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The escalating greenhouse gas (GHG) emissions from maritime trade present a serious environmental and biological threat. With increasing emission reduction initiatives, such as the European Union’s incorporation of the maritime sector into the emissions trading system, both challenges and opportunities emerge for maritime transport and associated industries. To address these concerns, this study presents a model specifically designed for estimating and projecting the spatiotemporal GHG emission inventory of ships, particularly when dealing with incomplete automatic identification system datasets. In the computational aspect of the model, various data processing techniques are employed to rectify inaccuracies arising from incomplete or erroneous AIS data, including big data cleaning, ship trajectory aggregation, multi-source spatiotemporal data fusion and missing data complementation. Utilizing a bottom-up ship dynamic approach, the model generates a high-resolution GHG emission inventory. This inventory contains key attributes such as the types of ships emitting GHGs, the locations of these emissions, the time periods during which emissions occur, and emissions. For predictive analytics, the model utilizes temporal fusion transformers equipped with the attention mechanism to accurately forecast the critical emission parameters, including emission locations, time frames, and quantities. Focusing on the sea area around Tianjin port—a region characterized by high shipping activity—this study achieves fine-grained emission source tracking via detailed emission inventory calculations. Moreover, the prediction model achieves a promising loss function of approximately 0.15 under the optimal parameter configuration, obtaining a better result than recurrent neural network (RNN) and long short-term memory network (LSTM) in the comparative experiments. The proposed method allows for a comprehensive understanding of emission patterns across diverse vessel types under various operational conditions. Coupled with the prediction results, the study offers valuable theoretical and data-driven support for formulating emission reduction strategies and optimizing resource allocation, thereby contributing to sustainable maritime transformation.

Membrane materials were widely used in guided tissue regeneration (GTR) to prevent fibroblast invasion and form a confined area for preferentially growing of osteoblast. A novel collagen-hyaluronate composite gradient membrane was prepared by Tilapia (Oreochromis mossambicus) skin collagen and sodium hyaluronate for potential GTR applications and their bioactivities were investigated by cellular viability. SEM results indicated the membrane showed a dense outer and a porous inner surface for effectively guiding the growth of bone tissue. Physicochemical and biosafety experiments showed the tensile strength of membrane was 466.57 ± 44.31 KPa and contact angle was 74.11°, and the membrane showed perfect biocompatibility and cytocompatibility as well, which met the requirements of GTR material. Cell morphology revealed that the membrane could facilitate the adherence and proliferation of fibroblast and osteoblast. The results of qRT-PCR and ELISA demonstrated that the membrane could effectively activate TGF-β/Smad pathway in fibroblast, and promote the expressions of TGF-β1, FN1 and VEGF. Remarkably, RUNX2 was stimulated in BMP2 pathway by the membrane to regulate osteoblast differentiation. In summary, the collagen-hyaluronate composite gradient membrane not only fulfills the prerequisites for use as a GTR material but also demonstrates substantial potential for practical applications in the field.

IntroductionIn recent years, the adverse effects of escalating maritime trade and international shipping– particularly in regard to increased greenhouse gas emissions and their impact on human health– have come to the fore. These issues have thus instigated a surge in pressure to enhance the regulation of shipborne carbon emissions.MethodsThe study utilized the automatic identification system (AIS) data, Lloyd’s register data, and pollutant emission parameters to calculate the carbon emissions from the main engine, auxiliary engine, and boiler of vessels under varying sailing conditions, utilizing the dynamic method of ships. In relation to geographic information and ship trajectory, a comprehensive inventory of ship carbon emissions was developed, revealing pronounced spatiotemporal characteristics. To assure the accuracy of the substantial AIS dataset, procedures including data cleaning, trajectory integration, data fusion, and completion were executed. Such processes are indispensable, given the potential for transmission and storage errors associated with AIS data. To forecast CO2 emissions over diverse time intervals, a temporal fusion transformer model equipped with attention mechanisms was employed.ResultThe paper furnishes a case study on Tianjin Port, wherein a high-resolution carbon emissions inventory was devised based on AIS data acquired from vessels. This inventory was subsequently employed to generate multi-feature predictions of future carbon emissions. Given the optimal parameter configuration, the proposed method attained P50 and P90 values of 0.244 and 0.118 respectively, thereby demonstrating its efficacy.DiscussionRecognizing the sources of ship carbon emissions in this region and forecasting such emissions in the future substantiates that this method accurately portrays the laws of ship carbon emissions. Our study provides a scientific basis for decision-making in port and pollution management, enabling the creation of targeted emission reduction policies for ships.

