Search

Exopolysaccharides (EPSs) produced by microorganisms play an important role in biotolerance and reducing heavy metal (HM) contamination by limiting the migration of HMs into plants. However, research on the application of EPS-producing marine bacteria for soil heavy metal remediation remains limited, particularly regarding their mechanisms of HM immobilization in soil and impact on plant growth. In this study, the EPS-producing marine bacterium Micrococcus antarcticus HZ was investigated for its ability to immobilize Pb and produce EPSs in soil filtrate. The effects on the growth quality and biomass of pakchoi (Brassica chinensis L.), as well as bacterial communities in inter-root soil contaminated with Pb, were also investigated. The results indicated that HZ could reduce the Pb concentration in the soil filtrate, achieving a removal rate of 43.25–63.5%. The EPS content and pH levels increased in the presence of Pb. Pot experiments showed that adding HZ significantly increased the biomass of pakchoi (9.45–14.69%), vitamin C (Vc) (9.69–12.92%), and soluble protein content (22.58–49.7%). HZ reduced the Pb content in the roots (17.52–47.48%) and leaves (edible tissues) (43.82–52.83%) of pakchoi. HZ increased soil enzyme activities (alkaline phosphatase, dehydrogenase, and urease), and the contents of ammonium nitrogen and nitrate nitrogen. Additionally, HZ also increased the relative abundance of beneficial bacteria (e.g., Proteobacteria, Cyanobacteria, and Chlorobacteria) in the inter-root soil, which have prophylactic and heavy-metal fixation functions. In summary, HZ reduces effective Pb content in edible tissues, roots, and inter-root soil by regulating inter-root soil microbial community structure, increasing soil pH, nitrogen content, and soil enzyme activity, and altering dominant phylum abundance.

In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity.

An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and on the ground. This paper presents a newly established GNSS ionospheric observation network (GION) around the north equatorial ionization anomaly (EIA) crest in south China, which has a longitudinal coverage of ∼30° from 94°E to 124°E. The measurement with signals from geostationary earth orbit (GEO) satellites of the BeiDou navigation satellite system (BDS) is capable of separating the temporal and spatial variations of the ionosphere. A temporal fluctuation of TEC (TFT) parameter is proposed to characterize EPBs. The longitude of the EPBs’ generation can be located with TFT variations in the time–longitude dimension. It is found that the post-sunset EPBs have a high degree of longitudinal variability. They generally show a quasiperiodic feature, indicating their association with atmospheric gravity wave activities. Wave-like structures with different scale sizes can co-exist in the same night.

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%.

Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data. For unordered multi-categorical variables, existing encoding methods may produce dimensional catastrophes and may also introduce additional order misrepresentation and distance bias in neural network computation. To solve the above problems, this paper proposes an unordered multi-categorical variable encoding method O-AE using orthogonal matrix for encoding and encoding representation learning and dimensionality reduction via an autoencoder. Bayesian optimization is used for hyperparameter optimization of the autoencoder. Finally, seven experiments were designed with the basic O-AE, Bayesian optimization of the hyperparameters of the autoencoder for O-AE, and other encoding methods to encode unordered multi-categorical variables in five datasets, and they were input into a BP neural network to carry out target prediction experiments. The results show that the experiments using O-AE and O-AE-b have better prediction results, proving that the method proposed in this paper is highly feasible and applicable and can be an optional method for the data processing of unordered multi-categorical variables.

This study examines the challenges related to the storage and distribution of cabin materials on board during the construction of cruise ships. The construction process of cruise ships involves the assembly of various cabin materials, which are temporarily stored and transported in a complex deck environment with multiple obstacles. These processes can be viewed as location-routing problems (LRPs). However, existing solutions for LRPs do not account for the obstacles commonly encountered in deck environments. Therefore, this paper introduces a new variant of LRPs, termed as the BE-LRP (blocking environment location-routing problem). Initially, by considering the optimization objectives of minimizing the distribution distance and the number of vehicles required, the paper develops an optimization model for the location-routing of on-board materials while taking obstacles into consideration. Subsequently, a method for estimating blocked distances is proposed. This method utilizes Gaussian process regression to predict blocked distances and enables the rapid estimation of distances obstructed by obstacles between different destinations. Lastly, a hybrid obstacle blocking distance processing HO-NSGAII algorithm is formulated to address the BE-LRP. Experimental comparisons demonstrate that the distribution scheme derived from this research method does not necessitate modifications to the delivery distance when compared to the traditional NSGAII algorithm, and better aligns with the actual deck conditions. With an equivalent number of delivery vehicles, the scheme results in shorter delivery distances. The delivery distance can be reduced by 3–17% under varying circumstances. These findings are advantageous for enhancing the efficiency of cruise ship construction and mitigating the impact of warehousing and distribution on construction progress.

