Upgraded Model Research Articles

Numerical study on the behaviour of horizontal anchor using upgraded SANISAND-MS

Horizontal plate anchors are widely used to secure tension leg platforms via mooring lines. A thorough understanding of the monotonic, cyclic and post-cyclic behaviour of plate anchors is essential to accurately estimate plate anchors' capacity. This paper presents a systematic investigation of horizontal rectangular plate anchors subjected to vertical monotonic and cyclic loads in quartz sand under drained conditions using three-dimensional finite-element simulations. The upgraded SANISAND-MS model is employed to accurately capture sand's ratcheting behaviour. The monotonic uplift load is affected by the contact types used between the soil-anchor interface, and it increases with increasing embedded depth ratio, aspect ratio, and relative density. The results of cyclic tests reveal that in loose and dense sands, the cyclic amplitude ratio and cyclic asymmetric ratio affect the displacement accumulation at the cyclic stage. The cyclic loading leads to a higher post-cyclic uplift load than the monotonic tests. Moreover, the post-cyclic uplift load increases more in tests with a high cyclic amplitude ratio. Investigation on local soil responses shows that this can be attributed to sand's densification, which increases sand's strength and stiffness. The study also supports the development of long term analysis for anchoring systems subjected to cyclic loading in offshore applications.

Enabling a process-oriented hydro-biogeochemical model to simulate soil erosion and nutrient losses

Abstract. Water-induced erosion and associated particulate carbon (PC), particulate nitrogen (PN) and particulate phosphorus (PP) nutrient losses are vital parts of biogeochemical cycling. Identifying their intensity and distribution characteristics is of great significance for the control of soil and water loss and nitrogen/phosphorus nonpoint source pollution. This study incorporated modules of physical soil erosion and associated PC, PN and PP losses into a process-oriented hydro-biogeochemical model (Catchment Nutrients Management Model coupled with DeNitrification–DeComposition, CNMM-DNDC) to enable it to predict soil and water loss. The results indicated that the upgraded CNMM-DNDC (i) performed well in simulating the observed temporal dynamics and magnitudes of surface runoff, sediment and PN/PP yields in the lysimetric plot of the Jieliu catchment in Sichuan Province and (ii) successfully predicted the observed monthly dynamics and magnitudes of stream flow, sediment yield and PN yields at the catchment outlet, with significant univariate linear regressions and acceptable Nash–Sutcliffe indices higher than 0.74. The upgraded CNMM-DNDC demonstrated that a greater proportion of PN to total nitrogen (TN) during the period with large precipitation events and amounts than that during the drought period (16.2 %–26.6 % versus 2.3 %–12.4 %). The intensities of soil erosion and particulate nutrient yields in the Jieliu catchment were closely related to land use type in the following order: sloping cultivated upland (SU) > residential areas (RA) > forest land (FL). The scenario analysis demonstrated that high greenhouse gas (GHG) emissions scenarios provided a greater risk of soil erosion than did low GHG emissions scenarios and that land use change (i.e., from SU to FL) could help to mitigate soil and water loss accelerated by climate change in the future. The upgraded model was demonstrated to have the ability of predicting ecosystem productivity, hydrologic nitrogen loads, emissions of GHGs and pollutant gases, soil erosion and particulate nutrient yields, which renders it a potential decision support tool for soil erosion and nonpoint source pollution control coordinated with increasing production and reducing GHG and pollutant gases emissions in a catchment.

Detection of Large Foreign Objects on Coal Mine Belt Conveyor Based on Improved

An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and images acquired with low quality, recursive filtering and MSRCR image enhancement algorithms were utilized to preprocess the dynamic images collected by underground monitoring devices, substantially enhancing image quality. The YOLOv5 model has been improved by introducing a multi-scale attention module (MSAM) during the channel map slicing, thereby increasing the model’s resistance to interference from redundant image features. Deep separable convolution was utilized in place of conventional convolution to detect, identify, and process large foreign objects on the belt conveyor as well as to increase detection speed. The MSAM-YOLOv5 model was trained before being installed on the NVIDIA Jetson Xavier NX platform and utilized to identify videos gathered from the coal mine belt conveyor. According to the experimental findings, the upgraded MSAM-YOLOv5 model has a greater recognition accuracy than YOLOv5L, with an average recall rate for different foreign objects of 96.27%, an average detection accuracy of 97.35%, and a recognition speed of 44 frames/s. The algorithm assures detection accuracy while increasing detection speed, satisfying the requirements for large foreign object detection on belt conveyors in coal mines.

