Insight into the experiment and extraction mechanism for separating carbazole from anthracene oil with quaternary ammonium-based deep eutectic solvents

Carbazole is an irreplaceable basic organic chemical raw material and intermediate in industry.The separation of carbazole from anthracene oil by environmental benign solvents is important but still a challenge in chemical engineering.Deep eutectic solvents (DESs) as a sustainable green separation solvent have been proposed for the separation of carbazole from model anthracene oil.In this research,three quaternary ammonium-based DESs were prepared using ethylene glycol (EG) as hydrogen bond donor and tetrabutylammonium chloride (TBAC),tetrabutylammonium bromide or choline chloride as hydrogen bond acceptors.To explore their extraction performance of carbazole,the conductor-like screening model for real solvents (COSMO-RS) model was used to predict the activity coefficient at infinite dilution (γ^(∞)) of carbazole in DESs,and the result indicated TBAC:EG (1:2) had the stronger extraction ability for carbazole due to the higher capacity at infinite dilution (C^(∞)) value.Then,the separation performance of these three DESs was evaluated by experiments,and the experimental results were in good agreement with the COSMO-RS prediction results.The TBAC:EG (1:2) was determined as the most promising solvent.Additionally,the extraction conditions of TBAC:EG (1:2) were optimized,and the extraction efficiency,distribution coefficient and selectivity of carbazole could reach up to 85.74%,30.18 and 66.10%,respectively.Moreover,the TBAC:EG (1:2) could be recycled by using environmentally friendly water as antisolvent.In addition,the separation performance of TBAC:EG (1:2) was also evaluated by real crude anthracene,the carbazole was obtained with purity and yield of 85.32%,60.27%,respectively.Lastly,the extraction mechanism was elucidated byσ-profiles and interaction energy analysis.Theoretical calculation results showed that the main driving force for the extraction process was the hydrogen bonding ((N–H...Cl) and van der Waals interactions (C–H...O and C–H...π),which corresponding to the blue and green isosurfaces in IGMH analysis.This work presented a novel method for separating carbazole from crude anthracene oil,and will provide an important reference for the separation of other high value-added products from coal tar.

Deep learning approaches to recover the plasma current density profile from the safety factor based on Grad–Shafranov solutions across multiple tokamaks

Many magnetohydrodynamic stability analyses require generation of a set of equilibria with a fixed safety factor q-profile while varying other plasma parameters.A neural network(NN)-based approach is investigated that facilitates such a process.Both multilayer perceptron(MLP)-based NN and convolutional neural network(CNN)models are trained to map the q-profile to the plasma current density J-profile,and vice versa,while satisfying the Grad–Shafranov radial force balance constraint.When the initial target models are trained,using a database of semianalytically constructed numerical equilibria,an initial CNN with one convolutional layer is found to perform better than an initial MLP model.In particular,a trained initial CNN model can also predict the q-or J-profile for experimental tokamak equilibria.The performance of both initial target models is further improved by fine-tuning the training database,i.e.by adding realistic experimental equilibria with Gaussian noise.The fine-tuned target models,referred to as fine-tuned MLP and fine-tuned CNN,well reproduce the target q-or J-profile across multiple tokamak devices.As an important application,these NN-based equilibrium profile convertors can be utilized to provide a good initial guess for iterative equilibrium solvers,where the desired input quantity is the safety factor instead of the plasma current density.

Toward understanding the role of genomic repeat elements in neurodegenerative diseases

Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

Role of the globus pallidus in motor and non-motor symptoms of Parkinson's disease