Prefabricated cabin modular units (PMCUs) are a widespread type of intermediate products used during ship or offshore platform construction. This paper focuses on the scheduling problem of PMCU assembly flow shops, which is summarized as a multi-objective, fuzzy-blocking hybrid flow-shop-scheduling problem based on learning and fatigue effects (FB-HFSP-LF) to minimize the maximum fuzzy makespan and maximize the average fuzzy due-date agreement index. This paper proposes a multi-objective non-dominated sorting gravitational search algorithm (MONSGSA) to solve it. In the proposed MONSGSA, the ranked-order value is used to convert continuous solutions to discrete solutions. Multi-dimensional Latin hypercube sampling is used to enhance initial population diversity. Setting up an external archive to maintain non-dominated solutions while introducing an adaptive inertia factor and a trap avoidance operator to guide individual positional updates. The results of multiple sets of experiments show that Pareto solutions of MONSGSA have better distribution and convergence compared to other competitors. Finally, the instance of PMCU manufacturer is used for validation, and the results show that MONSGSA has better applicability to practical problems.

Background: The escalating complexity of the COVID-19 epidemic underscores the need for heightened attention to booster vaccinations. This study aims to examine the changing trend in the public’s intention to receive the second COVID-19 booster vaccination over time and the associated factors following the COVID-19 policy optimization in China. Method: Eight cross-sectional surveys utilizing SMS questionnaire links were conducted in Guangzhou, China, from December 2022 to April 2023. The Mann–Kendall test was employed to analyze the trend in intentions to receive the second booster vaccination across the survey time. Adjusted and multivariate logistic analyses were used to analyze the factors associated with vaccination intention. Parallel analyses were performed for two subgroups with different COVID-19 infection statuses. Results: A total of 9860 respondents were surveyed in the eight rounds, of which 8048 completed the first booster vaccination and were included in the analysis. The overall COVID-19 infection rate was 60.0% (4832/8048), while the overall vaccination intention was 72.2% (5810/8048) among respondents. The vaccination intention exhibited a significant declining trend over time, decreasing from 81.5% in December 2022 to 52.2% in April 2023. An adjusted logistic regression analysis revealed that anxiety and depression were negatively associated with an intention to receive the second booster vaccination, while COVID-19-related preventive behaviors and a high engagement in COVID-19-related information were positively associated with an intention to receive the second booster vaccination. A subgroup analysis revealed that the association between psychological and behavioral characteristics and vaccination intention remained relatively stable among individuals with different histories of COVID-19 infections. Conclusion: There was a significant decline in the intention to receive the second booster vaccination following the optimization of the COVID policy in China. Our findings emphasize the urgency of the second booster vaccination and provide a foundation for the development of tailored interventions to enhance and sustain vaccination intention among the public.

In the context of increasingly competitive shipbuilding, the flexible multi-level picking system, composed of high-rise shelves, Automated Guided Vehicles (AGVs), and picking stations, has been of gradual interest because of its advantages in operation efficiency, system flexibility, and system robustness. Compared with other simple-level systems, the flexible multi-level picking system has a more complex coupling temporal relationship, which makes the scheduling optimization of shipbuilding automated collaborative order picking (SACOP) extremely difficult. In order to avoid the dilemma of finding a feasible and optimal collaborative scheduling scheme under the constraints of a complex temporal relationship, this paper proposed a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method, which takes the AGV scheduling scheme as the direct solution and modifies it to a feasible solution under the reasonably designed interaction strategy of stacker, AGV, and the interaction strategy of picking station, AGV. Furthermore, taking the minimum energy consumption and operation time as the optimization objectives, a multi-objective optimization mathematical model was established to describe MADPSO, and an improved NSGA-III algorithm was designed to solve the problem. Finally, several experiments were conducted in various scenarios and verified that using MADPSO can achieve a comprehensive optimization index improvement of 52.02–75.66% compared with traditional picking methods, which has a certain reference significance for shipyards.

In cruise ship manufacturing, it is common practice for prefabricated modular cabin units (PMCU) to be assembled in the workshop and transported to a designated installation location on board the ship. This paper proposed a Radau pseudospectral method based on a probabilistic roadmap (PRM) for PMCU path planning in complex deck environments. Firstly, a kinematic model is constructed, and the continuous-time optimal control path planning problem is established. Among them, an area-based obstacle avoidance constraint is applied to the approach. Then, the continuous-time optimal control problem is discretized into a nonlinear programming (NLP) problem using the orthogonal collocation method. For the initial value-sensitive NLP problem solving, a strategy using PRM combined with a heuristic rule is used to obtain a high-quality initial guess. Finally, experiments under two operating scenarios of PMCU pushing demonstrate that the method is capable of generating a better path while ensuring that the whole process is collision-free. The results show that the algorithm proposed in this paper can effectively solve the PMCU pushing path planning problem in the complex deck environment, and the path considering kinematic characteristics can be used for cabin installation, which has strong operability and applicability.

In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated “operation–machine” combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories.

As a starting channel, the H-intermigration reaction of alkylperoxy radicals (ROO radicals) that yields hydroperoxyl alkyl radicals (QOOH radicals) determines the low-temperature chemistry of alkanes. In this work, this type of reaction was investigated for typical cyclic alkanes, which are important fuel components and soot precursors, using theoretical ab initio methods. First, all the molecular geometries and vibrational frequencies were computed using the density functional theory method and the single point energies were refined using the post-Hartree fork method (M062X/6-311G(d,p)//DLPNO-CCSD(T)/CBS). Then, high-pressure limit rate constants were evaluated with tight transition state theory, with which tunneling effects were considered using the Eckart model and low-frequency torsion modes were modeled as hindered rotors. Pressure-dependent rate constants were also calculated for typical reaction channels. Rate expressions in the Arrhenius form for 91 reactions are proposed. All reactions were categorized into seven reaction types and the rate rule for each reaction type was estimated with uncertainty factors of three to six. These rules can be potentially used in the development of low-temperature kinetic mechanisms for cycloalkanes. A comparison between different reaction types was also performed and the favorable channels are discussed.

