Characteristics and main controlling factors of intra-platform shoal thin-layer dolomite reservoirs:A case study of Middle Permian Qixia Formation in Gaoshiti-Moxi area of Sichuan Basin,SW China

Based on the study of the distribution of intra-platform shoals and the characteristics of dolomite reservoirs in the Middle Permian Qixia Formation in the Gaoshiti–Moxi area of the Sichuan Basin,SW China,the controlling factors of reservoir development were analyzed,and the formation model of“intra-platform shoal thin-layer dolomite reservoir”was established.The Qixia Formation is a regressive cycle from bottom to top,in which the first member(Qi1 Member)develops low-energy open sea microfacies,and the second member(Qi2 Member)evolves into intra-platform shoal and inter-shoal sea with decreases in sea level.The intra-platform shoal is mainly distributed near the top of two secondary shallowing cycles of the Qi2 Member.The most important reservoir rock of the Qixia Formation is thin-layer fractured-vuggy dolomite,followed by vuggy dolomite.The semi-filled saddle dolomite is common in fracture-vug,and intercrystalline pores and residual dissolution pores combined with fractures to form the effective pore-fracture network.Based on the coupling analysis of sedimentary and diagenesis characteristics,the reservoir formation model of“pre-depositional micro-paleogeomorphology controlling shoal,sedimentary shoal controlling dolomite,penecontemporaneous dolomite benefiting preservation of pores,and late hydrothermal action effectively improving reservoir quality”was systematically established.The“first-order high zone”micro-paleogeomorphology before the deposition of the Qixia Formation controlled the development of large area of intra-platform shoals in Gaoshiti area during the deposition of the Qi2 Member.Shoal facies is the basic condition of early dolomitization,and the distribution range of intra-platform shoal and dolomite reservoir is highly consistent.The grain limestone of shoal facies is transformed by two stages of dolomitization.The penecontemporaneous dolomitization is conducive to the preservation of primary pores and secondary dissolved pores.The burial hydrothermal fluid enters the early dolomite body along the fractures associated with the Emeishan basalt event,makes it recrystallized into medium–coarse crystal dolomite.With the intercrystalline pores and the residual vugs after the hydrothermal dissolution along the fractures,the high-quality intra-platform shoal-type thin-layer dolomite reservoirs are formed.The establishment of this reservoir formation model can provide important theoretical support for the sustainable development of Permian gas reservoirs in the Sichuan Basin.

A highly sensitive LITES sensor based on a multi-pass cell with dense spot pattern and a novel quartz tuning fork with low frequency

A highly sensitive light-induced thermoelectric spectroscopy(LITES)sensor based on a multi-pass cell(MPC)with dense spot pattern and a novel quartz tuning fork(QTF)with low resonance frequency is reported in this manuscript.An erbi-um-doped fiber amplifier(EDFA)was employed to amplify the output optical power so that the signal level was further enhanced.The optical path length(OPL)and the ratio of optical path length to volume(RLV)of the MPC is 37.7 m and 13.8 cm^(-2),respectively.A commercial QTF and a self-designed trapezoidal-tip QTF with low frequency of 9461.83 Hz were used as the detectors of the sensor,respectively.The target gas selected to test the performance of the system was acetylene(C2H2).When the optical power was constant at 1000 mW,the minimum detection limit(MDL)of the C2H2-LITES sensor can be achieved 48.3 ppb when using the commercial QTF and 24.6 ppb when using the trapezoid-al-tip QTF.An improvement of the detection performance by a factor of 1.96 was achieved after replacing the commer-cial QTF with the trapezoidal-tip QTF.