Handwritten Digit Recognition Based on the MNIST Dataset under PyTorch

Thanks to advancements in machine learning and artificial intelligence techniques, computers can now practice on data and learn from it in a manner that is similar to how the human brain works. Handwritten character and number identification has been one of the most pressing and fascinating subjects in pattern recognition and image processing. One of the most urgent and intriguing topics in pattern recognition and picture processing has been the identification of handwritten characters and numbers. As a crucial part of artificial intelligence, handwritten digit identification technology provides a vast array of application possibilities. The data demonstrates that, even though handwritten numbers are simply created with a few straightforward strokes, the appearance of numbers is more variable due to the various writing styles of each person. In this study, a deep learning framework-based upgraded LeNet-5 convolutional neural network model is used to build a handwritten number recognition model in Python. Automatic recognition of handwritten numbers will become the standard recognition technique if it can be applied to a wide range of industries, including banking and accounting, and hence save human costs.

Fully Coupled Photochemistry of the Deuterated Ionosphere of Mars and Its Effects on Escape of H and D

AbstractAlthough deuterium (D) on Mars has received substantial attention, the deuterated ionosphere remains relatively unstudied. This means that we also know very little about non‐thermal D escape from Mars, since it is primarily driven by excess energy imparted to atoms produced in ion‐neutral reactions. Most D escape from Mars is expected to be non‐thermal, highlighting a gap in our understanding of water loss from Mars. In this work, we set out to fill this knowledge gap. To accomplish our goals, we use an upgraded 1D photochemical model that fully couples ions and neutrals and does not assume photochemical equilibrium. To our knowledge, such a model has not been applied to Mars previously. We model the atmosphere during solar minimum, mean, and maximum, and find that the deuterated ionosphere behaves similarly to the H‐bearing ionosphere, but that non‐thermal escape on the order of 8,000–9,000 cm−2 s−1 dominates atomic D loss under all solar conditions. The total fractionation factor, f, is f = 0.04–0.07, and integrated water loss is 147–158 m global equivalent layer. This is still less than geomorphological estimates. Deuterated ions at Mars are likely difficult to measure with current techniques due to low densities and mass degeneracies with more abundant H ions. Future missions wishing to measure the deuterated ionosphere in situ will need to develop innovative techniques to do so.

UTTSR: A Novel Non-Structured Text Table Recognition Model Powered by Deep Learning Technology

To prevent the compilation of documents, many table documents are formatted with non-editable and non-structured texts such as PDFs or images. Quickly recognizing the contents of tables is still a challenge due to factors such as irregular formats, uneven text quality, and complex and diverse table content. This article proposes the UTTSR table recognition model, which consists of four parts: text region detection, text line detection and recognition, and table sequence recognition. For table detection, the Cascade Faster RCNN with the ResNeXt105 network is implemented, using TPS (Thin Plate Spline) transformation and affine transformation to correct the image and to improve accuracy. For text line detection, DBNET is used with Do-Conv in FPN (Feature Pyramid Networks) to speed up training. Text lines are recognized using CRNN without the CTC module, enhancing recognition performance. Table sequence recognition is based on the transformer combined with post-processing algorithms that fuse table structure sequences and unit grid content. Experimental results show that the UTTSR model outperforms the compared methods. This upgraded model significantly improves the accuracy of the previous state-of-the-art F1 score on complex tables, reaching 97.8%.