The detection of underwater targets through hyperspectral imagery is a relatively novel topic as the assumption of target background independence is no longer valid, making it difficult to directly detect underwater targets using land target information. Meanwhile, deep-learning-based methods have faced challenges regarding the availability of training datasets, especially in underwater conditions. To solve these problems, a transfer-based framework is proposed in this paper, which exploits synthetic data to train deep-learning models and transfers them to real-world applications. However, the transfer becomes challenging due to the imparity in the distribution between real and synthetic data. To address this dilemma, the proposed framework, named the transfer-based underwater target detection framework (TUTDF), first divides the domains using the depth information, then trains models for different domains and develops an adaptive module to determine which model to use. Meanwhile, a spatial–spectral process is applied prior to detection, which is devoted to eliminating the adverse influence of background noise. Since there is no publicly available hyperspectral underwater target dataset, most of the existing methods only run on simulated data; therefore, we conducted expensive experiments to obtain datasets with accurate depths and use them for validation. Extensive experiments verify the effectiveness and efficiency of TUTDF in comparison with traditional methods.

Oncolytic herpes simplex virus (oHSV) is a type of virus that selectively targets and kills cancer cells, leaving normal cells unharmed. Accurate viral titer is of great importance for the production and application of oHSV products. Droplet digital PCR (ddPCR) is known for having good reproducibility, not requiring a standard curve, not being affected by inhibitors, and being precise even in the detection of low copies. In the present study, we developed a droplet digital PCR assay for the quantification of HSV-1 and applied it in the oHSV production. The established ddPCR showed good specificity, linearity, a low limit of quantification, great reproducibility, and accuracy. The quantification result was well-associated with that of plaque assay and CCID50. Amplification of the purified virus without DNA extraction by ddPCR presented similar results to that from the extracted DNA, confirming the good resistance against PCR inhibitors. With the ddPCR, viral titer could be monitored in real time during the production of oHSV; the optimal harvest time was determined for the best virus yield in each batch. The ddPCR can be used as a useful tool for the quantification of oHSV and greatly facilitate the manufacturing process of oHSV products.

Cascade reactions catalyzed by multi-enzyme systems are important in science and industry and can be used to synthesize drugs and nutrients. In this study, two types of macromolecules of bi-enzyme self-assembly clusters (BESCs) consisting of carbonyl reductase (CpCR) and glucose dehydrogenase (GDH) were examined. Stereoselective CpCR and GDH were successfully fused with SpyCatcher and SpyTag, respectively, to obtain four enzyme modules, namely: SpyCatcher-CpCR, SpyCatcher-GDH, SpyTag-CpCR, and SpyTag-GDH, which were covalently coupled in vitro to form two types of hydrogel-like BESCs: CpCR-SpyCatcher-SpyTag-GDH and GDH-SpyCatcher-SpyTag-CpCR. CpCR-SpyCatcher-SpyTag-GDH showed a better activity and efficiently converted ethyl 2-oxo-4-phenylbutyrate (OPBE) to ethyl(R)2-hydroxy-4-phenylbutanoate ((R)-HPBE), while regenerating NADPH. At 30 °C and pH 7, the conversion rate of OPBE with CpCR-SpyCatcher-SpyTag-GDH as a catalyst reached 99.9%, with the ee% of (R)-HPBE reaching above 99.9%. This conversion rate was 2.4 times higher than that obtained with the free bi-enzyme. The pH tolerance and temperature stability of the BESCs were also improved compared with those of the free enzymes. In conclusion, bi-enzyme assemblies were docked using SpyCatcher/SpyTag to produce BESCs with a special structure and excellent catalytic activity, improving the catalytic efficiency of the enzyme.

(R)-1-[3,5-bis(trifluoromethyl)phenyl]ethanamine, a key chiral intermediate of selective tetrodotoxin-sensitive blockers, was efficiently synthesized by a bienzyme cascade system formed by with R-ω-transaminase (ATA117) and an alcohol dehydrogenase (ADH) co-expression system. Herein, we report that the use of ATA117 as the biocatalyst for the amination of 3,5-bistrifluoromethylacetophenone led to the highest efficiency in product performance (enantiomeric excess > 99.9%). Moreover, to further improve the product yield, ADH was introduced into the reaction system to promote an equilibrium shift. Additionally, bienzyme cascade system was constructed by five different expression systems, including two tandem expression recombinant plasmids (pETDuet-ATA117-ADH and pACYCDuet-ATA117-ADH) and three co-expressed dual-plasmids (pETDuet-ATA117/pET28a-ADH, pACYCDuet-ATA117/pET28a-ADH, and pACYCDuet-ATA117/pETDuet-ADH), utilizing recombinant engineered bacteria. Subsequent studies revealed that as compared with ATA117 single enzyme, the substrate handling capacity of BL21(DE3)/pETDuet-ATA117-ADH (0.25 g wet weight) developed for bienzyme cascade system was increased by 1.50 folds under the condition of 40 °C, 180 rpm, 0.1 M pH9 Tris-HCl for 24 h. To the best of our knowledge, ours is the first report demonstrating the production of (R)-1-[3,5-bis(trifluoromethyl)phenyl]ethanamine using a bienzyme cascade system, thus providing valuable insights into the biosynthesis of chiral amines.