Polymer flooding, one of the main methods for improving crude oil recovery using chemical flooding technology in China, is widely used for actual oil displacement. Partially hydrolyzed polyacrylamide (HPAM) is a commonly used linear polymer in polymer flooding, but it exhibits poor temperature and salt resistance due to its molecular structure. Therefore, branched polymers have been studied. This article provides a review of the specific synthetic methods and current practical applications in the petrochemical field of dendritic polymers and hyperbranched macromolecules. The advantages and disadvantages of each synthetic method for branched polymers are also elaborated. Finally, the application prospects of branched polymers are discussed. The feasibility of branched polymers in large quantities should be further verified through additional field tests, which should address concerns such as synthesis costs and reaction efficiency.

The requirements for drilling bentonites are tightening due to ever-increasing demands for petroleum resources, coupled with cost and reaction technology constraints. In addition to raising the risk of drilling, bentonite’s poor performance also raises the possibility of safety incidents and significant financial losses. Organically modified bentonites effectively reduce the consumption of drilling fluids, conserve resources, and lessen environmental effects. This paper aims to provide an overview of the several organic modification methods of bentonite for drilling fluids. It also evaluates the characteristics and application impacts of bentonite. We primarily describe the three popular modification methods represented by intercalation, coupling, and grafting. Also, this review provides the effect of molecular simulation on the investigation of structure in microconfined conditions. Through microlearning, organically modified bentonite with exceptional performance is to be further developed.

The gasification filter cake (GFC) has great application potential for improving the characteristics of seedling substrates due to its nutrient richness and excellent water retention capacity. However, GFCs leach heavy metals easily and thus pose certain ecological risks. Sodium silicate can enhance plant resistance to heavy metal toxicity by fixing heavy metals. This study investigated the impact of sodium silicate on cucumber plant growth, the chemical characterization of the substrate, and the distribution and transfer of heavy metals. Sodium silicate was added to the seedling substrate mix at mass rates of 0 g/kg−1 (GFC0), 2 g/kg−1 (GFC2), 4 g/kg−1 (GFC4), and 8 g/kg−1 (GFC8). The seedling substrate was composed of a commercial matrix, caragana compost, and GFC (m:m 7:7:2). The GFC increased the content of total phosphorus (P), available phosphorus (P), and available potassium (K) in the substrate by 31.58%, 16.58%, and 80.10%, respectively. Conversely, the GFC decreased the plant height by 12.3%. Adding sodium silicate to the GFC increased the chlorophyll content of the plants, fixed heavy metals in the substrate, and promoted nutrient absorption and utilization by the plants. Compared with GFC0 without sodium silicate, adding sodium silicate at a mass rate of 2 g/kg−1 (GFC2) reduced the chromium, lead, and cadmium contents by 51.13%, 26.37%, and 90.04%, respectively, which effectively alleviated heavy metal stress and was more conducive to plant growth.

BackgroundThe expectation maximization (EM) algorithm is a common tool for estimating the parameters of Gaussian mixture models (GMM). However, it is highly sensitive to initial value and easily gets trapped in a local optimum.MethodTo address these problems, a new iterative method of EM initialization (MRIPEM) is proposed in this paper. It incorporates the ideas of multiple restarts, iterations and clustering. In particular, the mean vector and covariance matrix of sample are calculated as the initial values of the iteration. Then, the optimal feature vector is selected from the candidate feature vectors by the maximum Mahalanobis distance as a new partition vector for clustering. The parameter values are renewed continuously according to the clustering results.ResultsTo verify the applicability of the MRIPEM, we compared it with other two popular initialization methods on simulated and real datasets, respectively. The comparison results of the three stochastic algorithms indicate that MRIPEM algorithm is comparable in relatively high dimensions and high overlaps and significantly better in low dimensions and low overlaps.

The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.