Upgraded closed-form cutting force models for general-helix cylindrical milling tools with application to cutting power and energy demand modeling

This paper upgrades the original closed-form models of cutting force for general-helix milling tools for higher accuracy and demonstrates an application of the upgraded models in closed-form modeling of cutting power. The proposed models are shown to be numerically exact for the conventional fixed helix angle milling tools while the original models are not even though they are more accurate than the numerical methods. Errors of 0.00%, 12.15%, and 50.66% are recorded for an upgraded closed-form model, the equivalent original closed-form model, and an equivalent numerical method. Higher accurate applicability of the upgraded closed-form models to variable helix tools is also demonstrated for the harmonic case. Typical errors of 1.37%, 4.84%, and 9.94% are recorded for an upgraded closed-form model, the equivalent original closed-form model, and an equivalent numerical method. The proposed closed-form cutting force models are used to formulate new closed-form cutting power models for general-helix cylindrical milling tools which are applied in numerical evaluation of average cutting power. Evaluated data sets of average cutting power (seen to agree with published values) and spindle speed are then used in empirical calibration of average milling machine power demand. The high goodness-of-fit of the models with three published measured data sets are reflected in the high [Formula: see text] values of 0.9980, 0.9834, and 0.9472 and low mean percentage errors (MPE) of −0.1247, −0.4137, and −0.6242.

Rail Surface Defect Detection Based on Image Enhancement and Improved YOLOX

During the long and high-intensity railway use, all kinds of defects emerge, which often produce light to moderate damage on the surface, which adversely affects the stable operation of trains and even endangers the safety of travel. Currently, models for detecting rail surface defects are ineffective, and self-collected rail surface images have poor illumination and insufficient defect data. In light of the aforementioned problems, this article suggests an improved YOLOX and image enhancement method for detecting rail surface defects. First, a fusion image enhancement algorithm is used in the HSV space to process the surface image of the steel rail, highlighting defects and enhancing background contrast. Then, this paper uses a more efficient and faster BiFPN for feature fusion in the neck structure of YOLOX. In addition, it introduces the NAM attention mechanism to increase image feature expression capability. The experimental results show that the detection of rail surface defects using the algorithm improves the mAP of the YOLOX network by 2.42%. The computational volume of the improved network increases, but the detection speed can still reach 71.33 fps. In conclusion, the upgraded YOLOX model can detect rail surface flaws with accuracy and speed, fulfilling the demands of real-time detection. The lightweight deployment of rail surface defect detection terminals also has some benefits.

Modeling sea ice albedo and transmittance measurements with a fully-coupled radiative transfer model.

A rigorous treatment of the sea ice medium has been incorporated in the advanced Coupled Ocean-Atmosphere Radiative Transfer (COART) model. The inherent optical properties (IOPs) of brine pockets and air bubbles over the 0.25-4.0 µm spectral region are parameterized as a function of the sea ice physical properties (temperature, salinity and density). We then test the performance of the upgraded COART model using three physically-based modeling approaches to simulate the spectral albedo and transmittance of sea ice, and compare them with measurements collected during the Impacts of Climate on the Ecosystems and Chemistry of the Arctic Pacific Environment (ICESCAPE) and the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaigns. The observations are adequately simulated when at least three layers are used to represent bare ice, including a thin surface scattering layer (SSL), and two layers to represent ponded ice. Treating the SSL as a low-density ice layer yields better model-observation agreement than treating it as a snow-like layer. Sensitivity results indicate that air volume (which determines the ice density) has the largest impact on the simulated fluxes. The vertical profile of density drives the optical properties but available measurements are scarce. The approach where the scattering coefficient for the bubbles is inferred in lieu of density leads to essentially equivalent modeling results. For ponded ice, the albedo and transmittance in the visible are mainly determined by the optical properties of the ice underlying the water layer. Possible contamination from light-absorbing impurities, such as black carbon or ice algae, is also implemented in the model and is able to effectively reduce the albedo and transmittance in the visible spectrum to further improve the model-observation agreement.