Reciprocal toolpaths with four-axis simultaneous motion of five-axis or four-axis machine tools are commonly used in the machining of blades which are widely applied in high-end equipment such as the aero-engine and the marine steam turbine. Due to the complex geometry of the blades, the tool orientation always suffers from frequent swing for this kind of toolpaths, which induces unnecessary acceleration/deceleration of the feed axes, thus degrading processing efficiency and quality. Although there are tool orientation optimization methods aiming at solving the above problem, they are mainly proposed for universal processing of the toolpaths for complex surfaces. Different from them, this paper proposes a piecewise decoupling tool orientation re-scheduling method for this kind of toolpath specifically, which takes full use of the characteristic of the reciprocal toolpaths of the blades, and takes the monotonous variation of rotation axes as an additional constraint. The re-scheduling process is realized based on the construction of a S-θ plane, where the scheduling problem is converted to the adjustment of a S-θ curve inside a feasible channel. Through two procedures, namely linearization scheduling and control-point assigning-based smoothing, the tool orientation path expressed by the S-θ curve can be effectively scheduled in a piecewise manner, and the smoothness between two adjacent pieces of the toolpaths can be ensured directly. The whole algorithm is lightweight and does not involve complex iterative operations or functional optimization solutions. Simulation and experimental tests verify the feasibility and superiority of this method. The results show that the machining efficiency of the blade is improved by 24.5%, due to the reduction of the requirement on highest feed-axis kinematics parameters after rescheduling. In addition, compared with the existing methods, the proposed method not only can improve the dynamics of feed axes in multi-axis machining, but also has advantages in computational complexity and monotonic variation property of the tool orientation.

The Global Navigation Satellite System (GNSS) allows for the cost-effective estimation of the ionospheric total electron content (TEC). However, research on error characteristics of the derived TEC is scarce, which provides insights into the quality of the GNSS ionospheric observation. We investigate characteristics of errors in the derived TEC with data from ~260 GNSS dual-frequency receivers of the Crustal Movement Observation Network of China (CMONOC). The slant TEC is calculated from carrier phase measurements and the vertical TEC over China is fitted with a spatial resolution of 1° by 1° in latitude and longitude in four seasons of 2014. It is found that the errors of both the slant TEC and the derived TEC follow Laplace distribution rather than Gaussian distribution in all seasons. The errors of the slant TEC have sharper peaks than those of the derived TEC. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the slant TEC are typically 0.04 TECU and 0.2 TECU, while the MAE and RMSE of the fitting residuals for the derived TEC are typically 1 TECU and under 2 TECU, respectively. Both MAEs and RMSEs of the derived TEC have the largest value in spring and the smallest value in summer, while the seasonal dependence is only observed in RMSE of the slant TEC.

Breast disease is one of the critical diseases that plague females, as is known, breast cancer has high mortality, despite significant pathophysiological progress during the past few years. Novel diagnostic and therapeutic approaches are needed to break the stalemate. An organ-on-chip approach is considered due to its ability to repeat the real conditions found in the body on microfluidic chips, offsetting the shortcomings of traditional 2D culture and animal tests. In recent years, the organ-on-chip approach has shown diversity, recreating the structure and functional units of the real organs/tissues. The applications were also developed rapidly from the laboratory to the industrialized market. This review focuses on breast tumor-on-a-chip approaches concerning the diversity models and applications. The models are summarized and categorized by typical biological reconstitution, considering the design and fabrication of the various breast models. The breast tumor-on-a-chip approach is a typical representative of organ chips, which are one of the precedents in the market. The applications are roughly divided into two categories: fundamental mechanism research and biological medicine. Finally, we discuss the prospect and deficiencies of the emerging technology. It has excellent prospects in all of the application fields, however there exist some deficiencies for promotion, such as the stability of the structure and function, and uniformity for quantity production.

More extensive enterprise cooperation is an effective means to increase the competitiveness of shipbuilding companies in the current distributed manufacturing environment. Most cross-enterprise collaborative processes for shipbuilding projects have been widely concerned. However, the symmetry and cooperativity among the order decision-making process is rarely involved. A key issue for decision makers is to balance the interests of each symmetric participant and realize the consistent decision-making for the order. Existing order decision-making methods in the shipbuilding industry are low efficiency. The aim of this paper is to provide an assistant decision-making method to support effective order decision-making and multi-party cooperation for the multi-wining negotiation objectives. To solve this problem, a collaborative order decision-making framework based on decision support system (DSS) and multi-agent system (MAS) theory is presented, simulating the collaborative order decision-making process, and bridging the order decision-making with production scheduling. Then, a multi-stage negotiation method is provided to solve the distributed and symmetric order decision-making problem, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system will be reported as a result.

