogate model. The inter-class fusion strategy incorporates the information from other categories in a nonlinear manner, effectively enhances the feature fusion effect, and guides the misclassification of adversarial examples. Extensive experiments on the MSTAR dataset and SEN1-2 dataset demonstrate that ITINFA exhibits significantly better transferability than the existing transfer-based methods, with the average transfer attack success rate increases exceeding 8% for single models and over 4% for ensemble models.

The preoperative assessment of difficult airways is of great significance in the practice of anesthesia intubation. In recent years, although a large number of difficult airway recognition algorithms have been investigated, defects such as low recognition accuracy and poor recognition reliability still exist. In this paper, we propose a Dual-Path Multi-View Fusion Network (DMF-Net) based on multi-view metric learning, which aims to predict difficult airways through multi-view facial images of patients. DMF-Net adopts a dual-path structure to extract features by grouping the frontal and lateral images of the patients. Meanwhile, a Multi-Scale Feature Fusion Module and a Hybrid Co-Attention Module are designed to improve the feature representation ability of the model. Consistency loss and complementarity loss are utilized fully for the complementarity and consistency of information between multi-view data. Combined with Focal Loss, information bias is effectively avoided. Experimental validation illustrates the effectiveness of the proposed method, with the accuracy, specificity, sensitivity, and F1 score reaching 77.92%, 75.62%, 82.50%, and 71.35%, respectively. Compared with methods such as clinical bedside screening tests and existing artificial intelligence-based methods, our method is more accurate and reliable and can provide a reliable auxiliary tool for clinical healthcare personnel to effectively improve the accuracy and reliability of preoperative difficult airway assessments. The proposed network can help to identify and assess the risk of difficult airways in patients before surgery and reduce the incidence of postoperative complications.

The spaceborne turntable is a piece of precision optical equipment utilized for monitoring space information and facilitating laser communication. The measurement accuracy of optical instruments is correlated to some extent with the mechanical properties of the structure. Addressing the design challenge posed by the U-frame in the spaceborne optoelectronic tracking and pointing turntable, this paper proposes a multi-constraint topology optimization method utilizing Abaqus/CAE and TOSCA. This method offers insights for U-frame design and has undergone scientific validation. Initially, the empirical design structure is established, optimization design areas are defined, and the initial model undergoes pre-processing in Abaqus/CAE. Subsequently, conceptual and mathematical models for topology optimization are developed, with the objective of minimizing structural compliance (thereby maximizing stiffness), and including constraints related to volume fraction, first-order natural frequency, and stress. Utilizing TOSCA, the U-frame topology optimization design is finalized, resulting in an optimized conceptual model. A comparative analysis of the optimized design against the empirical design structure demonstrates a significant 27.53% reduction in maximum stress, enhanced static stiffness, an increase of 11.48% in the first natural frequency, and a 4.3% reduction in mass. Moreover, the turntable that utilizes the optimized design structure not only meets reliability requirements in spacecraft structural design but also provides a stable platform for precise optical load alignment. The static and dynamic performance of the optimized U-frame has been enhanced, effectively mitigating the impact of external excitation on optical instrument measurement accuracy and meeting the lightweight design requirements for the U-frame, thus demonstrating the efficacy of the topology optimization method. This research offers valuable insights into U-frame design for spaceborne optoelectronic tracking and pointing turntables and carries significant guiding implications.

Sugar substitutes, which generally refer to a class of food additives, mostly have vibration frequencies within the terahertz (THz) band. Therefore, THz technology can be used to analyze their molecular properties. To understand the characteristics of sugar substitutes, this study selected mannitol and erythritol as representatives. Firstly, PXRD and Raman techniques were used to determine the crystal structure and purity of mannitol and erythritol. Then, the THz time-domain spectroscopy (THz-TDS) system was employed to measure the spectral properties of the two sugar substitutes. Additionally, density functional theory (DFT) was utilized to simulate the crystal configurations of mannitol and erythritol. The experimental results showed good agreement with the simulation results. Finally, microfluidic chip technology was used to measure the THz spectroscopic properties of the two sugar substitutes in solution. A comparison was made between their solid state and aqueous solution state, revealing a strong correlation between the THz spectra of the two sugar substitutes in both states. Additionally, it was found that the THz spectrum of a substance in solution is related to its concentration. This study provides a reference for the analysis of sugar substitutes.

Surfactants play a crucial role in tertiary oil recovery by reducing the interfacial tension between immiscible phases, altering surface wettability, and improving foam film stability. Oil reservoirs have high temperatures and high pressures, making it difficult and hazardous to conduct lab experiments. In this context, molecular dynamics (MD) simulation is a valuable tool for complementing experiments. It can effectively study the microscopic behaviors (such as diffusion, adsorption, and aggregation) of the surfactant molecules in the pore fluids and predict the thermodynamics and kinetics of these systems with a high degree of accuracy. MD simulation also overcomes the limitations of traditional experiments, which often lack the necessary temporal–spatial resolution. Comparing simulated results with experimental data can provide a comprehensive explanation from a microscopic standpoint. This article reviews the state-of-the-art MD simulations of surfactant adsorption and resulting interfacial properties at gas/oil–water interfaces. Initially, the article discusses interfacial properties and methods for evaluating surfactant-formed monolayers, considering variations in interfacial concentration, molecular structure of the surfactants, and synergistic effect of surfactant mixtures. Then, it covers methods for characterizing microstructure at various interfaces and the evolution process of the monolayers’ packing state as a function of interfacial concentration and the surfactants’ molecular structure. Next, it examines the interactions between surfactants and the aqueous phase, focusing on headgroup solvation and counterion condensation. Finally, it analyzes the influence of hydrophobic phase molecular composition on interactions between surfactants and the hydrophobic phase. This review deepened our understanding of the micro-level mechanisms of oil displacement by surfactants and is beneficial for screening and designing surfactants for oil field applications.

Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model’s ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis-segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively.

Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of objects with rich morphological diversity. This paper proposes a novel method for salient object detection leveraging multi-visual perception, mirroring the human visual system’s rapid identification, and focusing on impressive objects/regions within complex scenes. First, a feature map is derived from the original image. Then, salient object detection results are obtained for each perception feature and combined via a feature fusion strategy to produce a saliency map. Finally, superpixel segmentation is employed for precise salient object extraction, removing interference areas. This multi-feature approach for salient object detection harnesses complementary features to adapt to complex scenarios. Competitive experiments on the MSRA10K and ECSSD datasets place our method in the first tier, achieving 0.1302 MAE and 0.9382 F-measure for the MSRA10K dataset and 0.0783 MAE and and 0.9635 F-measure for the ECSSD dataset, demonstrating superior salient object detection performance in complex natural scenarios.

Recycling economy and the re-utilization of solid waste have become important parts of sustainable development strategy. To improve the utilization rate of waste glass, glass powder high-strength cementitious material (GHSC) was prepared by replacing part of the cement in the cementitious material with ground waste glass powder. Firstly, the effect of glass powder particle size on the flexural and compressive strength of GHSC was investigated by the gray correlation method, and the optimal grinding time was obtained. Additionally, the effect of the magnitude of steam curing temperature and the length of steam curing time on the compressive strength and flexural strength of GHSC was investigated, and the mechanism of the effect of the curing regime on the strength was explored by examination of the microstructure. Finally, to simplify the curing process of GHSC, the effects of Ca(OH)2 and Na2SO4 as excitation agents on the compressive strength and flexural strength of GHSC at different dosing levels were compared. The results showed that glass powder with a particle size of less than 20 μm would improve the compressive strength and flexural strength of the specimen. Steam curing can significantly improve the flexural strength and compressive strength of GHSC specimens. At a steam curing temperature of 90 °C for a duration of three days, the compressive strength and flexural strength of GHSC increased by 76.7% and 98.2%, respectively, compared with the standard curing specimens. Ca(OH)2 and Na2SO4 as excitation agents significantly enhanced the compressive and flexural strengths of GHSC under standard curing conditions.

The Fukang Sag in the Junggar Basin is an important petroleum exploration and exploitation region. However, the geothermal regime and tectono-thermal evolution of the Fukang Sag, which control its hydrocarbon generation and conservation, are still controversial. This study involved a systematic analysis of the present-day geothermal gradient, heat flow, and thermal history of the Fukang Sag for better further exploration. According to the well log data and well-testing temperature data, we calculated that the geothermal gradient of the Fukang Sag ranges from 16.6 °C/km to 29.6 °C/km, with an average of 20.8 °C/km, and the heat flow ranges from 34.6 mWm−2 to 64.3 mWm−2, with an average of 44.6 mWm−2. Due to the basement relief, they decrease from northeast to southwest. The weight averages of the single-grain apatite (U-Th)/He ages of the core samples are 1.3–85.2 Ma, and their apatite fission track ages range from 50.9 Ma to 193.8 Ma. The thermal modeling results revealed that the Fukang Sag experienced late Permian, late Jurassic, and late Cretaceous cooling events (although the timing and magnitude of these events varied among the samples), which were related to the continuous compression of the Junggar Basin. In addition, basin modeling indicated that the heat flow of the Fukang Sag decreased from 80 mWm−2 in the Carboniferous to the current value of 44.6 mWm−2. The Fukang Sag’s edge exhibits prolific hydrocarbon generation in the Carboniferous–Permian source rocks, while the Jurassic source rocks within the sag also undergo abundant hydrocarbon generation. This study provides new insights into the present-day geothermal field and tectono-thermal evolutionary history of the Fukang Sag, which are significant in terms of regional tectonic evolution and oil and gas resource assessment.

Aiming at the problem of water burst plugging in karst pipelines, in this paper, a new type of waterborne epoxy resin magnetic self-polymerization grouting material (WEMS) suitable for karst pipeline water burst plugging was developed, and a visual simulation test device for karst pipeline water burst grouting plugging was designed and built. Through the orthogonal test of grouting plugging, the influence of different factors on the effect of grouting plugging was analyzed, and the shear mechanism of magnetic slurry magnetization plugging was also analyzed. The results showed that (i) the best grouting sealing performance was achieved when the new WEMS had a water–cement ratio of 0.35–0.55 and Fe3O4 powder misery of 20–40%. (ii) The primary relationship between the factors affecting the dynamic water flow rate and slurry retention is Fe3O4 power ratio > plugging length > water–cement ratio. (iii) The relationship equation was established between the shear strength of magnetic self-polymerizing slurry and the magnetic field strength, blocking length, and slurry ratio.

Background: This meta-analysis aimed to determine the efficacy and safety of antidiabetic agents in the treatment of major depressive disorder and bipolar depression. Methods: Randomized controlled trials (RCTs) of antidiabetic agents in major depressive disorder or bipolar depression were searched in three electronic databases and three clinical trial registry websites from their inception up to October 2023. The differences in changes in the depression rating scale scores from baseline to endpoint or pre-defined sessions, response rate, remission rate, rate of side effects and dropout rate between antidiabetic agents and placebo were meta-analyzed. Results: Six RCTs involving 399 participants were included in the final meta-analysis, which did not find that antidiabetics outperformed the placebo in reducing depressive symptoms. The standardized mean difference (SMD) in the depression scores from baseline to endpoint was 0.25 (95% CI −0.1, 0.61). However, a subgroup analysis found a significant difference between antidiabetics and placebos in reducing depressive symptoms in Middle Eastern populations, with an SMD of 0.89 (95% CI 0.44, 1.34). Conclusions: The current meta-analysis does not support the efficacy of antidiabetics being superior to the placebo in the treatment of unipolar and bipolar depression. However, a subgroup analysis indicates that patients from the Middle East may benefit from adding an antidiabetic medication to their ongoing medication(s) for their depression. Larger studies with good-quality study designs are warranted.