Photothermal-photocatalytic thin-layer flow system for synergistic treatment of wastewater

The integration of the photocatalytic effect into solar steam is highly desirable for addressing freshwater shortages and water pollution.Here,a ternary film structure for the adsorption and photothermal and photocatalytic treatment of wastewater was designed by combining the technique of self-assembled carbon nano paper with a nitrogen composite titanium dioxide(N-TiO_(2))deposited on the surface of carbon nanotubes(CNT)using polyvinylidene fluoride(PVDF)as a substrate.The photogeneration of reactive oxygen species can be promoted by rapid oxygen diffusion at the three-phase interface,whereas the interfacial photothermal effect promotes subsequent free radical reactions for the degradation of rhodamine B(93%).The freshwater evaporation rate is 1.35 kg·m^(-2)·h^(-1)and the solar-to-water evaporation efficiency is 94%.Importantly,the N-TiO_(2)/CNT/PVDF(N-TCP)film not only effectively resists mechanical damage from the environment and maintains structural integrity,but can also be made into a large film for outdoor experiments in a large solar energy conversion device to collect fresh water from polluted water and degrade organic dyes in source water simultaneously,opening the way for applications in energy conversion and storage.

Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma(DBA/2J)mice

Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the brain.However,it is not yet clear how the primary visual cortex(V1)is altered in glaucoma.This study used DBA/2J mice as a model for spontaneous secondary glaucoma.The aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J mice.We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band oscillations.The morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections.Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined.Compared with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation.Moreove r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J mice.These findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual tuning.Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model.This study might offer some innovative perspectives regarding the treatment of glaucoma.

Manipulation of High-Fidelity Sidebands under Large Detuning by Floquet Technology:Application to Multi-Mode Cooling

The Floquet technology,a powerful way to manipulate quantum states,is employed to drive sidebands transition under large detuning.Our results demonstrate that high fidelities over 99%can be achieved through optimizing suitable modulation frequencies under large detuning.We observe high-fidelity transitions within a high bandwidth by utilizing a single modulation frequency and reveal that this capability is due to the emergence of a flat-band structure in the bandwidth range.The key finding of high-fidelity sideband manipulation under large detuning is experimentally confirmed in nuclear magnetic resonance platform.Finally,we propose a new parallel sideband cooling scheme that enables simultaneous cooling of multiple motional modes.This approach improves the cooling rate compared to conventional schemes with fixed laser frequency and power,and eliminates the need for mode-specific addressing.Our Floquet parallel scheme is applicable to any harmonic oscillator system and is not limited by bandwidth in theory.

Conceptual design of a 714-MHz RFQ for compact proton injectors and development of a new tuning algorithm on its aluminium prototype

Radio frequency quadrupoles(RFQs),which are crucial components of proton injectors,significantly affect the performance of proton accelerator facilities.An RFQ with a high frequency of 714 MHz dedicated to compact proton injectors for medi-cal applications is designed in this study.The RFQ is designed to accelerate proton beams from 50 keV to 4 MeV within a short length of 2 m and can be matched closely with the downstream drift tube linac to capture more particles through a preliminary optimization.To develop an advanced RFQ,challenging techniques,including fabrication and tuning method,must be evaluated and verified using a prototype.An aluminium prototype is derived from the conceptual design of the RFQ and then redesigned to confirm the radio frequency performance,fabrication procedure,and feasibility of the tuning algorithm.Eventually,a new tuning algorithm based on the response matrix and least-squares method is developed,which yields favorable results based on the prototype,i.e.,the errors of the dipole and quadrupole components reduced to a low level after several tuning iterations.Benefiting from the conceptual design and techniques obtained from the prototype,the formal mechanical design of the 2-m RFQ is ready for the next manufacturing step.

基于prompt tuning的中文文本多领域情感分析研究

不同领域的情感文本表达方式不一样,通常需要为各个领域训练相应的情感分析模型。针对无法用一个模型进行高效多领域情感分析的问题,提出了基于提示微调(prompt tuning)的多领域文本情感分析方法MSAPT。借助hard prompt,指示情感文本的所属领域和待选的情感标签,调动不同领域情感分析相关的知识,再为情感分析预训练一个统一的“通才模型”,在下游的各领域文本学习中,保持模型冻结,通过prompt tuning使模型学习到下游各领域情感文本的特征。MSAPT仅需保存一个模型和一些参数量远远小于模型的prompt,实现了多领域情感分析。在多个属于不同领域的情感文本数据集上进行实验,结果表明仅进行prompt tuning时,MSAPT效果优于模型微调(model tuning)的。最后,分别对适应特定领域的prompt tuning、hard prompt、soft prompt的长度和中间训练数据集的大小进行消融实验,从证明其对情感分析效果的影响。