Global ionospheric total electron content (TEC) is generally derived with ground-based Global Navigation Satellite System (GNSS) observations based on mathematical models in a solar-geomagnetic reference frame. However, ground-based observations are not well-distributed. There is a lack of observations over sparsely populated areas and vast oceans, where the accuracy of TEC derivation is reduced. Additionally, the modified dip (modip) latitude is more suitable than geomagnetic latitude for the ionosphere. This paper investigates the improvement of global TEC with multi-source data and modip latitude, and a simulation with International Reference Ionosphere (IRI) model is developed. Compared with using ground-based observations in geomagnetic latitude, the mean improvement was about 10.88% after the addition of space-based observations and the adoption of modip latitude. Nevertheless, the data from JASON-2 satellite altimetry and COSMIC occultation are sparsely-sampled, which makes the IRI TEC a reasonable estimation for the areas without observation. By using multi-source data from ground-based, satellite-based and IRI-produced observations, global TEC was derived in both geomagnetic and modip latitudes for 12 days of four seasons in 2014 under geomagnetic quiet conditions. The average root-mean-square error (RMSE) of the fitting was reduced by 7.02% in modip latitude. The improvement was largest in March and smallest in June.

Pedestrian detection is the core of the driver assistance system, which collects the road conditions through the radars or cameras on the vehicle, judges whether there is a pedestrian in front of the vehicle, supports decisions such as raising the alarm, automatically slowing down, or emergency stopping to keep pedestrians safe, and improves the security when the vehicle is moving. Suffering from weather, lighting, clothing, large pose variations, and occlusion, the current pedestrian detection still has a certain distance from the practical applications. In recent years, deep networks have shown excellent performance for image detection, recognition, and classification. Some researchers employed deep network for pedestrian detection and achieve great progress, but deep networks need huge computational resources, which make it difficult to put into practical applications. In real scenarios of autonomous vehicles, the computation ability is limited. Thus, the shallow networks such as UDN (Unified Deep Networks) is a better choice, since it performs well while consuming less computation resources. Based on UDN, this paper proposes a new deep network model named two-stream UDN, which augments another branch for solving traditional UDN’s indistinction of the difference between trees/telegraph poles and pedestrians. The new branch accepts the upper third part of the pedestrian image as input, and the partial image has less deformation, stable features, and more distinguished characters from other objects. For the proposed two-stream UDN, multi-input features including the HOG (Histogram of Oriented Gradients) feature, Sobel feature, color feature, and foreground regions extracted by GrabCut segmentation algorithms are fed. Compared with the original input of UDN, the multi-input features are more conducive for pedestrian detection, since the fused HOG features and significant objects are more significant for pedestrian detection. Two-stream UDN is trained through two steps. First, the two sub-networks are trained until converge; then, we fuse results of the two subnets as the final result and feed it back to the two subnets to fine tune network parameters synchronously. To improve the performance, Swish is adopted as the activation function to obtain a faster training speed, and positive samples are mirrored and rotated with small angles to make the positive and negative samples more balanced.

In the current distributed manufacturing environment, more extensive enterprise cooperation is an effective means for shipbuilding companies to increase the competitiveness. However, considering the project scale and the uneven production capacity between the collaborative enterprises, a key issue for shipbuilding companies is to effectively combine the product-oriented project tasks and the specialized production-oriented plants. Due to information privatization, the decision-making process of project planning and scheduling is distributed and symmetric. Existing project scheduling methods and collaboration mechanisms in the shipbuilding industry are somehow inefficient. The aim of the research is to provide an assistant decision-making method to support effective task dispatching and multi-party cooperation for better utilization of the distributed resources and to help project managers control the shipbuilding process. The article initially establishes an agent-based complex shipbuilding project collaborative planning and symmetric scheduling framework, simulating the distributed collaborative decision-making process and bridging the multi-project planning with the individual project scheduling in much detail, which fills the research gap. A negotiation method based on iterative combination auction (ICA) is further proposed to solve the integration problem of project planning and task scheduling, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system on shipbuilding projects collaborative planning and scheduling will be reported as a result.

Ground- and space-based Global Navigation Satellite System (GNSS) receivers can provide three-dimensional (3D) information about the occurrence of equatorial plasma bubbles (EPBs). For this study, we selected March 2014 data (during solar maximum of cycle 24) for the analysis. The timing and the latitudinal dependence of the EPBs occurrence rate are derived by means of the rate of the total electron content (TEC) index (ROTI) data from GNSS receivers in China, whereas vertical profiles of the scintillation index S4 are provided by COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate). The GNSS receivers of the low Earth orbit satellites give information about the occurrence of amplitude scintillations in limb sounding geometry where the focus is on magnetic latitudes from 20° S to 20° N. The occurrence rates of the observed EPB-induced scintillations are generally smaller than those of the EPB-induced ROTI variations. The timing and the latitude dependence of the EPBs occurrence rate agree between the ground-based and spaceborne GNSS data. We find that EPBs occur at 19:00 LT and they are mainly situated above the F2 peak layer which descended from 450 km at 20:00 LT to 300 km at 24:00 LT in the equatorial ionosphere. At the same time, the spaceborne GNSS data also show, for the first time, a high occurrence rate of post-sunset scintillations at 100 km altitude, indicating the coexistence of equatorial sporadic E with EPBs.