A novel upgraded molecular mechanics framework with considering the length scale parameter and its advantages in analyzing the behavior of nanostructures

A novel upgraded molecular mechanics framework has been developed by improving the flexural and torsional behavior in bonds between atoms based on modified couple stress theory (MCS-MM). The MCS-MM approach drives a considerable advancement in predicting the mechanical behavior of nanostructures. Due to applying the modified couple stress theory in the beam energy equations, the length scale parameter is added to the equations. The presented method provides a better prediction for the elastic and geometric properties of the equivalent beams in the atomic structure. In order to show the efficiency of this upgraded proposed model, various aspects of graphene sheets were considered, such as energy deviation, mechanical properties, buckling behavior, and crack opening displacement (COD). The results confirm the efficiency and accuracy of the model and are consistent with molecular dynamics modeling. The MCS MM method leads to a significant improvement in strain energy deviation. It predicts the elastic properties and mechanical behavior of graphene sheets more accurately. The MCS-MM method can be replaced with previous ones as an appropriate approach.

Origin and Extent of the Opacity Challenge for Atmospheric Retrievals of WASP-39 b

As the James Webb Space Telescope (JWST) came online last summer, we entered a new era of astronomy. This new era is supported by data products of unprecedented information content that require novel reduction and analysis techniques. Recently, Niraula et al. (N22) highlighted the need for upgraded opacity models to prevent facing a model-driven accuracy wall when interpreting exoplanet transmission spectra. Here, we follow the same approach as N22 to explore the sensitivity of inferences on the atmospheric properties of WASP-39 b to the opacity models used. We find that the retrieval of the main atmospheric properties from this first JWST exoplanet spectrum is mostly unaffected by the current state of the community’s opacity models. Abundances of strong opacity sources like water and carbon dioxide are reliably constrained within ∼0.30 dex, beyond the 0.50 dex accuracy wall reported in N22. Assuming the completeness and accuracy of line lists, N22's accuracy wall is primarily driven by model uncertainties on broadening coefficients and far-wing behaviors, which we find to have marginal consequences for interpreting the transmission spectra of large, hot, high-metallicity atmospheres such as WASP-39 b, in opposition to emission spectra and climate modeling, which depend on deeper parts of a planetary atmosphere. The origin of the opacity challenge in the retrieval of metal-rich hot Jupiters via transmission spectroscopy will thus mostly stem from the incompleteness and inaccuracy of line lists.

Group decision-making with multiplicative probabilistic linguistic preference relations based on consistency improvement and upgraded multiplicative DEA cross-efficiency

Multiplicative probabilistic linguistic preference relation (MPLPR) has been widely used by decision-makers (DMs) to tackle group decision-making (GDM) problems. However, due to the complexity of the decision-making circumstance and individual subjectivity of DMs, they often provide inconsistent MPLPRs which often lead to unreasonable decision results. To solve this problem, this paper investigates a novel approach to GDM with MPLPRs based on consistency improvement and upgraded multiplicative data envelopment analysis (DEA) cross-efficiency. First, the concept of sequential consistency of MPLPR is defined. Then, a consistency improvement algorithm is proposed, which can convert any unacceptable consistent MPLPR into an acceptable one. Furthermore, we use geometric averages to transform MPLPR into multiplicative preference relation (MPR). Meanwhile, considering the conservative psychology of DMs, an upgraded multiplicative DEA cross-efficiency model based on the pessimistic criterion is constructed, which can derive the priority vector of MPLPR. Therefore, we can obtain the rational ranking results for all alternatives. Finally, a case analysis of emergency logistics under COVID-19 is provided to illustrate the validity and applicability of the proposed approach.

A crowd clustering prediction and captioning technique for public health emergencies.

The COVID-19 pandemic has come to the end. People have started to consider how quickly different industries can respond to disasters due to this public health emergency. The most noticeable aspect of the epidemic regarding news text generation and social issues is detecting and identifying abnormal crowd gatherings. We suggest a crowd clustering prediction and captioning technique based on a global neural network to detect and caption these scenes rapidly and effectively. We superimpose two long convolution lines for the residual structure, which may produce a broad sensing region and apply our model's fewer parameters to ensure a wide sensing region, less computation, and increased efficiency of our method. After that, we can travel to the areas where people are congregating. So, to produce news material about the present occurrence, we suggest a double-LSTM model. We train and test our upgraded crowds-gathering model using the ShanghaiTech dataset and assess our captioning model on the MSCOCO dataset. The results of the experiment demonstrate that using our strategy can significantly increase the accuracy of the crowd clustering model, as well as minimize MAE and MSE. Our model can produce competitive results for scene captioning compared to previous approaches.