The globus pallidus plays a pivotal role in the basal ganglia circuit. Parkinson's disease is characterized by degeneration of dopamine-producing cells in the substantia nigra, which leads to dopamine deficiency in the brain that subsequently manifests as various motor and non-motor symptoms. This review aims to summarize the involvement of the globus pallidus in both motor and non-motor manifestations of Parkinson's disease. The firing activities of parvalbumin neurons in the medial globus pallidus, including both the firing rate and pattern, exhibit strong correlations with the bradykinesia and rigidity associated with Parkinson's disease. Increased beta oscillations, which are highly correlated with bradykinesia and rigidity, are regulated by the lateral globus pallidus. Furthermore,bradykinesia and rigidity are strongly linked to the loss of dopaminergic projections within the cortical-basal ganglia-thalamocortical loop. Resting tremors are attributed to the transmission of pathological signals from the basal ganglia through the motor cortex to the cerebellum-ventral intermediate nucleus circuit. The cortico–striato–pallidal loop is responsible for mediating pallidi-associated sleep disorders. Medication and deep brain stimulation are the primary therapeutic strategies addressing the globus pallidus in Parkinson's disease. Medication is the primary treatment for motor symptoms in the early stages of Parkinson's disease, while deep brain stimulation has been clinically proven to be effective in alleviating symptoms in patients with advanced Parkinson's disease,particularly for the movement disorders caused by levodopa. Deep brain stimulation targeting the globus pallidus internus can improve motor function in patients with tremordominant and non-tremor-dominant Parkinson's disease, while deep brain stimulation targeting the globus pallidus externus can alter the temporal pattern of neural activity throughout the basal ganglia–thalamus network. Therefore, the composition of the globus pallidus neurons, the neurotransmitters that act on them, their electrical activity,and the neural circuits they form can guide the search for new multi-target drugs to treat Parkinson's disease in clinical practice. Examining the potential intra-nuclear and neural circuit mechanisms of deep brain stimulation associated with the globus pallidus can facilitate the management of both motor and non-motor symptoms while minimizing the side effects caused by deep brain stimulation.

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.

Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

Fracture identification of carbonate reservoirs by deep forest model:An example from the D oilfield in Zagros Basin

Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.

Dynamic Location Method for Shallow Ocean Bottom Nodes Using the Levenberg-Marquart Algorithm

Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

Harnessing machine learning tools for water quality assessment in the Kebili shallow aquifers,Southwestern Tunisia

An integrated method that implements multivariate statistical analysis and ML methods to evaluate groundwater quality of the shallow aquifers of the Djerid and Kebili district,Southern Tunisia,was adopted.An evaluation of their suitability for irrigation and/or drinking purposes is necessary.A comprehensive hydrochemical assessment of 52 samples with entropy weighted water quality index(EWQI)was also proposed.Eleven water parameters were calculated to ascertain the potential use of those resources in irrigation and drinking.Multivariate analysis showed two main components with Dim1(variance=62.3%)and Dim.2(variance=22%),due to the bicarbonate,dissolution,and evaporation and the intrusion of drainage water.The evaluation of water quality has been carried out using EWQI model.The calculated EWQI for the Djerid and Kebili waters(i.e.,52 samples)varied between 7.5 and 152.62,indicating a range of 145.12.A mean of 79.12 was lower than the median(88.47).From the calculation of EWQI,only 14 samples are not suitable for irrigation because of their poor to extremely poor quality(26.92%).The bivariate plot showed high correlation for EWQI~TH(r=0.93),EWQI~SAR(r=0.87),indicating that water quality depended on those parameters.Diff erent ML algorithms were successfully applied for the water quality classifi cation.Our results indicated high prediction accuracy(SVM>LDA>ANN>kNN)and perfect classifi cation for kNN,LDA and Naive Bayes.For the purposes of developing the prediction models,the dataset was divided into two groups:training(80%)and testing(20%).To evaluate the models’performance,RMSE,MSE,MAE and R^(2) metrics were used.kNN(R^(2)=0.9359,MAE=6.49,MSE=79.00)and LDA(accuracy=97.56%;kappa=96.21%)achieved high accuracy.Moreover,linear regression indicated high correlation for both training(R^(2)=0.9727)and testing data(0.9890).This well confi rmed the validity of LDA algorithm in predicting water quality.Cross validation showed a high accuracy(92.31%),high sensitivity(89.47%)and high specifi city(95%).These fi ndings are fundamentally important for an integrated water resource management in a larger context of sustainable development of the Kebili district.