As a fundamental and challenging problem, non-rigid structure-from-motion (NRSfM) has attracted a large amount of research interest. It is worth mentioning that NRSfM has been applied to dynamic scene understanding and motion segmentation. Especially, a motion segmentation approach combining NRSfM with the subspace representation has been proposed. However, the current subspace representation for non-rigid motions clustering do not take into account the inherent sequential property, which has been proved vital for sequential data clustering. Hence this paper proposes a novel framework to segment the complex and non-rigid motion via an ordered subspace representation method for the reconstructed 3D data, where the sequential property is properly formulated in the procedure of learning the affinity matrix for clustering with simultaneously recovering the 3D non-rigid motion by a monocular camera with 2D point tracks. Experiment results on three public sequential action datasets, BU-4DFE, MSR and UMPM, verify the benefits of method presented in this paper for classical complex non-rigid motion analysis and outperform state-of-the-art methods with lowest subspace clustering error (SCE) rates and highest normalized mutual information (NMI) in subspace clustering and motion segmentation fields.

The design and synthesis of selective and sensitive chemosensors for the quantification of environmentally and biologically important ionic species has attracted widespread attention. Amidochlorin p6 (ACP); an effective colorimetric and fluorescent probe for copper ions (Cu2+) in aqueous solution derived from methyl pheophorbide-a (MPa) was designed and synthesized. A remarkable color change from pale yellow to blue was easily observed by the naked eye upon addition of Cu2+; and a fluorescence quenching was also determined. The research of fluorescent quenching of ACP-Cu2+ complexation showed the detection limit was 7.5 × 10−8 mol/L; which suggested that ACP can act as a high sensitive probe for Cu2+ and can be used to quantitatively detect low levels of Cu2+ in aqueous solution. In aqueous solution the probe exhibits excellent selectivity and sensitivity toward Cu2+ ions over other metal ions (M = Zn2+; Ni2+; Ba2+; Ag+; Co2+; Na+; K+; Mg2+; Cd2+; Pb2+; Mn2+; Fe3+; and Ca2+). The obvious change from pale yellow to blue upon the addition of Cu2+ could make it a suitable “naked eye” indicator for Cu2+.

Background and Objectives: Chronic total occlusion (CTO) is a complex lesion of coronary artery disease (CAD) with a detection rate of approximately 25% on coronary angiography. CTO patients generally experience poor quality of life and prognosis. This study aims to evaluate the association between the estimated glucose disposal rate (eGDR), a surrogate marker for insulin resistance (IR), and the prognosis of CTO PCI patients, as well as to investigate the potential role of the systemic immune-inflammation index (SII) in this process. Methods: We retrospectively included 1482 non-diabetic patients who underwent successful CTO PCI at Anzhen Hospital between January 2018 and December 2021. The primary endpoint was major adverse cardiovascular events (MACEs). Clinical characteristics, biochemical markers, and interventional records were collected, and the eGDR and SII were calculated. Cox regression, restricted cubic splines (RCSs), receiver operating characteristic (ROC) analysis, and Kaplan–Meier curves were used to assess associations. Results: MACEs occurred in 158 patients (10.67%). Patients with MACEs had lower eGDR and higher SII levels. A high eGDR significantly reduced MACE risk (Q4 vs. Q1: HR 0.06, 95% CI 0.03–0.12), while a high SII increased it (Q4 vs. Q1: HR 3.32, 95% CI 1.78–6.33). The combination of low eGDRs and high SIIs predicted the highest MACE risk (HR 4.36, 95% CI 2.71–6.01). The SII partially mediated the relationship between eGDR and MACEs. Conclusions: A low eGDR and high SII are significant predictors of poor prognosis in non-diabetic CTO PCI patients. Combining the eGDR and the SII provides a comprehensive assessment for better predicting cardiovascular outcomes.

Students’ action behavior performance is an important part of classroom teaching evaluation. To detect the action behavior of students in classroom teaching videos, and based on the detection results, the action behavior sequence of individual students in the teaching time of knowledge points is obtained and analyzed. This paper proposes a method for recognizing students’ action behaviors based on classroom time-series images. First, we propose an improved Asynchronous Interaction Aggregation (AIA) network for student action behavior detection. By adding a Multi-scale Temporal Attention (MsTA) module and a Multi-scale Channel Spatial Attention (MsCSA) module to the fast pathway and slow pathway, respectively, the accuracy of student action behavior recognition is improved in SlowFast, which is the backbone network of the improved AIA network,. Second, the Equalized Focal Loss function is introduced to improve the category imbalance that exists in the student action behavior dataset. Experimental results on the student action behavior dataset show that the method proposed in this paper can detect different action behaviors of students in the classroom and has better detection performance compared to the original AIA network. Finally, based on the results of action behavior recognition, the seat number is used as the index to obtain the action behavior sequence of individual students during the teaching time of knowledge points and the performance of students in this period is analyzed.