Improved isochronous mass spectrometry with tune measurement

In conventional isochronous mass spectrometry(IMS)performed on a storage ring,the precision of mass measurements for short-lived nuclei depends on the accurate determination of the revolution times(T)of stored ions.However,the resolution of T inevitably deteriorates due to the magnetic rigidity spread of the ions,limiting the mass-resolving power.In this study,we used the betatron tunes Q(the number of betatron oscillations per revolution)of the ions and established a correlation between T and Q.From this correlation,T was transformed to correspond to a fixed Q with higher resolution.Using these transformed T values,the masses of ^(63)Ge,^(65)As,^(67)Se,and ^(71)Kr agreed well with the mass values measured using the newly developed IMS(Bρ-IMS).We also studied the systematics of Coulomb displacement energies(CDEs)and found that anomalous staggering in CDEs was eliminated using new mass values.This method of T transformation is highly effective for conventional IMS equipped with a single time-of-flight detector.

Vibration attenuation performance of wind turbine tower using a prestressed tuned mass damper under seismic excitation

With the rapid development of large megawatt wind turbines,the operation environment of wind turbine towers(WTTs)has become increasingly complex.In particular,seismic excitation can create a resonance response and cause excessive vibration of the WTT.To investigate the vibration attenuation performance of the WTT under seismic excitations,a novel passive vibration control device,called a prestressed tuned mass damper(PS-TMD),is presented in this study.First,a mathematical model is established based on structural dynamics under seismic excitation.Then,the mathematical analytical expression of the dynamic coefficient is deduced,and the parameter design method is obtained by system tuning optimization.Next,based on a theoretical analysis and parameter design,the numerical results showed that the PS-TMD was able to effectively mitigate the resonance under the harmonic basal acceleration.Finally,the time-history analysis method is used to verify the effectiveness of the traditional pendulum tuned mass damper(PTMD)and the novel PS-TMD device,and the results indicate that the vibration attenuation performance of the PS-TMD is better than the PTMD.In addition,the PS-TMD avoids the nonlinear effect due to the large oscillation angle,and has the potential to dissipate hysteretic energy under seismic excitation.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Parameters Optimization and Performance Evaluation of the Tuned Inerter Damper for the Seismic Protection of Adjacent Building Structures

In order to improve the seismic performance of adjacent buildings,two types of tuned inerter damper(TID)damping systems for adjacent buildings are proposed,which are composed of springs,inerter devices and dampers in serial or in parallel.The dynamic equations of TID adjacent building damping systems were derived,and the H2 norm criterion was used to optimize and adjust them,so that the system had the optimum damping performance under white noise random excitation.Taking TID frequency ratio and damping ratio as optimization parameters,the optimum analytical solutions of the displacement frequency response of the undamped structure under white noise excitation were obtained.The results showed that compared with the classic TMD,TID could obtain a better damping effect in the adjacent buildings.Comparing the TIDs composed of serial or parallel,it was found that the parallel TIDs had more significant advantages in controlling the peak displacement frequency response,while the H2 norm of the displacement frequency response of the damping system under the coupling of serial TID was smaller.Taking the adjacent building composed of two ten-story frame structures as an example,the displacement and energy collection time history analysis of the adjacent building coupled with the optimum design parameter TIDs were carried out.It was found that TID had a better damping effect in the full-time range compared with the classic TMD.This paper also studied the potential power of TID in adjacent buildings,which can be converted into available power resources during earthquakes.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Optimizing Enterprise Conversational AI: Accelerating Response Accuracy with Custom Dataset Fine-Tuning