The management of crop production in a sandy soil “culture” is difficult, mainly due to its low soil-water-holding capacity, organic matter and poor fertilizer efficiency. Options to increase soil water and nutrient retention for these soils include the addition of surface mulch covers, amendment with biochar and the use of layers of a mixture of charcoal and compost material. Our objective was to measure the distribution of water and nutrients for layers of control 1 (CK1), control 2 (CK2) and compost material of different thicknesses (0.02, 0.05 and 0.10 m) buried 0.01 m from the surface in a column (0.2 m radius, 0.5 m height) filled with sand. The experiment was conducted in a greenhouse located at the Agricultural Science Training Base of Ningxia University, China. There were three replicates per treatment and one soil column per replicate. The soil columns were watered with 2 L via a surface drip emitter and 45 days later, soil samples were obtained in 0.01 m increments across the diameter and depth of 0.4 m, with a total of 12 samples per column. In each sample, we measured soil water, pH, electrical conductivity, ammonium and nitrate nitrogen and available P and K. The results showed that the distribution of water content and nutrient contents were centered on the dripper and diffused to its surroundings. Notably, the horizontal diffusion distance was smaller than that of the vertical direction. In the vertical direction, compared with control 1, adding compost changed the spatial distribution of WC and nutrients and had a greater impact on available potassium (AK) than on inorganic nitrogen (IN) and available phosphorus (AP). Compared with control 1, the composting treatment decreased the content of water in the 0–10 cm surface soil, reduced the electrical conductivity (EC) and nitrate nitrogen (NO3-N), C5 and C10 increased the available potassium. Moreover, composting treatments increased the electrical conductivity, available phosphorus, available potassium and nitrate nitrogen of the 10–30 cm substrate by 61–384%, 10–240%, 11–45% and 133–929%, respectively, when compared with control 1.The nutrients increased as the thickness of the compost interlayer increased. A principal component analysis (PCA) of the C5 and C10 treatments significantly distinguished them from control 1. A linear regression fitting analysis showed that the inorganic nitrogen, available potassium and total nutrients positively correlated with the water content and electrical conductivity of the sand. The 5 cm and 10 cm composting interlayers had a high water content and ability to conserve fertilizer for sand culture, but C10 caused an excessive accumulation of nutrients. Thus, it was concluded that a composting interlayer that was less than 5 cm reduced the base fertilizer input by 24–84%. All these results suggest that applying a composting interlayer of 5 cm could retain more suitable root zone water and fertilizer for the next crop season and provide technological support to reduce fertilizer inputs.

To fully explore the influence mechanism of interactions between different monomer units of proanthocyanidins (PAs) on biological activity, a path analysis model of the PA structure-activity relationship was proposed. This model subdivides the total correlation between each monomer unit and activity into direct and indirect effects by taking into account not only each monomer unit but also the correlation with its related monomer units. In addition, this method can determine the action mode of each monomer unit affecting the activity by comparing the direct and total indirect effects. Finally, the advantage of this model is demonstrated through an influence mechanism analysis of Rhodiola crenulata PA monomer units on antioxidant and anti-diabetes activities.

Previous studies have reported that a series of sensory–motor-related cortical areas are affected when a healthy human is presented with images of tools. This phenomenon has been explained as familiar tools launching a memory-retrieval process to provide a basis for using the tools. Consequently, we postulated that this theory may also be applicable if images of tools were replaced with images of daily objects if they are graspable (i.e., manipulable). Therefore, we designed and ran experiments with human volunteers (participants) who were visually presented with images of three different daily objects and recorded their electroencephalography (EEG) synchronously. Additionally, images of these objects being grasped by human hands were presented to the participants. Dynamic functional connectivity between the visual cortex and all the other areas of the brain was estimated to find which of them were influenced by visual stimuli. Next, we compared our results with those of previous studies that investigated brain response when participants looked at tools and concluded that manipulable objects caused similar cerebral activity to tools. We also looked into mu rhythm and found that looking at a manipulable object did not elicit a similar activity to seeing the same object being grasped.

The accuracy estimation of induction motors’ efficiency is beneficial and crucial in the industry for energy savings. The requirement for in situ machine efficiency estimation techniques is increasing in importance because it is the precondition to making the energy-saving scheme. Currently, the torque and speed identification method is widely applied in online efficiency estimation for motor systems. However, the higher precision parameters, such as stator resistance Rs and equivalent resistance of iron losses Rfe, which are the key to the efficiency estimation process with the air gap torque method, are of cardinal importance in the estimation process. Moreover, the computation burden is also a severe problem for the real-time data process. To solve these problems, as for the torque and speed-identification-based efficiency estimation method, this paper presents a lower time burden method based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA). The contribution of the proposed method is to transform the disadvantages of former algorithms to develop a reliable hybrid algorithm to identify the crucial parameters, namely, Rs and Rfe. Sensorless speed identification based on the rotor slot harmonic frequency (RSHF) method is adopted for speed determination. This hybrid algorithm reduces the computation burden by about 1/3 compared to the classical genetic algorithm (GA). The proposed method was validated by testing a 5.5 kW motor in the laboratory and a 10 MW induction motor in the field.