Cage aquaculture makes it easier to produce high-quality aquatic products and allows full use of water resources. 3Therefore, cage aquaculture development is highly valued globally. However, the current digitalization level of cage aquaculture is low, and the farming risks are high. Research and development of digital management of the fish population in cages are greatly desired. Real-time monitoring of the activity status of the fish population and changes in the fish population size in cages is a pressing issue that needs to be addressed. This paper proposes an improved network called CC-YOLOv5 by embedding CoordConv modules to replace the original ConV convolution modules in the network, which improves the model’s generalization capability. By using two-stage detection logic, the target detection accuracy is enhanced to realize prediction of the number of fish populations. OpenCV is then used to measure fish tail lengths to establish growth curves of the fish and to predict the output of the fish population in the cages. Experimental results demonstrate that the mean average precision (mAP) of the improved algorithm increases by 14.9% compared to the original YOLOv5, reaching 95.4%. This research provides an effective solution to promote the intelligentization of cage aquaculture processes. It also lays the foundation for AI (Artificial Intelligence) applications in other aquaculture scenarios.

In this research, fully degradable elastomeric sealing materials were developed to enhance the environmental sustainability of oil and gas extraction. The modification of millable polyurethane rubber (MPU) with polyglycolic acid/hydrogenated nitrile butadiene rubber (PGA/HNBR) led to the synthesis of PGA@MPU/HNBR composite materials. The impact of varying monomer quantities on the mechanical properties, degradation behavior, degradation mechanisms, and thermal stability of these materials was investigated. Our findings illustrate that an increasing proportion of HNBR in the PGA@MPU/HNBR composite materials resulted in a decreased degradation rate. Simultaneously, higher HNBR content improved the thermal stability of the materials, while the inclusion of PGA reduced material hardness, rendering the composites more susceptible to swelling. At an HNBR content of 40 phr, MPU at 60 phr, and PGA at 6 phr, the composite material demonstrated the highest retention of mechanical properties at 31.3% following 168 h of hydrolysis at 100 °C. The degradation of the composite materials in 100 °C water primarily resulted from the hydrolysis of MPU’s ester groups, with HNBR remaining unaffected.

With the increasing development of low-permeability reservoirs, the significance of conformance control treatment has risen considerably. To address the conflict between injectability and plugging performance, as well as to enhance the deep migration capacity of conformance control agents, preformed submicron crosslinked polymer coils (SCPCs) have been manufactured using aqueous solution dispersion polymerization. Fourier transform infrared and scanning electron microscopy were employed to examine the chemical structure and micromorphology, while particle size distribution, zeta potential, rheological, and filtration properties were analyzed. The effectiveness of conformance control was confirmed through the parallel core displacement. The effective particle size of SCPCs was at a submicron level (500~800 nm). SCPCs exhibit a transitional threshold concentration between gel and sol states (0.25 wt%~0.5 wt%). SCPCs can efficiently block the 1.2 μm microporous filter membrane. The filtration time is up to 67.8 min. SCPCs can improve the water absorption rate of lower permeability cores from 21.21% to 57.89% with a permeability difference of 5. Therefore, SCPCs have good injectability, plugging performance, and deep migration capacity and can be used for conformance control in low-permeability reservoirs.

A fresh censored δ shock model is investigated. The arrival of random shocks follows a generalized Pólya process, and the failure mechanism of the system occurs based on the censored δ shock model. The generalized Pólya process is used for modeling because the generalized Pólya process has excellent properties, including the homogeneous Poisson process, the non-homogeneous Poisson process, and the Pólya process. Thus far, the lifetime properties of the censored δ shock model under the generalized Pólya process have not been studied. Therefore, for the established generalized Pólya censored δ shock model, the corresponding reliability function, the upper bound of the reliability function, the mean lifetime, the failure rate, and the class of life distribution are obtained. In addition, a replacement strategy N, based on the number of failures of the system, is considered using a geometric process. We determined the optimal replacement policy N* by objective function minimization. Finally, a numerical example is presented to verify the rationality of the model.

Global navigation satellite systems (GNSSs) are an integral part of global positioning. However, because GNSS performance is impacted by signal obscuration and the presence of multipath in urban and deep urban environments, it is not accurate, reliable, and widely available enough to be a standalone system in all environments. This creates two problems: (1) the GNSS user does not know when or where GNSS performance may be degraded and (2) the GNSS user has limited ability to mitigate these issues. No mitigation strategy exists to improve the availability of GNSSs themselves. Inertial measurement units (IMUs) and sensor fusion provide other costly methods to improve positioning performance, but most systems still rely on GNSSs for absolute position. Spirent’s GNSS Foresight service aims to solve both issues. As a cloud-based solution, GNSS Foresight provides satellite and signal information, and this can be employed to support the decision-making strategy and calculations in the GNSS receiver to improve its positioning solution performance, integrity, and reliability. In this paper, GNSS Foresight is introduced, and a performance evaluation of GNSS Foresight in dense urban areas is presented. Using the data collected from two urban areas in North America, we evaluated GNSS Foresight and compared the performance of GNSS positioning solutions with and without Foresight-aided data. The comparison results show the observed improvements in GNSS receiver operation. Foresight can also be used to develop measurement engine performance enhancements in the acquisition of new satellites and the tracking/re-acquisition of current satellites using line-of-sight (LOS) satellite information. In the positional computation process, Foresight enables receivers to prioritize LOS signals over degraded non-line-of-sight (NLOS) signals, hence significantly reducing positioning errors and outperforming conventional GNSS positioning, particularly in difficult urban environments.