The development of low-cost and high-performance bifunctional electrocatalysts for overall water splitting is still challenging. Herein, we employed a facile electrodeposition method to prepare bifunctional cobalt phosphide for overall water splitting. The needle-like cobalt phosphide (Co-P-1) nanoarray is uniformly distributed on nickel foam. Co-P-1 exhibits excellent electrocatalytic activity for hydrogen evolution reaction (HER, 85 mV at 10 mA/cm2, 60 mV/dec) and oxygen evolution reaction (OER, 294 mV at 50 mA/cm2, 60 mV/dec). The cell-voltage of 1.60 V is found to achieve the current density of 10 mA/cm2 for overall water splitting in the two-electrode system, comparable to that of previously reported Pt/C/NF||RuO2/NF. The excellent electrocatalytic performance can be attributed to the needle-like structure with more active sites, accelerated charge transfer and evolved bubbles’ release. This work can provide new approach to the development of a bifunctional electrocatalyst for overall water splitting.

Lithium batteries incorporating LiFePO4 (LFP) as the cathode material have gained significant attention in recent research. However, the limited electronic and ionic conductivity of LFP poses challenges to its cycling performance and overall efficiency. In this study, we address these issues by synthesizing a series of LiFePO4/carbon (LFP/C) composites through low-temperature carbonization coating of LFP in the presence of Coke as the carbon source. The resulting lithium batteries utilizing LFP/C as the cathode material exhibited impressive discharge specific capacities of 148.35 mA·h/g and 126.74 mA·h/g at 0.1 C and 1 C rates, respectively. Even after 200 cycles of charging and discharging, the capacities remained remarkably high, with values of 93.74% and 97.05% retention, showcasing excellent cycling stability. Notably, the LFP/C composite displayed exceptional rate capability, and capacity retention of 99.27% after cycling at different multiplication rates. These findings underscore the efficacy of in situ low-temperature carbonization capping of LFP with Coke in significantly improving both the cycling stability and rate capability of lithium batteries.

Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labels are independent of each other, or, although label correlation is explored, the relationship between related labels and features is difficult to understand or specify. In real applications, both situations may occur where the labels are correlated and the features may belong specifically to some labels. Moreover, these methods treat features individually without considering the interaction between features. Based on this, we present a novel online streaming feature selection method based on label group correlation and feature interaction (OSLGC). In our design, we first divide labels into multiple groups with the help of graph theory. Then, we integrate label weight and mutual information to accurately quantify the relationships between features under different label groups. Subsequently, a novel feature selection framework using sliding windows is designed, including online feature relevance analysis and online feature interaction analysis. Experiments on ten datasets show that the proposed method outperforms some mature MFS algorithms in terms of predictive performance, statistical analysis, stability analysis, and ablation experiments.

Traditional graphite anode material typically shows a low theoretical capacity and easy lithium decomposition. Molybdenum disulfide is one of the promising anode materials for advanced lithium-ion batteries, which possess low cost, unique two-dimensional layered structure, and high theoretical capacity. However, the low reversible capacity and the cycling-capacity retention rate induced by its poor conductivity and volume expansion during cycling blocks further application. In this paper, a collaborative control strategy of monodisperse MoS2/graphite composites was utilized and studied in detail. MoS2/graphite nanocomposites with different ratios (MoS2:graphite = 20%:80%, 40%:60%, 60%:40%, and 80%:20%) were prepared by mechanical ball-milling and low-temperature annealing. The graphite sheets were uniformly dispersed between the MoS2 sheets by the ball-milling process, which effectively reduced the agglomeration of MoS2 and simultaneously improved the electrical conductivity of the composite. It was found that the capacity of MoS2/graphite composites kept increasing along with the increasing percentage of MoS2 and possessed the highest initial discharge capacity (832.70 mAh/g) when MoS2:graphite = 80%:20%. This facile strategy is easy to implement, is low-cost, and is cosmically produced, which is suitable for the development and manufacture of advance lithium-ion batteries.

The downburst events have been a research focus for decades, as their associated disastrously strong winds pose a great threat to aviation, the shipping industry, agriculture, and the power industry. On 14 May 2021, a series of severe convection occurred in middle and eastern China, during which six 500-kilovolt transmission line towers in Zhejiang were toppled down by a downburst event, resulting in a large range of power outages. By using the Weather Research and Forecasting (WRF) model version 4.4, key features of the downburst event were reproduced reasonably; based on which, we explored the evolutionary mechanisms and the three-dimensional structures of the strong winds associated with the downburst event. It was found that a southwest–northeast-orientated, eastward moving strong squall line was the parent convection system for the downburst event. The downburst-associated convection was deep (from surface to 200 hPa); in the near surface layer, it was mainly associated with positive geopotential height and negative temperature deviations, whereas, at higher levels, it was mainly associated with negative geopotential height and positive temperature deviations. Backward trajectory analysis indicates that the air particles that came from the middle troposphere west of the key region (~61.2% in proportion) were crucial for producing the strong winds of the downburst event. These air particles experienced notable descending processes, during which most of the air particles decreased notably in their potential temperature, while they increased significantly in their specific humidity. The kinetic energy budget analyses denote that, for the region surrounding the location where the tower toppling appeared, the work done by the strong pressure gradient force between the high-pressure closed center (corresponding to intense descending motions) and the low-pressure closed center (corresponding to strong latent heat release) dominated the rapid wind enhancement.