As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidable challenges. These models, honed on vast and diverse datasets, have undoubtedly pushed the boundaries of natural language understanding and generation. However, they often stumble when faced with the intricate demands of nuanced enterprise applications. This research advocates for a strategic paradigm shift, urging enterprises to embrace a fine-tuning approach as a means to optimize conversational AI. While generalized LLMs are linguistic marvels, their inability to cater to the specific needs of businesses across various industries poses a critical challenge. This strategic shift involves empowering enterprises to seamlessly integrate their own datasets into LLMs, a process that extends beyond linguistic enhancement. The core concept of this approach centers on customization, enabling businesses to fine-tune the AI’s functionality to fit precisely within their unique business landscapes. By immersing the LLM in industry-specific documents, customer interaction records, internal reports, and regulatory guidelines, the AI transcends its generic capabilities to become a sophisticated conversational partner aligned with the intricacies of the enterprise’s domain. The transformative potential of this fine-tuning approach cannot be overstated. It enables a transition from a universal AI solution to a highly customizable tool. The AI evolves from being a linguistic powerhouse to a contextually aware, industry-savvy assistant. As a result, it not only responds with linguistic accuracy but also with depth, relevance, and resonance, significantly elevating user experiences and operational efficiency. In the subsequent sections, this paper delves into the intricacies of fine-tuning, exploring the multifaceted challenges and abundant opportunities it presents. It addresses the technical intricacies of data integration, ethical considerations surrounding data usage, and the broader implications for the future of enterprise AI. The journey embarked upon in this research holds the potential to redefine the role of conversational AI in enterprises, ushering in an era where AI becomes a dynamic, deeply relevant, and highly effective tool, empowering businesses to excel in an ever-evolving digital landscape.

Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load

High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.

A review of SAGD technology development and its possible application potential on thin-layer super-heavy oil reservoirs

Super-heavy oil is a significant unconventional energy source,and more than 30 years of research have shown that steam-assisted gravity drainage(SAGD)technology is suitable for thick super-heavy oil reservoirs.Recently,more and more thin-layer super-heavy oil reservoirs have been discovered in China,while their deep buried depth and serous heterogeneity make the existing SAGD technology difficult to apply,so it is urgent to improve the existing SAGD technology for the thin-layer super-heavy oil.To this end,this paper focuses on the enlightenment of field application in SAGD technology.Firstly,based on typical SAGD field projects,the development history of SAGD technology in the world was reviewed,and the influence of reservoir physical properties on the application of SAGD technology in thin-layer superheavy oil reservoirs was analyzed.Secondly,the well pattern,wellbore structure,pre-heating,artificial lift,and monitor technique of SAGD were detailed described,and their adjustment direction was expounded for the development of thin-layer super-heavy oil reservoirs.Lastly,the gas-and solventassistant SAGD were comprehensively evaluated,and their application potential in thin-layer superheavy oil reservoirs was studied.The research results can provide theoretical guidance for the application of SAGD technology in thin-layer super-heavy oil reservoirs.

Study on microwave-induced thin-layer drying of municipal sludge for fuel reclamation

Since the adverse factors such as deficient penetration and long reaction time have restricted the complete microwave-used drying of municipal sludge, the microwave-induced drying is considered, which has advantages in such aspects. The investigation of the microwave-induced drying to uncover the mechanism bears great meaning for its development and utilization. The effects of temperature and microwave cracking in municipal sewage sludge drying characteristics are stud- ied through municipal sewage sludge drying experiment. Experiments shows that higher drying temperature would lead to a more acutely changing drying rate （DR）. The DR had increased from 0.005 g/（g.min） to 0.060 g/（g·min）, which was 12 times enlarged, while the temperature rose from 70℃ to 160 ℃. The higher the temperature was, the earlier the peak value of DR appeared. The experiments indicated that the temperature was the decisive factor affecting the DR. The microwave-induced sludge reached the highest DR at the moisture rate （MR） of 40%, with a 20% grade promotion compared with that of the original one. The molecular fracture caused by microwave radiation had obviously accelerated the drying process and the DR was rising in proportion to the microwave radiation dose. The diffusion coefficient had been calculated according to Fick＇s law. In com- parison to that of the original one, the diffusion coefficient of microwave-induced sludge was obviously enlarged more than two times. By fit examinations, Model Weibull was proven to be the most fit one for thermal thin-layer drying of municipal sludge. By means of Arrhenius equation, the obtained average activation energy of municipal sludge was 37.1 kJ/（mol·K）.