Background. The complexities of surgery require an efficient and explicit method to evaluate and standardize surgical procedures. A reliable surgical evaluation tool will be able to serve various purposes such as development of surgery training programs and improvement of surgical skills. Objectives. (a) To develop a modeling framework based on integration of dexterity analysis and design structure matrix (DSM), to be generally applicable to predict total duration of a surgical procedure, and (b) to validate the model by comparing its results with laparoscopic cholecystectomy surgery protocol. Method. A modeling framework is developed through DSM, a tool used in engineering design, systems engineering and management, to hierarchically decompose and describe relationships among individual surgical activities. Individual decomposed activities are assumed to have uncertain parameters so that a rework probability is introduced. The simulation produces a distribution of the duration of the modeled procedure. A statistical approach is then taken to evaluate surgery duration through integrated numerical parameters. The modeling framework is applied for the first time to analyze a surgery; laparoscopic cholecystectomy, a common surgical procedure, is selected for the analysis. Results. The present simulation model is validated by comparing its results of predicted surgery duration with the standard laparoscopic cholecystectomy protocols from the Atlas of Minimally Invasive Surgery with 2.5% error and that from the Atlas of Pediatric Laparoscopy and Thoracoscopy with 4% error. Conclusion. The present model, developed based on dexterity analysis and DSM, demonstrates a validated capability of predicting laparoscopic cholecystectomy surgery duration. Future studies will explore its potential applications to other surgery procedures and in improving surgeons’ performance and training novices.

Nowadays, variable preparation, forming and processing methods of fiber metal laminates are constantly developing to meet the requirements of different application fields, hence the characteristics and evolution of residual stresses under different manufacturing conditions deserve more attention. In this work, the evolution behaviors of curing residual stresses in GLARE under a hot-pressing condition were studied, and the residual stress reduction mechanism was also explained. Results suggested the FE prediction models of the entire cure process, verified by the fiber Bragg grating (FBG) sensors, were more precise than the traditional elastic model. Moreover, the stress evolution during the cure process mainly occurred in the cooling stage, in which the different coefficient of thermal expansion (CTE) of aluminum and GFRP played a major role. Meanwhile, curing shrinkage stress in the GFRP layer during the holding stage at curing temperature obviously influenced the final stress level. The residual stresses in GFRP layers differed by 9.6 MPa under a hot-pressing and autoclave condition, in which the convection heat transfer condition played a major role as it caused lower thermal stress in the holding stage and a smaller temperature gradient in the cooling stage. Considering this, a lower cooling rate could be a feasible way to obtain GLARE with lower residual stress under a hot-pressing condition.

The accuracy of user location information is inversely proportional to the user's privacy preserving degree k, and is proportional to quality of query service. In order to balance the conflict between privacy preserving security and query quality caused by the accuracy of location information, a clustering algorithm aiming at eliminating outliers based on the k-anonymity location privacy preserving model is proposed, which is used to realize the establishment of anonymous group in the anonymous model. The distribution of user in the anonymous group is optimized. The idea of replacing the user location query by the center of the anonymous group is proposed. The number of repeated queries is reduced, and the quality of query service is improved on the premise of ensuring security through the experimental analysis and comparison with other schemes.

At present, with the increasingly growing application of Internet of Things, virtualization and intelligent technologies, and the maturity of smart campus systems, campus card technology based on radio frequency identification (RFID) has developed rapidly. Due to the convenience of use, it is widely used in dormitory access control system, book borrowing system, attendance system, and payment system. Because the data carried by the RFID card is more and more complex, the requirements for its security are getting higher and higher. In order to solve the problem of identity authentication and privacy protection of the RFID application existed in the digital campus in recent years, an improved mutual authentication protocol in mobile RFID for smart campus is put forward by analyzing and studying existing smart campus authentication protocols based on hash algorithm, which uses the technology of random number S and time stamp T. This protocol implements mutual authentication among the tag, reader, and backend database without the premise that the signal path between the database and reader communication is a safe signal path and can effectively resist counterfeit attack, replay attack, position tracking attack, de-synchronization attack, eavesdropping attack, denial of service attack, and man-in-the-middle attack, and it can also guarantee forward security. Due to the large data operations and storage have been transferred to readers and backend data, the protocol is formally proved to have a strong reliability and a good security. In addition, through the comparison with other existing authentication protocols on the resident variable storage overhead, the cost of computing complexity and the security performance, the authentication protocol proposed in this paper has a better storage and calculation performance, and it can better satisfy the security requirements in mobile RFID applications for smart campus in the future and has a good application prospect.

Abstract In order to reduce the impact force of swing legs and improve walking stability when a small humanoid robot is walking, a set of impact dynamics equations based on the second kind Lagrange equation is produced, and an impact compensation control strategy with a BP network optimized by a particle swarm algorithm is designed. The core element of the compensation controller is replacing the error back propagation with a particle swarm algorithm. Due to the regulating joints of the knee, hip and ankle, the walking process is more stable than before. The experiment results show that when the left swing leg lands, the impact force drops by 2N and 1.5N respectively in the moments 4.5s and 10.5s. Therefore, the compensation strategy can reduce the impact force effectively and improve the walking stability.

We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system.