DeepNeo: a webserver for predicting immunogenic neoantigens.

Non-self epitopes, whether originated from foreign substances or somatic mutations, trigger immune responses when presented by major histocompatibility complex (MHC) molecules and recognized by T cells. Identification of immunogenically active neoepitopes has significant implications in cancer and virus medicine. However, current methods are mostly limited to predicting physical binding of mutant peptides and MHCs. We previously developed a deep-learning based model, DeepNeo, to identify immunogenic neoepitopes by capturing the structural properties of peptide-MHC pairs with T cell reactivity. Here, we upgraded our DeepNeo model with up-to-date training data. The upgraded model (DeepNeo-v2) was improved in evaluation metrics and showed prediction score distribution that better fits known neoantigen behavior. The immunogenic neoantigen prediction can be conducted at https://deepneo.net.

Positive family history as a predictor for disease outcomes after radical prostatectomy for nonmetastatic prostate cancer

ABSTRACT Background While family history (FHx) of prostate cancer (PCa) increases the risk of PCa, comparably less is known regarding the impact of FHx on pathologic and oncologic outcomes after radical prostatectomy (RP). Methods We retrospectively reviewed our multicenter database comprising 6,041 nonmetastatic PCa patients treated with RP. Patients with a FHx of PCa in one or more first-degree relatives were considered as FHx positive. We examined the association of FHx with pathologic outcomes and biochemical recurrence (BCR) using logistic and Cox regression models, respectively. Results In total, 1,677 (28%) patients reported a FHx of PCa. Compared to patients without FHx, those with, were younger at RP (median age of 59 vs. 62 years, p < 0.01), and had significantlymore favorable biopsy and RP histopathologic findings. On multivariable logistic regression analysis, positive FHx was associated with extracapsular extension (odds ratio [OR] 0.77, 95% confidence interval [CI] 0.66–0.90, p < 0.01; model AUC 0.73) and upgrading (OR 0.70, 95% CI 0.62–0.80, p < 0.01; model AUC 0.68). Incorporating FHx significantly improved the AUC of the base model for upgrading (p < 0.01). Positive FHx was not associated with BCR in pre- and postoperative multivariable models (p = 0.1 and p = 0.7); c-indexes of Cox multivariable models were: 0.73 and 0.82, respectively. Conclusions We found that patients with clinically nonmetastatic PCa who have positive FHx of PCa undergo RP at a younger age and have more favorable pathologic outcomes. Nevertheless, FHx of PCa did not confer better BCR rates, suggesting that FHx leads to potentially early detection and treatment without impact on BCR.

Comments: AI Language Tools Hit the Books . . . and Technical Content?