APPLICATIONS AND MANUFACTURE OF THE MICROSCALE LONG-OPTICAL-PATH ELECTROCHEMICAL CELL WITH A PLUG-IN THIN-LAYER ELECTRODE

The construction and characteristics of a microscale long-optical-path electrochemi- cal cell with a plug-in thin-layer electrode are described.Using ferricyanide as the test species,the thermodynamic parameters of electron transfer processes are determined at car- bon,plantinum,and gold electrodes.

超高层Tuned Mass Damper防震支撑系统技术研究应用

超高层建筑是现代城市建设的重要标志之一,其高度已经超过了传统建筑的极限。然而,随着建筑高度不断增加,地震的破坏力也越来越强,超高层建筑面临着更加严峻的安全挑战。因此,研究超高层建筑防震支撑系统技术非常重要,Tuned Mass Damper(TMD)是一种被广泛研究和应用的超高层建筑防震支撑系统技术,TMD最初是在20世纪60年代提出的,最早应用于桥梁上,后来,TMD被引入到建筑领域,并得到广泛的应用。通过精确调节质量、阻尼和弹性等参数来削弱地震引起的建筑物减震效应,从而减少了建筑物因地震造成的损害和崩塌的风险.

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Since 2019,the coronavirus disease-19(COVID-19)has been spreading rapidly worldwide,posing an unignorable threat to the global economy and human health.It is a disease caused by severe acute respiratory syndrome coronavirus 2,a single-stranded RNA virus of the genus Betacoronavirus.This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells.With the increase in the number of confirmed COVID-19 diagnoses,the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent.Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure.Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed.However,traditional hyperparameter tuning methods are usually time-consuming and unstable.To solve this issue,we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network(PSTCNN),allowing the model to tune hyperparameters automatically.Therefore,the proposed approach can reduce human involvement.Also,the optimisation algorithm can select the combination of hyperparameters in a targeted manner,thus stably achieving a solution closer to the global optimum.Experimentally,the PSTCNN can obtain quite excellent results,with a sensitivity of 93.65%±1.86%,a specificity of 94.32%±2.07%,a precision of 94.30%±2.04%,an accuracy of 93.99%±1.78%,an F1-score of 93.97%±1.78%,Matthews Correlation Coefficient of 87.99%±3.56%,and Fowlkes-Mallows Index of 93.97%±1.78%.Our experiments demonstrate that compared to traditional methods,hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

Stark Tuning of Telecom Single-Photon Emitters Based on a Single Er^(3+)

The implementation of scalable quantum networks requires photons at the telecom band and long-lived spin coherence.The single Er^(3+) in solid-state hosts is an important candidate that fulfills these critical requirements simultaneously.However,to entangle distant Er^(3+) ions through photonic connections,the emission frequency of individual Er^(3+) in solid-state matrix must be the same,which is challenging because the emission frequency of Er^(3+) depends on its local environment.Herein,we propose and experimentally demonstrate the Stark tuning of the emission frequency of a single Er^(3+) in a Y_(2)SiO_(5) crystal by employing electrodes interfaced with a silicon photonic crystal cavity.We obtain a Stark shift of 182.9±0.8 MHz,which is approximately 27 times of the optical emission linewidth,demonstrating promising applications in tuning the emission frequency of independent Er^(3+) into the same spectral channels.Our results provide a useful solution for construction of scalable quantum networks based on single Er^(3+) and a universal tool for tuning emission of individual rare-earth ions.