The acquisition of high-accuracy reflectance in mid-infrared channels is of great significance for the on-orbit cross-calibration of other bands using the mid-infrared band. However, due to the phenomenon that some sensors have a wide range of wavelengths covered by adjacent channels in the mid-infrared band, the traditional method of estimating the mid-infrared reflectivity assumes that the sea surface reflectivity in different mid-infrared bands is equal, which will lead to a large error during calculation. To solve this problem, this study proposes a nonlinear split-window algorithm involving ocean sun glint data to retrieve reflectivity of FY-3D/MERSI-2 channels 20. The results show that the variation range of sea surface reflectivity of channel 20 in the glint area is 10~25%, the mean value of the reflectivity difference obtained by the nonlinear split-window algorithm is 0.27%, and the RMSE is 0.0066. Among the main influencing factors, the atmospheric conditions have the greatest impact, and the effects of the uncertainties in the water vapor content and aerosol optical thickness on the calculation results are 1.16% and 0.34%, respectively. The initial value limits of the mid-infrared sea surface reflectivity also contribute approximately 0.84%, and their contribution to the uncertainty represents one of the main components. This work shows that the nonlinear split-window algorithm can calculate the infrared sea surface reflectivity with high accuracy and can be used as a reference for in-orbit cross-calibration between different bands.

Four new sesterterpenes, arthproliferins A–D (1–4), together with four known derivatives, were isolated and characterized from the mangrove-sediment-derived fungus Arthrinium sp. SCSIO41221. Their structures were determined using detailed nuclear magnetic resonance (NMR) and mass spectroscopic (MS) analyses. Some of the isolated compounds were evaluated for their cytotoxicity in vitro. The results revealed that terpestacin (6) exhibited significant activity with an IC50 value of 20.3 μM, and compounds 2 and 5 were found to show weak inhibitory effects against U87MG-derived GSCs.

Recent studies have found that many marine microbial polysaccharides exhibit distinct immune activity. However, there is a relative scarcity of research on the immunomodulatory activity of marine fungal exopolysaccharides. A novel water-soluble fungal exopolysaccharide ASP-1 was isolated from the fermentation broths of marine coral-associated fungus Aspergillus pseudoglaucus SCAU265, and purified by Diethylaminoethyl-Sepharose-52 (DEAE-52) Fast Flow and Sephadex G-75. Structural analysis revealed that ASP-1 had an average molecular weight of 36.07 kDa and was mainly composed of (1→4)-linked α-D-glucopyranosyl residues, along with highly branched heteropolysaccharide regions containing 1,4,6-glucopyranosyl, 1,3,4-glucopyranosyl, 1,4,6-galactopyranosyl, T(terminal)-glucopyranosyl, T-mannopyranosyl, and T-galactopyranosyl residues. ASP-1 demonstrated significant effects on the proliferation, nitric oxide levels, and the secretion of cytokines TNF-α and IL-6 in macrophage RAW264.7 cells. Metabolomic analysis provided insights into the potential mechanisms of the immune regulation of ASP-1, suggesting its involvement in regulating immune function by modulating amino acid anabolism, particularly arginine synthesis and metabolism. These findings provide fundamental scientific data for further research on its accurate molecular mechanism of immunomodulatory activity.

It is evident that zinc supplementation is essential for maintaining good health and preventing disease. In this study, a novel oyster peptide–zinc complex with an average molecular weight of 500 Da was prepared from oyster meat and purified using ultrafiltration, ultrasound, a programmed cooling procedure, chelating, and dialysis. The optimal chelating process parameters obtained through a response surface methodology optimization design are a peptide/zinc ratio of 15, pH of 6.53, reaction time of 80 min, and peptide concentration of 0.06 g/mL. Then, the structure of a peptide–zinc complex (named COP2-Zn) was investigated using the UV and infrared spectrums. The results showed that the maximum absorption peak was redshifted from 224.5 nm to 228.3 nm and the main difference of the absorption peaks was 1396.4 cm−1. The cytotoxicity and antiproliferative effects of COP2-Zn were evaluated. The results showed that COP2-Zn had a better antiproliferative effect than the unchelated peptide against HepG2 cells. A DNA flow cytometric analysis showed that COP2-Zn induced S-phase arrest in HepG2 cells in a dose-dependent manner. Additionally, the flow cytometer indicated that COP2-Zn significantly induced HepG2 cell apoptosis.

The dysregulated expression of cyclin genes can lead to the uncontrolled proliferation of cancer cells. Histone demethylase Jumonji-C domain-containing protein 5 (KDM8, JMJD5) and cyclin A1 (CCNA1) are pivotal in cell cycle progression. A promising candidate for augmenting cancer treatment is Allyl isothiocyanate (AITC), a natural dietary chemotherapeutic and epigenetic modulator. This study aimed to investigate AITC’s impact on the KDM8/CCNA1 axis to elucidate its role in oral squamous cell carcinoma (OSCC) tumorigenesis. The expression of KDM8 and CCNA1 was assessed using a tissue microarray (TMA) immunohistochemistry (IHC) assay. In vitro experiments with OSCC cell lines and in vivo experiments with patient-derived tumor xenograft (PDTX) and SAS subcutaneous xenograft tumor models were conducted to explore AITC’s effects on their expression and cell proliferation. The results showed elevated KDM8 and CCNA1 levels in the OSCC patient samples. AITC exhibited inhibitory effects on OSCC tumor growth in vitro and in vivo. Additionally, AITC downregulated KDM8 and CCNA1 expression while inducing histone H3K36me2 expression in oral cancer cells. These findings underscore AITC’s remarkable anticancer properties against oral cancer, highlighting its potential as a therapeutic option for oral cancer treatment by disrupting the cell cycle by targeting the KDM8/CCNA1 axis.

Percutaneous interventions are gaining rapid acceptance in cardiology and revolutionizing the treatment of structural heart disease (SHD). As new percutaneous procedures of SHD are being developed, their associated complexity and anatomical variability demand a high-resolution special understanding for intraprocedural image guidance. During the last decade, three-dimensional (3D) transesophageal echocardiography (TEE) has become one of the most accessed imaging methods for structural interventions. Although 3D-TEE can assess cardiac structures and functions in real-time, its limitations (e.g., limited field of view, image quality at a large depth, etc.) must be addressed for its universal adaptation, as well as to improve the quality of its imaging and interventions. This review aims to present the role of TEE in the intraprocedural guidance of percutaneous structural interventions. We also focus on the current and future developments required in a multimodal image integration process when using TEE to enhance the management of congenital and SHD treatments.