Are you among the millions of ChatGPT users/experimenters around the world? Or impatiently parked on the waitlist? OpenAI launched ChatGPT in late November 2022 with little fanfare. Within a week, the company reported 1 million users. Within the first month, it exceeded 57 million users. Since then, it has been updated several times and a UBS analyst estimated in February that the artificial intelligence (AI) language tool reached 100 million users. The website exceeded 13 million daily visitors as of January. On 1 February, OpenAI announced a ChatGPT Plus monthly subscription plan to give priority access to the chatbot. Clearly, AI language tools are fulfilling the needs and/or satisfying the curiosity of novices and more experienced AI users. Microsoft took notice and in January confirmed the extension of its partnership with OpenAI, an investment rumored to be $10 billion (not confirmed by Microsoft). Microsoft Azure will also continue as the exclusive cloud provider for the tool since OpenAI uses Azure to train its models. ChatGPT (generative pretrained transformer) is not the only AI tool available. Microsoft upgraded its Bing AI search engine, which is powered by an upgraded model of ChatGPT. ChatSonic, also built on top of ChatGPT, can access the internet. Jasper Chat, based on GPT 3.5 with OpenAI as its partner, was built for advertising and marketing businesses. Google Bard is an experimental conversational AI service powered by Google’s own next-generation language model. Character AI revolves around the concept of personas, trained with conversations in mind. Users choose from various personalities (e.g., Elon Musk, Tony Stark, Socrates, US President Biden, Kayne West, etc.) vs. interacting with a single AI chatbot. YouChat is built into a search engine and trained on a ChatGPT model. It holds conversations with full access to the internet. Caktus AI, not available as a free service, is aimed toward students and touted by the company as “the first-ever educational AI tool.” It helps with student content ranging from essays to writing paragraphs and extends to discussions, questions, and coding. There’s also a bot for programmers, GitHub Copilot X, which suggests and completes code and functions in real time. There are many more options and sure to be plenty more developed with specific user types in mind (e.g., technical specialties, business communications, conversational, special interests). All will have their own pros and cons. A retired systems engineer, formerly with General Dynamics Canada, Rob Miller, wrote a column in Canada’s National Observer about his experience using ChatGPT as a research tool for a story about carbon capture and storage—he gave a mixed review. As AI tools gain attention and provide assistance to a wide range of authors and researchers, SPE recently announced its policy for authors who use the tools to generate content for their papers. AI-generated content may be used within SPE publications, but under specific conditions. Open-source libraries, such as those used in ChatGPT and others, are not fail-safe solutions, as evidenced on 20 March when OpenAI took the chatbot offline due to a bug “which allowed some users to see titles from another active user’s chat history,” according to the company. “It’s also possible that the first message of a newly created conversation was visible in someone else’s chat history if both users were active around the same time.” OpenAI also discovered that “the same bug may have caused the unintentional visibility of payment-related information of 1.2% of the ChatGPT Plus subscribers who were active during a specific 9-hour window. … it was possible for some users to see another active user’s first and last name, email address, payment address, the last four digits (only) of a credit card number, and credit card expiration date.” I’d like to hear about your experience with ChatGPT or any of the other options. How did you use it? What type of content were you generating? If you were aiming for assistance with technical content related to oil/gas/energy, how useful was the chatbot? Share your comments and pros and cons at JPT Comments.

Separation of aliphatic amino acids from aqueous media with an aminophosphonic ion exchanger

The sorption of the aliphatic amino acids glycine and α-alanine by an aminophosphonic ion exchanger is studied and the dynamics of sorption is described using a kinetic equation. Experimental studies were carried out on a designed experimental setup with a fixed ion exchanger layer, in which the purified and regenerating solutions are passed through the sorbent layer in various directions. The heterogeneous ion exchange process includes the transport of sorbent ions in the liquid phase to the grain surface and the removal of desorbed ions from it, interphase transfer, diffusion of sorbed and desorbed ions inside the grain, since not all functional groups of the sorbent are localized on the surface, and a reversible ion exchange reaction. The kinetic equation of the Thomas model takes into account the multistage sorption and adequately describes the dependence of the degree of extraction of the component on the duration of contact of the solution with the ion exchanger layer. The equation of the Thomas model has been modernized taking into account the effect on the dynamics of the process of diffusion resistances in the channels of the layer and the grains of the ion exchanger in a column with a fixed loading. The upgraded model is applied to describe the dynamics of ion exchange of aliphatic amino acids on an aminophosphonic ion exchanger and the possibility of using a one-dimensional capillary flow model to estimate the diffusion resistance during fluid movement in the channels of the ion exchanger layer is shown. The agreement of the calculated and experimental output curves of sorption of aliphatic amino acids from aqueous solutions of various concentrations has been verified. It is shown that the upgraded model adequately describes the dependence of the degree of amino acid extraction on the duration of contact of the solution with the polyampholite layer at different feed rates of the purified solution.