Dynamic impact properties of deep sandstone under thermal-hydraulicmechanical coupling loads

The deep rock mass within coal mines situated in a challenging environment are characterized by high ground stress,high geotemperature,high osmotic water pressure,and dynamic disturbances from mechanical excavation.To investigate the impact of this complex mechanical environment on the dynamic characteristics of roof sandstone in self-formed roadways without coal pillars,standard specimens of deep sandstone from the 2611 upper tunnel working face of the Yongmei Company within the Henan Coal Chemical Industry Group in Henan,China were prepared,and an orthogonal test was designed.Using a self-developed geotechnical dynamic impact mechanics test system,triaxial dynamic impact tests under thermal-hydraulicmechanical coupling conditions were conducted on deep sandstone.The results indicate that under high confining pressure,deep sandstone exhibits pronounced brittle failure at low temperatures,with peak strength gradually decreasing as temperature and osmotic water pressure increase.Conversely,under low confining pressure and low temperature,the brittleness of deep sandstone weakens gradually,while ductility increases.Moreover,sandstone demonstrates higher peak strength at low temperatures under high axial pressure conditions,lower peak strength at high temperatures,and greater strain under low axial pressure and high osmotic water pressure.Increases in impact air pressure and osmotic water pressure have proportionally greater effects on peak stress and peak strain.Approximately 50%of the input strain energy is utilized as effective energy driving the sandstone fracture process.Polar analysis identifies the optimal combination of factors affecting the peak stress and peak strain of sandstone.Under the coupling effect,intergranular and transgranular fractures occur within the sandstone.SEM images illustrate that the damage forms range from minor damage with multiple fissures to extensive fractures and severe fragmentation.This study elucidates the varied dynamic impact mechanical properties of deep sandstones under thermal-hydraulic-mechanical coupling,along with multifactor analysis methods and their optimal factor combinations.

A Deep Learning Approach to Shape Optimization Problems for Flexoelectric Materials Using the Isogeometric Finite Element Method

A new approach for flexoelectricmaterial shape optimization is proposed in this study.In this work,a proxymodel based on artificial neural network(ANN)is used to solve the parameter optimization and shape optimization problems.To improve the fitting ability of the neural network,we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training.The isogeometric analysis-finite element method(IGA-FEM)is used to discretize the flexural theoretical formulas and obtain samples,which helps ANN to build a proxy model from the model shape to the target value.The effectiveness of the proposed method is verified through two numerical examples of parameter optimization and one numerical example of shape optimization.

Deep near infrared light-excited stable synergistic photodynamic and photothermal therapies based on P-IR890 nano-photosensitizer constructed via a non-cyanine dye

The cyanine dyes represented by IR780 can achieve synergistic photodynamic therapy(PDT)and photothermal therapy(PTT)under the stimulation of near-infrared(NIR)light(commonly 808 nm).Unfortunately,the stability of NIR-excited cyanine dyes is not satisfactory.These cyanine dyes can be attacked by self-generated reactive oxygen species(ROS)during PDT processes,resulting in structural damage and rapid degradation,which is fatal for phototherapy.To address this issue,a novel non-cyanine dye(IR890)was elaborately designed and synthesized by our team.The maximum absorption wavelength of IR890 was located in the deep NIR region(ca.890 nm),which was beneficial for further improving tissue penetration depth.Importantly,IR890 exhibited good stability when continuously illuminated by deep NIR light.To improve the hydrophilicity and biocompatibility,the hydrophobic IR890 dye was grafted onto the side chain of hydrophilic polymer(POEGMA-b-PGMA-g-C≡CH)via click chemistry.Then,the synthesized POEGMA-b-PGMA-g-IR890 amphiphilic polymerwas utilized to prepare P-IR890 nano-photosensitizer via self-assembly method.Under irradiation with deep NIR light(850 nm,0.5 W/cm^(2),10 min),the dye degradation rate of P-IR890 was less than 5%.However,IR780 was almost completely degraded with the same light output power density and irradiation duration.In addition,P-IR890 could stably generate a large number of ROS and heat at the same time.It was rarely reported that the stable synergistic combination therapy of PDT and PTT could be efficiently performed by a single photosensitizer via irradiation with deep NIR light.P-IR890 exhibited favorable anti-tumor outcomes through apoptosis pathway.Therefore,the P-IR890 could provide a new insight into the design of photosensitizers and new opportunities for synergistic combination therapy of PDT and PTT.