Overcoming the blood–brain barrier (BBB) remains a significant challenge with regard to drug delivery to the brain. By incorporating targeting ligands, and by carefully adjusting particle sizes, nanocarriers can be customized to improve drug delivery. Among these targeting ligands, transferrin stands out due to the high expression level of its receptor (i.e., transferrin receptor) on the BBB. Porous silicon nanoparticles (pSiNPs) are a promising drug nanocarrier to the brain due to their biodegradability, biocompatibility, and exceptional drug-loading capacity. However, an in-depth understanding of the optimal nanoparticle size and transferrin surface density, in order to maximize BBB penetration, is still lacking. To address this gap, a diverse library of pSiNPs was synthesized using bifunctional poly(ethylene glycol) linkers with methoxy or/and carboxyl terminal groups. These variations allowed us to explore different transferrin surface densities in addition to particle sizes. The effects of these parameters on the cellular association, uptake, and transcytosis in immortalized human brain microvascular endothelial cells (hCMEC/D3) were investigated using multiple in vitro systems of increasing degrees of complexity. These systems included the following: a 2D cell culture, a static Transwell model, and a dynamic BBB-on-a-chip model. Our results revealed the significant impact of both the ligand surface density and size of pSiNPs on their ability to penetrate the BBB, wherein intermediate-level transferrin densities and smaller pSiNPs exhibited the highest BBB transportation efficiency in vitro. Moreover, notable discrepancies emerged between the tested in vitro assays, further emphasizing the necessity of using more physiologically relevant assays, such as a microfluidic BBB-on-a-chip model, for nanocarrier testing and evaluation.

Hf0.5Zr0.5O2-based multi-level cell (MLC) ferroelectric random-access memory (FeRAM) has great potential for high-density storage applications. However, it is usually limited by the issues of a small operation margin and a large input offset. The study of circuit design and optimization for MLC FeRAM is necessary to solve these problems. In this work, we propose and simulate a configuration for a Hf0.5Zr0.5O2-based 3TnC MLC FeRAM macro circuit, which also presents a high area efficiency of 12F2 for each bit. Eight polarization states can be distinguished in a single fabricated Hf0.5Zr0.5O2-based memory device for potential MLC application, which is also simulated by a SPICE model for the subsequent circuit design. Therein, a nondestructive readout approach is adopted to expand the reading margin to 450 mV between adjacent storage levels, while a capacitorless offset-canceled sense amplifier (SA) is designed to reduce the offset voltage to 20 mV, which improves the readout reliability of multi-level states. Finally, a 4 Mb MLC FeRAM macro is simulated and verified using a GSMC 130 nm CMOS process. This study provides the foundation of circuit design for the practical fabrication of a Hf0.5Zr0.5O2-based MLC FeRAM chip in the future, which also suggests its potential for high-density storage applications.

Celery seeds have been used as an effective dietary supplement to manage hyperuricemia and diminish gout recurrence. Xanthine oxidase (XOD), the critical enzyme responsible for uric acid production, represents the most promising target for anti-hyperuricemia in clinical practice. In this study, we aimed to establish a method based on affinity ultrafiltration–liquid chromatography–mass spectrometry (UF–LC–MS) to directly and rapidly identify the bioactive compounds contributing to the XOD-inhibitory effects of celery seed crude extracts. Chemical profiling of celery seed extracts was performed using UPLC-TOF/MS. The structure was elucidated by matching the multistage fragment ion data to the database and publications of high-resolution natural product mass spectrometry. Thirty-two compounds, including fourteen flavonoids and six phenylpeptides, were identified from celery seed extracts. UF–LC–MS showed that luteolin-7-O-apinosyl glucoside, luteolin-7-O-glucoside, luteolin-7-O-malonyl apinoside, luteolin-7-O-6′-malonyl glucoside, luteolin, apigenin, and chrysoeriol were potential binding compounds of XOD. A further enzyme activity assay demonstrated that celery seed extract (IC50 = 1.98 mg/mL), luteolin-7-O-apinosyl glucoside (IC50 = 3140.51 μmol/L), luteolin-7-O-glucoside (IC50 = 975.83 μmol/L), luteolin-7-O-6′-malonyl glucoside (IC50 = 2018.37 μmol/L), luteolin (IC50 = 69.23 μmol/L), apigenin (IC50 = 92.56 μmol/L), and chrysoeriol (IC50 = 40.52 μmol/L) could dose-dependently inhibit XOD activities. This study highlighted UF–LC–MS as a useful platform for screening novel XOD inhibitors and revealed the chemical basis of celery seed as an anti-gout dietary supplement.

Microfluidic technology is applied across various research areas including organ-on-chip (OOC) systems. The main material used for microfluidics is polydimethylsiloxane (PDMS), a silicone elastomer material that is biocompatible, transparent, and easy to use for OOC systems with well-defined microstructures. However, PDMS-based OOC systems can absorb hydrophobic and small molecules, making it difficult and erroneous to make quantitative analytical assessments for such compounds. In this paper, we explore the use of a synthetic fluoropolymer, poly(4,5-difluoro-2,2-bis(trifluoromethyl)-1,3-dioxole-co-tetrafluoroethylene) (Teflon™ AF 2400), with excellent “non-stick” properties to functionalize OOC systems. Cannabinoids, including cannabidiol (CBD), are classes of hydrophobic compounds with a great potential for the treatment of anxiety, depression, pain, and cancer. By using CBD as a testing compound, we examined and systematically quantified CBD absorption into PDMS by means of an LC-MS/MS analysis. In comparison to the unmodified PDMS microchannels, an increase of approximately 30× in the CBD signal was detected with the fluoropolymer surface modification after 3 h of static incubation. Under perfusion conditions, we observed an increase of nearly 15× in the CBD signals from the surface-modified microchannels than from the unmodified microchannels. Furthermore, we also demonstrated that fluoropolymer-modified microchannels are compatible for culturing hCMEC/D3 endothelial cells and for CBD perfusion experiments.