With the increasing construction of large-scale wind turbines in seismically active coastal areas, the survivability of these high-rated-power offshore wind turbines (OWTs) in marine and geological conditions becomes extremely important. Although research on the dynamic behaviors of OWTs under earthquakes has been conducted with consideration of the soil-structure interaction, the attention paid to the impact of earthquake-induced seabed liquefaction on OWTs supported by large-diameter monopiles remains limited. In view of this research gap, this study carries out dynamic analyses of a 10 MW OWT under combined wind, wave, and earthquake loadings. This study uses a pressure-dependent multisurface elastoplastic constitutive model to simulate the soil liquefaction phenomenon. The results indicate that the motion of the large-diameter monopile leads to more extensive soil liquefaction surrounding the monopile, specifically in the zone near the pile toe. Moreover, compared with earthquake loading alone, liquefaction becomes more severe under the coupled wind and earthquake loadings. Accordingly, the dynamic responses of the OWT are apparently amplified, which demonstrates the importance of considering the coupling loadings. Compared with wind loading, the effect of wave loading on the dynamic response and liquefaction potential is relatively insignificant.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder in the elderly people, currently with no cure. Its mechanisms are not well understood, thus studies targeting cause-directed therapy or prevention are needed. This study uses the transgenic Caenorhabditis elegans PD model. We demonstrated that dietary supplementation of the worms with an extract from the cultivated red seaweed Chondrus crispus decreased the accumulation of α-synulein and protected the worms from the neuronal toxin-, 6-OHDA, induced dopaminergic neurodegeneration. These effects were associated with a corrected slowness of movement. We also showed that the enhancement of oxidative stress tolerance and an up-regulation of the stress response genes, sod-3 and skn-1, may have served as the molecular mechanism for the C. crispus-extract-mediated protection against PD pathology. Altogether, apart from its potential as a functional food, the tested red seaweed, C. crispus, might find promising pharmaceutical applications for the development of potential novel anti-neurodegenerative drugs for humans.

Vertical axis wind turbines (VAWTs) exhibit many advantages and great application prospect as compared with horizontal ones. However, large-scale VAWTs are rarely reported, and the codes and guidelines for designing large-scale VAWTs are lacking. Designing a large-scale composite blade requires precise finite element (FE) modeling and stress analysis at the lamina level, while precise modeling of an entire VAWT is computationally intensive. This study proposes a comprehensive fatigue and ultimate strength analysis framework for VAWTs. The framework includes load determination, finite element (FE) model establishment, and fatigue and ultimate strength analyses. Wind load determination has been presented in the companion paper. In this study, laminated shell elements are used to model blades, which are separately analyzed by ignoring the influence of the tower and arms. Meanwhile, beam elements are used to model an entire VAWT to conduct a structural analysis of other structural components. A straight-bladed VAWT in Yang Jiang, China, is used as a case study. The critical locations of fatigue and ultimate strength failure of the blade, shaft, arms, and tower are obtained.

When compared with horizontal-axis wind turbines, vertical-axis wind turbines (VAWTs) have the primary advantages of insensitivity to wind direction and turbulent wind, simple structural configuration, less fatigue loading, and easy maintenance. In recent years, large-scale VAWTs have attracted considerable attention. Wind loads on a VAWT must be determined prior to structural analyses. However, traditional blade element momentum theory cannot consider the effects of turbulence and other structural components. Moreover, a large VAWT cannot simply be regarded as a planar structure, and 3D computational fluid dynamics (CFD) simulation is computationally prohibitive. In this regard, a practical wind load simulation method for VAWTs based on the strip analysis method and 2D shear stress transport (SST) k-ω model is proposed. A comparison shows that the wind pressure and aerodynamic forces simulated by the 2D SST k-ω model match well with those obtained by 2.5D large eddy simulation (LES). The influences of mean wind speed profile, turbulence, and interaction of all structural components are considered. A large straight-bladed VAWT is taken as a case study. Wind loads obtained in this study will be applied to the fatigue and ultimate strength analyses of the VAWT in the companion paper.

Electrospray is a simple and cost-effective method to fabricate micro-structured thin films. This work investigates the electrospray process of ZnO patterns. The effects of experimental parameters on jet characteristics and electrosprayed patterns are studied. The length of stable jets increases with increasing applied voltage and flow rate, and decreases with increasing nozzle-to-substrate distance, while electrospray angles exhibit an opposite trend with respect to the stable jet lengths. The diameter of electrosprayed particles decreases with increasing applied voltage, and increases with flow rate. Furthermore, an alcohol gas sensor is presented. The ZnAc is calcined into ZnO, which reveals good repeatability and stability of response in target gas. The sensing response, defined as the resistance ratio of R0/Rg, where R0 and Rg are resistance of ZnO in air and alcohol gas, increases with the concentration of alcohol vapors and electrospray deposition time.

A continuous near-field electrospray process has been developed to deposit micropatterns. Different from traditional electrospray technologies, the nozzle-to-substrate distance was shortened to less than 5 mm, and a glass capillary nozzle with a diameter of tens of microns was used. Steady and continuous ejection was achieved, and patterns with line widths of sub-100 μm were generated. The influence of experimental parameters was investigated. The critical voltage for electrospray increased with nozzle-to-substrate distance and flow rate. The line width of electrosprayed patterns increased with the increases in applied voltage, flow rate, nozzle diameter, and deposition time. This work provides a simple and potential route for on-demand deposition of micro-/nano-patterns in the electrospray process.