Malnutrition Assessed Using the Geriatric Nutritional Risk Index Is Associated with Preoperative Incidence of Deep Vein Thrombosis in Japanese Patients Undergoing Total Knee Arthroplasty

Purpose: Few studies have evaluated the association between malnutrition and the risk of preoperative deep vein thrombosis (DVT) in patients undergoing primary total joint arthroplasty. This study aimed to investigate the prevalence of preoperative DVT in Japanese patients undergoing total knee arthroplasty (TKA) and the importance of malnutrition in the risk of preoperative DVT. Methods: We retrospectively analyzed 394 patients admitted for primary TKA at our institution between January 2019 and December 2023. All patients scheduled for TKA at our institution had serum D-dimer levels measured preoperatively. Lower-limb ultrasonography was examined to confirm the presence of DVT in patients with D-dimer levels ≥ 1.0 µg/mL or who were considered to be at high risk of DVT by the treating physician. Based on the results of lower-limb ultrasonography, all patients were divided into the non-DVT and DVT groups. The incidence of and risk factors for preoperative DVT were investigated, as well as the correlation of DVT with the patient’s nutritional parameters. We used two representative tools for nutritional assessment: the Geriatric Nutritional Risk Index (GNRI) and Controlling Nutritional Status Score. Results: The mean age was 77.8 ± 6.9 years. Preoperative DVT was diagnosed in 57 of the 394 (14.5%) patients. Multivariate logistic regression analysis showed that advanced age and malnutrition status, assessed using the GNRI, were independent risk factors for preoperative DVT. Conclusion: A high incidence of preoperative DVT was observed in patients who underwent TKA. Malnutrition status, as assessed using the GNRI, increased the risk of preoperative DVT. Our findings suggest that clinicians should consider these factors when tailoring preventive strategies to mitigate DVT risk in patients undergoing TKA.

Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors

BACKGROUND Gastrointestinal stromal tumors(GIST)are prevalent neoplasm originating from the gastrointestinal mesenchyme.Approximately 50%of GIST patients experience tumor recurrence within 5 years.Thus,there is a pressing need to accurately evaluate risk stratification preoperatively.AIM To assess the application of a deep learning model(DLM)combined with computed tomography features for predicting risk stratification of GISTs.METHODS Preoperative contrast-enhanced computed tomography(CECT)images of 551 GIST patients were retrospectively analyzed.All image features were independently analyzed by two radiologists.Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy.Patients were randomly assigned to the training(n=386)and validation cohorts(n=165).A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort.RESULTS Among the analyzed CECT image features,tumor size,ulceration,and enlarged feeding vessels were identified as significant risk predictors(P<0.05).In DLM,the overall area under the receiver operating characteristic curve(AUROC)was 0.88,with the accuracy(ACC)and AUROCs for each stratification being 87%and 0.96 for low-risk,79%and 0.74 for intermediate-risk,and 84%and 0.90 for high-risk,respectively.The overall ACC and AUROC were 84%and 0.94 in the combined model.The ACC and AUROCs for each risk stratification were 92%and 0.97 for low-risk,87%and 0.83 for intermediate-risk,and 90%and 0.96 for high-risk,respectively.Differences in AUROCs for each risk stratification between the two models were significant(P<0.05).CONCLUSION A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data,demonstrating superiority compared to DLM.

Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.

Monitoring Sea Fog over the Yellow Sea and Bohai Bay Based on Deep Convolutional Neural Network

In this paper,we utilized the deep convolutional neural network D-LinkNet,a model for semantic segmentation,to analyze the Himawari-8 satellite data captured from 16 channels at a spatial resolution of 0.5 km,with a focus on the area over the Yellow Sea and the Bohai Sea(32°-42°N,117°-127°E).The objective was to develop an algorithm for fusing and segmenting multi-channel images from geostationary meteorological satellites,specifically for monitoring sea fog in this region.Firstly,the extreme gradient boosting algorithm was adopted to evaluate the data from the 16 channels of the Himawari-8 satellite for sea fog detection,and we found that the top three channels in order of importance were channels 3,4,and 14,which were fused into false color daytime images,while channels 7,13,and 15 were fused into false color nighttime images.Secondly,the simple linear iterative super-pixel clustering algorithm was used for the pixel-level segmentation of false color images,and based on super-pixel blocks,manual sea-fog annotation was performed to obtain fine-grained annotation labels.The deep convolutional neural network D-LinkNet was built on the ResNet backbone and the dilated convolutional layers with direct connections were added in the central part to form a string-and-combine structure with five branches having different depths and receptive fields.Results show that the accuracy rate of fog area(proportion of detected real fog to detected fog)was 66.5%,the recognition rate of fog zone(proportion of detected real fog to real fog or cloud cover)was 51.9%,and the detection accuracy rate(proportion of samples detected correctly to total samples)was 93.2%.