A new off-axis optical design alternative to that of the GRACE Follow-on mission for future NGGM missions is considered. In place of the triple-mirror assembly of the GRACE Follow-on mission, a laser retro-reflector is instead generated by means of lens systems. The receiving (RX) beam and transmitting (TX) beam are enforced to be anti-parallel by a control loop with differential wavefront sensing (DWS) signals as readout, and a fast-steering mirror is employed to actuate the pointing of the local beam. The tilt-to-length (TTL) coupling noise of the new off-axis optical bench layout is carefully studied in the present work. Local TTL originated from piston noise as well as assembly and alignment errors of optical components are studied. Effort is also made to have an in depth understanding of global TTL due to relative attitude jitter between spacecraft. The margin of TTL noise in the position noise budget for laser ranging is examined. With an open loop control of the offset between the reference point of the optical bench and the centre of mass of a satellite, the TTL noise of the new off-axis optical bench design may be suppressed efficiently.

The signal-receiving end of acquisition, pointing, and tracking (APT) systems applied to intersatellite laser communication terminals usually uses a fast-steering mirror (FSM) to control the fiber-coupling process, has a complex structural design, and induces large errors in the nonideal coaxial optical path. Herein, we propose a fiber-optic nutator using a piezoelectric ceramic tube (PCT) as the driving unit that allows scanning in the focal plane of the light signal to achieve active fiber coupling in the APT system. Specifically, this article describes the structural design principle of a PCT-based fiber optic nutator, establishes a simulation model of the mechanism, and proves the correctness of the simulation model by measuring the deflection angle of a PCT based on a parallel light collimator. The minimum accuracy of the designed nutator was 0.145 μm, the maximum nutation radius R was 20.09 μm, and the maximum nutation bandwidth was 20 kHz, as determined through simulation. Finally, the design parameters of the nutator were evaluated. The PCT-based fiber optic nutator, which met the design parameters, structurally replaced the fiber optic coupling component FSM and fine tracking camera in conventional APT systems successfully. Therefore, the PCT-based fiber optic nutator allows the active coupling control of signal light to a single-mode fiber (SMF) based on energy feedback on a theoretical basis and promotes the lightweight design of relay optical paths in APT systems. In addition, with future work in optimization of the nutation control algorithm, the scanning range and accuracy of the nutator can be improved.

Combining bioinformatics and in vitro cytology assays, a predictive method was established to quickly evaluate the protective effect of immunity acquired through SARS-CoV-2 infection against variants. Bioinformatics software was first used to predict the changes in the affinity of variant antigens to the CV30 monoclonal antibody by integrating bioinformatics and cytology assays. Then, the ability of the antibody to neutralize the variant antigen was further verified, and the ability of the CV30 to neutralize the new variant strain was predicted through pseudovirus neutralization experiments. The current study has demonstrated that when the Molecular Operating Environment (MOE) predicts |ΔBFE| ≤ 3.0003, it suggests that the CV30 monoclonal antibody exhibits some affinity toward the variant strain and can potentially neutralize it. However, if |ΔBFE| ≥ 4.1539, the CV30 monoclonal antibody does not display any affinity for the variant strain and cannot neutralize it. In contrast, if 3.0003 < |ΔBFE| < 4.1539, it is necessary to conduct a series of neutralization tests promptly with the CV30 monoclonal antibody and the variant pseudovirus to obtain results and supplement the existing method, which is faster than the typical procedures. This approach allows for a rapid assessment of the protective efficacy of natural immunity gained through SARS-CoV-2 infection against variants.

Using ultrasound imaging to diagnose liver steatosis is of great significance for preventing diseases such as cirrhosis and liver cancer. Accurate diagnosis under conditions of low quality, noise and poor resolutions is still a challenging task. Physiological studies have shown that the visual cortex of the biological visual system has selective attention neural mechanisms and feedback regulation of high features to low features. When processing visual information, these cortical regions selectively focus on more sensitive information and ignore unimportant details, which can effectively extract important features from visual information. Inspired by this, we propose a new diagnostic network for hepatic steatosis. In order to simulate the selection mechanism and feedback regulation of the visual cortex in the ventral pathway, it consists of a receptive field feature extraction module, parallel attention module and feedback connection. The receptive field feature extraction module corresponds to the inhibition of the non-classical receptive field of V1 neurons on the classical receptive field. It processes the input image to suppress the unimportant background texture. Two types of attention are adopted in the parallel attention module to process the same visual information and extract different important features for fusion, which improves the overall performance of the model. In addition, we construct a new dataset of fatty liver ultrasound images and validate the proposed model on this dataset. The experimental results show that the network has good performance in terms of sensitivity, specificity and accuracy for the diagnosis of fatty liver disease.

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms.

With the steady improvement of advanced manufacturing processes and big data technologies, modern industrial systems have become large-scale. To enhance the sensitivity of fault detection (FD) and overcome the drawbacks of the centralized FD framework in dynamic systems, a new data-driven FD method based on Hellinger distance and subspace techniques is proposed for dynamic systems. Specifically, the proposed approach uses only system input/output data collected via sensor networks, and the distributed residual signals can be generated directly through the stable kernel representation of the process. Based on this, each sensor node can obtain the identical residual signal and test statistic through the average consensus algorithms. In addition, this paper integrates the Hellinger distance into the residual signal analysis for improving the FD performance. Finally, the effectiveness and accuracy of the proposed method have been verified in a real multiphase flow facility.

Resistance random access memory (RRAM) based true random number generator (TRNG) has great potential to be applied to hardware security owing to its intrinsic switching variability. Especially the high resistance state (HRS) variation is usually taken as the entropy source of RRAM-based TRNG. However, the small HRS variation of RRAM may be introduced owing to fabrication process fluctuations, which may lead to error bits and be vulnerable to noise interference. In this work, we propose an RRAM-based TRNG with a 2T1R architecture scheme, which can effectively distinguish the resistance values of HRS with an accuracy of 1.5 kΩ. As a result, the error bits can be corrected to a certain extent while the noise is suppressed. Finally, a 2T1R RRAM-based TRNG macro is simulated and verified using the 28 nm CMOS process, which suggests its potential for hardware security applications.