Local and tele-coupling development between carbon emission and ecologic environment quality

Carbon emissions (CE) have a profound impact on the natural environment of the surface, and the ecologic environment quality (EEQ) affects the conversion and absorption of CE. CE and EEQ have an interaction and mutual influence relationship. The connection between them includes not only local interactions but also the long-range distance of external systems. It is critical to measure CE and EEQ and coordinate their association in order to increase the effectiveness of CE and EEQ collaborative governance. However, the traditional remote sensing ecological index (RSEI) model ignores the importance of air quality. Furthermore, there is limited current research on the local and telecoupling relationship between CE and the EEQ. To fill these research gaps, this paper first used night light data to dynamically monitor CE. Then we proposed an upgraded RSEI model (RSEInew) to assess EEQ. Finally, the local and telecoupling coordination degree (LTCCD) model was suggested to further investigate their relationship in the Yangtze River Economic Belt (YREB) from 2000 to 2019. The results showed that: (1) Total CE in the YREB increased from 837.5738 million tons to 4757.3794 million tons between 2000 and 2019 but has not yet reached its peak. Jiangsu and Zhejiang are at the forefront in terms of CE and have huge emission reduction potential. (2) The YREB's overall EEQ has deteriorated over the last 20 years. The average RSEInew value ranged between 0.52 and 0.71, with “medium” and “good” EEQ levels dominating. (3) The LTCCD of CE and EEQ in the YREB has increased year by year. This study can deliver a reference for decision-makers to synergistically formulate carbon emission reduction plans and environmental protection schemes.

Upgraded waveform model of eccentric binary black hole based on effective-one-body-numerical-relativity for spin-aligned binary black holes

Effective one body numerical relativity waveform models for spin-aligned binary black holes (SEOBNR) are based on the effective one body theoretical framework and numerical relativity simulation results. SEOBNR models have evolved through version 1 to version 4. We recently extended SEOBNRv1 model to SEOBNRE (Effective One Body Numerical Relativity waveform models for Spin-aligned binary black holes along Eccentric orbit) model which is also valid for spin-aligned binary black hole coalescence along eccentric orbit. In this paper, we update our previous SEOBNRE model to make it consistent to SEOBNRv4 which is the most widely used SEOBNR waveform model. This upgraded SEOBNRE model improves accuracy compared to previous SEOBNRE model, especially for highly spinning black holes. For spin-aligned binary black holes with mass ratio [Formula: see text], dimensionless spin [Formula: see text] and orbital eccentricity [Formula: see text] at reference frequency [Formula: see text] ([Formula: see text] is the total mass of the binary black hole, [Formula: see text]Hz), the upgraded SEOBNRE model can always fit numerical relativity waveform better than 98.2%. For most cases, the fitting factor can even be better than 99%.

Integrated Control for Mixed CAV and CV Traffic Flow in Expressway Merge Zones Combined with Variable Speed Limit, Ramp Metering, and Lane Changing

In the conventional human driving environment, due to poor traffic information transmission and the inability to actively control vehicles, variable speed limits (VSLs) and ramp metering (RM) strategies often fail to achieve the desired results when dealing with the merging zone of an expressway under high traffic demand. To improve traffic efficiency, an integrated control method for traffic flow in the merging area of expressways is proposed by combining connected and autonomous vehicle (CAV) active lane change (LC) technology with traditional VSL and entrance RM strategies. This method uses an upgraded cell transmission model (CTM) to estimate the state of the mainline and ramp input traffic volume and then uses the genetic algorithm (GA) to solve for the optimal combination of the mainline speed limit, number of mainline vehicles changing lanes, and ramp regulation under maximized flow in the merging zone. This integrated control strategy incorporating VSL, LC, and RM (VSL-LC-RM) enables synergistic control of the mainline and ramp traffic entering the merging zone in time-space. Finally, simulation experiments are conducted with the mainline two-lane and on-ramp single-lane expressway merging zone scenarios. The results show that compared with the VSL strategy and the strategy incorporating only VSL and RM (VSL-RM), the proposed VSL-LC-RM strategy can enhance the regulation of traffic flow, improve the flow of merging bottlenecks, and make full use of the road space-time resources.