Combined Optimal Dispatch of Thermal Power Generators and Energy Storage Considering Thermal Power Deep Peak Clipping and Wind Energy Emission Grading Punishment

Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing,which affects the stabilization of the PS(power system).This paper suggests integrated optimal dispatching of thermal power generators and BESS(battery energy storage system)taking wind energy emission grading punishment and deep peak clipping into consideration.Firstly,in order to minimize wind abandonment,a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced,and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system.Secondly,considering BESS and thermal power,the management approach of BESS-assisted virtual peak clipping of thermal power generators is aimed at reducing the degree of deep peak clipping of thermal power generators and optimizing the output of thermal power generators and the charging and discharging power of BESS.Finally,Give an example of how this strategy has been effective in reducing abandonment rates by 0.66% and 7.46% individually for different wind penetration programs,and the daily average can reduce the peak clipping power output of thermal power generators by 42.97 and 72.31 MWh and enhances the effect and economy of system peak clipping.

Developing a novel big dataset and a deep neural network to predict the bearing capacity of a ring footing

The accurate prediction of the bearing capacity of ring footings,which is crucial for civil engineering projects,has historically posed significant challenges.Previous research in this area has been constrained by considering only a limited number of parameters or utilizing relatively small datasets.To overcome these limitations,a comprehensive finite element limit analysis(FELA)was conducted to predict the bearing capacity of ring footings.The study considered a range of effective parameters,including clay undrained shear strength,heterogeneity factor of clay,soil friction angle of the sand layer,radius ratio of the ring footing,sand layer thickness,and the interface between the ring footing and the soil.An extensive dataset comprising 80,000 samples was assembled,exceeding the limitations of previous research.The availability of this dataset enabled more robust and statistically significant analyses and predictions of ring footing bearing capacity.In light of the time-intensive nature of gathering a substantial dataset,a customized deep neural network(DNN)was developed specifically to predict the bearing capacity of the dataset rapidly.Both computational and comparative results indicate that the proposed DNN(i.e.DNN-4)can accurately predict the bearing capacity of a soil with an R2 value greater than 0.99 and a mean squared error(MSE)below 0.009 in a fraction of 1 s,reflecting the effectiveness and efficiency of the proposed method.

Characteristics and Trends of Deep Oil and Gas Research in China (1984-2024)—Research from the Perspective of CiteSpace

Deep oil and gas refer to oil and gas resources buried at a significant depth below the surface. Compared with conventional oil and gas, deep oil and gas often face more complex geological conditions and technological challenges, therefore, the development and exploitation of these oil and gas resources require advanced technology and equipment. Use bibliometrics to study academic literature. Select available data and download it in “RefWorks” format. Import the data into Cite Space 6.3.R2 software for author collaboration and keyword emergence analysis and visualization. Use Microsoft Excel 2016 software to analyze the annual publication volume, literature institutions, and disciplinary distribution of domestic and international scholarly literature. Research has found that: 1) The institution with the highest number of publications in the field of deep oil and gas in China is the China Petroleum Exploration and Development Research Institute;The author with the highest number of publications is Zhu Guangyou;The author with the highest citation frequency is Jia Chengzao;The research work in the field of deep oil and gas in China is mainly led by national level fund projects. 2) The research hot-spots of deep oil and gas in China are showing a trend of shifting from Jilin and Henan to Xinjiang and Sichuan. 3) The research on deep oil and gas fields in the Paleogene of China is mainly concentrated in Henan Province and Shandong Province. The Lower Tertiary, Cambrian and Jurassic are respectively concentrated in Dongpu Sag, Dongying Sag, Sichuan Basin, Tarim Basin in Xinjiang, the Junggar Basin and Qaidam Basin in Qinghai. The Sinian, Ordovician, Cretaceous, and Neogene systems are mainly concentrated in Sichuan, Xinjiang, and Qinghai provinces. The Permian system is mainly located in the southwest and Northwest of China. This article uses a new research perspective and methodology to systematically analyze the current situation and future development trends of deep oil and gas exploration and development in China, which is of great significance for promoting effective exploration and development of deep oil and gas resources.