Maintaining a supply–demand balance of ecosystem services (ES) is essential for enhancing the effectiveness of ecosystem restoration. However, inappropriate land use and reforestation practices can negatively impact this balance. In this study, the ES balance of the Saihanba region in China was quantified by integrating land use/cover change (LUCC) data, landscape metrics, and ES indicators. The relationship between ES balance and its driving factors was analyzed using spatial panel models. The spatiotemporal changes of landscape patterns from 2002 to 2020 were also explored. The results indicated that the overall ES supply capacity of the study region, especially in the southwestern area, increased during the research period. The ES balance and its determining factors exhibited significant spatial heterogeneity and spillover effects. Large–scale afforestation increased the local ES supply and provided economic benefits, but it also led to ecological issues, including declines in wetland area and landscape fragmentation. Our study emphasized the importance of considering the supply–demand balance in the planning and decision–making of ES, providing insight into multifunctional management and the sustainable development in the Saihanba area.

To discover bioactive natural products from mangrove sediment-derived microbes, a chemical investigation of the two Beibu Gulf-derived fungi strains, Talaromyces sp. SCSIO 41050 and Penicillium sp. SCSIO 41411, led to the isolation of 23 natural products. Five of them were identified as new ones, including two polyketide derivatives with unusual acid anhydride moieties named cordyanhydride A ethyl ester (1) and maleicanhydridane (4), and three hydroxyphenylacetic acid derivatives named stachylines H–J (10–12). Their structures were determined by detailed nuclear magnetic resonance (NMR) and mass spectroscopic (MS) analyses, while the absolute configurations were established by theoretical electronic circular dichroism (ECD) calculation. A variety of bioactive screens revealed three polyketide derivatives (1–3) with obvious antifungal activities, and 4 displayed moderate cytotoxicity against cell lines A549 and WPMY-1. Compounds 1 and 6 at 10 μM exhibited obvious inhibition against phosphodiesterase 4 (PDE4) with inhibitory ratios of 49.7% and 39.6%, respectively, while 5, 10, and 11 showed the potential of inhibiting acetylcholinesterase (AChE) by an enzyme activity test, as well as in silico docking analysis.

Since introducing the Transformer model, it has dramatically influenced various fields of machine learning. The field of time series prediction has also been significantly impacted, where Transformer family models have flourished, and many variants have been differentiated. These Transformer models mainly use attention mechanisms to implement feature extraction and multi-head attention mechanisms to enhance the strength of feature extraction. However, multi-head attention is essentially a simple superposition of the same attention, so they do not guarantee that the model can capture different features. Conversely, multi-head attention mechanisms may lead to much information redundancy and computational resource waste. In order to ensure that the Transformer can capture information from multiple perspectives and increase the diversity of its captured features, this paper proposes a hierarchical attention mechanism, for the first time, to improve the shortcomings of insufficient information diversity captured by the traditional multi-head attention mechanisms and the lack of information interaction among the heads. Additionally, global feature aggregation using graph networks is used to mitigate inductive bias. Finally, we conducted experiments on four benchmark datasets, and the experimental results show that the proposed model can outperform the baseline model in several metrics.

Long-life pavement construction is an important research direction for sustainable road development. Fatigue cracking of aging asphalt pavement is one of the main reasons that affects its service life, and improving the fatigue resistance of aging asphalt pavement has become a key factor in promoting the development of long-life pavement. In order to enhance the fatigue resistance of aging asphalt pavement, hydrated lime and basalt fiber were selected to prepare a modified asphalt mixture. The resistance to fatigue is evaluated by the four-point bending fatigue test and self-healing compensation test, based on the energy method, the phenomenon-based approach, and other methods. The results of each method of evaluation were also compared and analyzed. The results indicate that the incorporation of hydrated lime can improve the adhesion of the asphalt binder, while the incorporation of basalt fiber can stabilize the internal structure. When incorporated alone, basalt fiber has no noticeable effect, while hydrated lime significantly improves the fatigue performance of the mixture after thermal aging. Mixing both ingredients produced the best improvement effect under various conditions, with a fatigue life improvement of 53%. In the multi-scale evaluation of fatigue performance, it was found that the initial stiffness modulus was unsuitable as a direct evaluation index of fatigue performance. Using the fatigue damage rate or the stable value of dissipated energy change rate as an evaluation index can clearly characterize the fatigue performance of the mixture before and after aging. The self-healing rate and self-healing decay index clearly reflected the fatigue damage healing process under repeated loading and could be used as relevant indices for evaluating the new-scale fatigue performance of asphalt mixtures.

The drag and lift force of a twin-cylinder structure are often greater than those of a single cylinder, causing serious structural safety problems. However, there are few studies on the passive control of twin cylinders. The study aimed to investigate the performance of passive drag reduction measures using small cylinders on twin cylinders at a Reynolds number of 100. The effects of small cylinder height (HD/D = 0~1.0, D is the side length of the twin cylinder) and cross-sectional shape on fluid force and flow structures were studied by direct numerical simulations. The control mechanism was analyzed using high-order dynamic mode decomposition (HODMD). The results showed that significant drag reduction occurred in the co-shedding state, particularly when the gap length of the twin cylinders L/D = 6.0. The small control cylinders with HD = 0.6, by contrast, showed the best performance in reducing the mean drag and fluctuating lift of the twin cylinders. It reduced the mean drag of the upstream cylinder (UC) by 2.58% and the downstream cylinder (DC) by more than 62.97%. The fluctuating lift coefficient for UC (DC) was also decreased by more than 70.41% (59.74%). The flow structures showed that when the flow hit UC under the action of small control cylinders, a virtual missile-like aerodynamic shape was formed at the leading edge of UC. In this way, the gap vortex consisted of two asymmetric steady vortices and the vortex length significantly increased. This was also confirmed by HODMD. The coherence modes in the gap were suppressed and thus the interaction between gap flow and wake flow was mitigated, which resulted in the fluid force reduction.

Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and fail to accurately control thermal comfort in indoor climates. Traditional comfort models also lack the ability to adapt to individual demands and sensations. This research developed a data-driven thermal comfort model to improve the overall thermal comfort of occupants in office buildings. An architecture based on cyber-physical system (CPS) is used to achieve these goals. A building simulation model is built to simulate multiple occupants’ behaviors in an open-space office building. Results suggest that a hybrid model can accurately predict occupants’ thermal comfort level with reasonable computing time. In addition, this model can improve occupants’ thermal comfort by 43.41% to 69.93%, while energy consumption remains the same or is slightly reduced (1.01% to 3.63%). This strategy can potentially be implemented in real-world building automation systems with appropriate sensor placement in modern buildings.

To address the issue of blurred images generated during ice wind tunnel tests, we propose a high-resolution dense-connection GAN model, named Dense-HR-GAN. This issue is caused by attenuation due to scattering and absorption when light passes through cloud and fog droplets. Dense-HR-GAN is specifically designed for this environment. The model utilizes an atmospheric scattering model to dehaze images with a dense network structure for training. First, sub-pixel convolution is added to the network structure to remove image artifacts and generate high-resolution images. Secondly, we introduce instance normalization to eliminate the influence of batch size on the model and improve its generalization performance. Finally, PatchGAN is used in the discriminator to capture image details and local information, and then drive the generator to generate a clear and high-resolution dehazed image. Moreover, the model is jointly constrained by multiple loss functions during training to restore the texture information of the hazy image and reduce color distortion. Experimental results show that the proposed method can achieve the state-of-the-art performance on image dehazing the in icing wind tunnel environment.

Pickering emulsions stabilized by food-grade colloidal particles have attracted increasing attention in recent years due to their “surfactant-free” nature. In this study, the alkali-treated zein (AZ) was prepared via restricted alkali deamidation and then combined with sodium alginate (SA) in different ratios to obtain AZ/SA composite particles (ZS), which were used to stabilize Pickering emulsion. The degree of deamidation (DD) and degree of hydrolysis (DH) of AZ were 12.74% and 6.58% respectively, indicating the deamidation occurred mainly in glutamine on the side chain of the protein. After the treatment with alkali, AZ particle size decreased significantly. Moreover, the particle size of ZS with different ratios was all less than 80 nm. when the AZ/SA ratio was 2:1(Z2S1) and 3:1(Z3S1), the three-phase contact angle (θo/w) were close to 90°, which was favorable for stabilizing the Pickering emulsion. Furthermore, at a high oil phase fraction (75%), Z3S1-stabilized Pickering emulsions showed the best long-term storage stability within 60 days. Confocal laser scanning microscope (CLSM) observations showed that the water-oil interface was wrapped by a dense layer of Z3S1 particles with non-agglomeration between independent oil droplets. At constant particle concentration, the apparent viscosity of the Pickering emulsions stabilized by Z3S1 gradually decreased with increasing oil phase fraction, and the oil-droplet size and the Turbiscan stability index (TSI) also gradually decreased, exhibiting solid-like behavior. This study provides new ideas for the fabrication of food-grade Pickering emulsions and will extend the future applications of zein-based Pickering emulsions as bioactive ingredient delivery systems.

To investigate traffic accident patterns in undersea tunnels and quantify the relationship among various factors and traffic accidents in undersea tunnels, we analyzed the rules of evolution during the entire process of vehicle congestion queuing. Additionally, we built a maximum queue-length estimation model based on shock-wave theory and a whole-process queue-length estimation model based on real-time data input. The results demonstrate that the model is most precise when the data are not smoothed and the time interval is 30 s. The maximum accuracy of the model is not improved by data smoothing processing, but it is substantially improved when the time interval is between 5 and 15 s. Various movable window lengths have no discernible effect on the results. Maximum queue-length estimation model accuracy is 92.34%, while real-time whole-process queue-length estimation model accuracy is 83.50%. The accuracy of the proposed model is greater than that of the input–output model, indicating that the proposed model can support timely and reasonable control measures.

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve’s future overtemperature state, which is characterized by the valve’s cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer–FCM–NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically.

This study aims to explore the relationship between the doses of inactivated COVID-19 vaccines received and SARS-CoV-2 Omicron infection in the real-world setting, so as to preliminarily evaluate the protective effect induced by COVID-19 vaccination. We conducted a test-negative case-control study and recruited the test-positive cases and test-negative controls in the outbreak caused by Omicron BA.2 in April 2022 in Guangzhou, China. All the participants were 3 years and older. The vaccination status between the case group and the control group was compared in the vaccinated and all participants, respectively, to estimate the immune protection of inactivated COVID-19 vaccines. After adjusting for sex and age, compared with a mere single dose, full vaccination of inactivated COVID-19 vaccines (OR = 0.191, 95% CI: 0.050 to 0.727) and booster vaccination (OR = 0.091, 95% CI: 0.011 to 0.727) had a more superior protective effect. Compared with one dose, the second dose was more effective in males (OR = 0.090), as well as two doses (OR = 0.089) and three doses (OR = 0.090) among individuals aged 18–59. Whereas, when compared with the unvaccinated, one dose (OR = 7.715, 95% CI: 1.904 to 31.254) and three doses (OR = 2.055, 95% CI: 1.162 to 3.635) could contribute to the increased risk of Omicron infection after adjusting for sex and age. Meanwhile, by contrast with unvaccinated individuals, the result of increased risk was also manifested in the first dose in males (OR = 12.400) and one dose (OR = 21.500), two doses (OR = 1.890), and a booster dose (OR = 1.945) in people aged 18–59. In conclusion, the protective effect of full and booster vaccination with inactivated COVID-19 vaccines exceeded the incomplete vaccination, of which three doses were more effective. Nevertheless, vaccination may increase the risk of Omicron infection compared with unvaccinated people. This may result from the transmission traits of BA.2, the particularity and stronger protection awareness of the unvaccinated population, as well as the ADE effect induced by the decrease of antibody titers after a long time of vaccination. It is crucial to explore this issue in depth for the formulation of future COVID-19 vaccination strategies.