A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Exercised blood plasma promotes hippocampal neurogenesis in the Alzheimer's disease rat brain

Background:Exercise training promotes brain plasticity and is associated with protection against cognitive impairment and Alzheimer’s disease(AD).These beneficial effects may be partly mediated by blood-borne factors.Here we used an in vitro model of AD to investigate effects of blood plasma from exercise-trained donors on neuronal viability,and an in vivo rat model of AD to test whether such plasma impacts cognitive function,amyloid pathology,and neurogenesis.Methods:Mouse hippocampal neuronal cells were exposed to AD-like stress using amyloid-βand treated with plasma collected from human male donors 3 h after a single bout of high-intensity exercise.For in vivo studies,blood was collected from exercise-trained young male Wistar rats(high-intensity intervals 5 days/week for 6 weeks).Transgenic AD rats(McGill-R-Thyl-APP)were inj ected 5 times/fortnight for 6 weeks at2 months or 5 months of age with either(a)plasma from the exercise-trained rats,(b)plasma from sedentary rats,or(c)saline.Cognitive function,amyloid plaque pathology,and neurogenesis were assessed.The plasma used for the treatment was analyzed for 23 cytokines.Results:Plasma from exercised donors enhanced cell viability by 44.1%(p=0.032)and reduced atrophy by 50.0%(p<0.001)in amyloid-β-treated cells.In vivo exercised plasma treatment did not alter cognitive function or amyloid plaque pathology but did increase hippocampal neurogenesis by~3 fold,regardless of pathological stage,when compared to saline-treated rats.Concentrations of 7 cytokines were significantly reduced in exercised plasma compared to sedentary plasma.Conclusion:Our proof-of-concept study demonstrates that plasma from exercise-trained donors can protect neuronal cells in culture and promote adult hippocampal neurogenesis in the AD rat brain.This effect may be partly due to reduced pro-inflammatory signaling molecules in exercised plasma.

Effect of the interval between two shocks on ejecta formation from the grooved aluminum surface

This work focuses on the effect of the interval between two shocks on the ejecta formation from the grooved aluminum(Al_(1100))surface by using smoothed particle hydrodynamics numerical simulation.Two unsupported shocks are obtained by the plate-impact between sample and two flyers at interval,with a peak pressure of approximately 30 GPa for each shock.When the shock interval varies from 2.11 to 7.67 times the groove depth,the bubble velocity reduces to a constant,and the micro jetting factor R_(J) from spike to bubble exhibits a non-monotonic change that decreases initially and then increases.At a shock interval of 3.6 times the groove depth,micro jetting factor R_(J) from spike to bubble reaches its minimum value of approximately 0.6.While,the micro jetting factor R_(F) from spike to free surface decreases linearly at first,and stabilizes around 0.25 once the shock interval surpasses 4.18 times the groove depth.When the shock interval is less than 4.18 times the groove depth,the unloading wave generated by the breakout of the first shock wave is superimpose with the unloading part of the second shock wave to form a large tensile area.

Optimizing profile line interval for enhanced accuracy in rock joint morphology and shear strength assessments

2D profile lines play a critical role in cost-effectively evaluating rock joint properties and shear strength.However, the interval(ΔI_(L)) between these lines significantly impacts roughness and shear strength assessments. A detailed study of 45 joint samples using four statistical measures across 500 different ΔI_(L)values identified a clear line interval effect with two stages: stable and fluctuation-discrete.Further statistical analysis showed a linear relationship between the error bounds of four parameters,shear strength evaluation, and their corresponding maximum ΔI_(L)values, where the gradient k of this linear relationship was influenced by the basic friction angle and normal stress. Accounting for these factors,lower-limit linear models were employed to determine the optimal ΔI_(L)values that met error tolerances(1%–10%) for all metrics and shear strength. The study also explored the consistent size effect on joints regardless of ΔI_(L)changes, revealing three types of size effects based on morphological heterogeneity.Notably, larger joints required generally higher ΔI_(L)to maintain the predefined error limits, suggesting an increased interval for large joint analyses. Consequently, this research provides a basis for determining the optimal ΔI_(L), improving accuracy in 2D profile line assessments of joint characteristics.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Suppressive effects of exercise-conditioned serum on cancer cells:A narrative review of the influence of exercise mode,volume,and intensity

Cancer is a major cause of morbidity and mortality worldwide,and the incidence is increasing,highlighting the need for effective strategies to treat this disease.Exercise has emerged as fundamental therapeutic medicine in the management of cancer,associated with a lower risk of recur-rence and increased survival.Several avenues of research demonstrate reduction in growth,proliferation,and increased apoptosis of cancer cells,including breast,prostate,colorectal,and lung cancer,when cultured by serum collected after exercise in vitro(i.e.,the cultivation of cancer cell lines in an experimental setting,which simplifies the biological system and provides mechanistic insight into cell responses).The underlying mechanisms of exercise-induced cancer suppressive effects may be attributed to the alteration in circulating factors,such as skeletal muscle-induced cytokines(i.e.,myokines)and hormones.However,exercise-induced tumor suppressive effects and detailed information about training interventions are not well investigated,constraining more precise application of exercise medicine within clinical oncology.To date,it remains unclear what role different training modes(i.e.,resistance and aerobic training)as well as volume and intensity have on exercise-condi-tioned serum and its effects on cancer cells.Nevertheless,the available evidence is that a single bout of aerobic training at moderate to vigorous intensity has cancer suppressive effects,while for chronic training interventions,exercise volume appears to be an influential candidate driving cancer inhibitory effects regardless of training mode.Insights for future research investigating training modes,volume and intensity are provided to further our understanding of the effects of exercise-conditioned serum on cancer cells.

Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

Theα-universal triple I(α-UTI)method is a recognized scheme in the field of fuzzy reasoning,whichwas proposed by our research group previously.The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent,which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference.Therefore,in this study,the interval robustness(embodied as the interval stability)of theα-UTI method is explored in the interval-valued fuzzy environment.To begin with,the stability of theα-UTI method is explored for the case of an individual rule,and the upper and lower bounds of its results are estimated,using four kinds of unified interval implications(including the R-interval implication,the S-interval implication,the QL-interval implication and the interval t-norm implication).Through analysis,it is found that theα-UTI method exhibits good interval stability for an individual rule.Moreover,the stability of theα-UTI method is revealed in the case of multiple rules,and the upper and lower bounds of its outcomes are estimated.The results show that theα-UTI method is stable for multiple rules when four kinds of unified interval implications are used,respectively.Lastly,theα-UTI reasoning chain method is presented,which contains a chain structure with multiple layers.The corresponding solutions and their interval perturbations are investigated.It is found that theα-UTI reasoning chain method is stable in the case of chain reasoning.Two application examples in affective computing are given to verify the stability of theα-UTImethod.In summary,through theoretical proof and example verification,it is found that theα-UTImethod has good interval robustness with four kinds of unified interval implications aiming at the situations of an individual rule,multi-rule and reasoning chain.

Determining the surface fault-rupture hazard zone for the Pazarcık segment of the East Anatolian fault zone through comprehensive analysis of surface rupture from the February 6,2023,Earthquake(Mw 7.7)

Following surface rupture observations in populated areas affected by the KahramanmaraşEarthquake(Mw 7.7)on February 6th,2023,along the Pazarcık segment of the East Anatolian Fault Zone(EAFZ),this study presents novel insights into physical criteria for delineating surface fault-rupture hazard zones(SRHZs)along ruptured strike-slip faults.To achieve this objective,three trench studies across the surface rupture were conducted on the Pazarcık segment of the EAFZ to collect field data,and earthquake recurrence intervals were interpreted using Bayesian statistics from previously conducted paleoseismological trenchings.The results of the proposed model indicate that the Pazarcık segment produced five significant surface-rupturing earthquakes in the last∼11 kyr:E1:11.13±1.74 kyr,E2:7.62±1.20 kyr,E3:5.34±1.05 kyr,E4:1.82±0.93 kyr,and E5:0.35±0.11 kyr.In addition,the recurrence intervals of destructive earthquakes on the subject in question range from 0.6 kyr to 4.8 kyr.Considering that the last significant earthquake occurred in 1513,the longest time since the most recent surface fault rupturing earthquake on this particular segment was 511 years.These results indicate that,in terms of the theoretical recurrence interval of earthquakes that can create surface ruptures on the Pazarcık segment,the period in which the February 6,2023,earthquake occurred was within the end of the expected return period.As a result,the potential for a devastating earthquake in the near future is not foreseen on the same fault.Finally,the SRHZ proposed for the Pazarcık section of Gölbaşıvillage was calculated as a 61-meter-wide offset on the fault lineament to reduce the negativities that may occur in the ruptured area in the future.It is recommended to take into account this width in the settlement of this area and nearby areas.

Alternative Method of Constructing Granular Neural Networks

Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information granules.Optimizing this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance.

The burden of upper motor neuron involvement is correlated with the bilateral limb involvement interval in patients with amyotrophic lateral sclerosis:a retrospective observational study

Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons.Early bilateral limb involvement significantly affects patients'daily lives and may lead them to be confined to bed.However,the effect of upper and lower motor neuron impairment and other risk factors on bilateral limb involvement is unclear.To address this issue,we retrospectively collected data from 586 amyotrophic lateral sclerosis patients with limb onset diagnosed at Peking University Third Hospital between January 2020 and May 2022.A univariate analysis revealed no significant differences in the time intervals of spread in different directions between individuals with upper motor neuron-dominant amyotrophic lateral sclerosis and those with classic amyotrophic lateral sclerosis.We used causal directed acyclic graphs for risk factor determination and Cox proportional hazards models to investigate the association between the duration of bilateral limb involvement and clinical baseline characteristics in amyotrophic lateral sclerosis patients.Multiple factor analyses revealed that higher upper motor neuron scores(hazard ratio[HR]=1.05,95%confidence interval[CI]=1.01–1.09,P=0.018),onset in the left limb(HR=0.72,95%CI=0.58–0.89,P=0.002),and a horizontal pattern of progression(HR=0.46,95%CI=0.37–0.58,P<0.001)were risk factors for a shorter interval until bilateral limb involvement.The results demonstrated that a greater degree of upper motor neuron involvement might cause contralateral limb involvement to progress more quickly in limb-onset amyotrophic lateral sclerosis patients.These findings may improve the management of amyotrophic lateral sclerosis patients with limb onset and the prediction of patient prognosis.

Comparison of QT Correction Methods in the Pediatric Population of a Community Hospital: A Retrospective Study

Objective:Accurate measurement of QT interval,the ventricular action potential from depolarization to repolarization,is important for the early detection of Long QT syndrome.The most effective QT correction(QTc)formula has yet to be determined in the pediatric population,although it has intrinsically greater extremes in heart rate(HR)and is more susceptible to errors in measurement.The authors of this study compare six dif-ferent QTc methods(Bazett,Fridericia,Framingham,Hodges,Rautaharju,and a computer algorithm utilizing the Bazett formula)for consistency against variations in HR and RR interval.Methods:Descriptive Retrospective Study.We included participants from a pediatric cardiology practice of a community hospital who had an ECG performed in 2017.All participants were healthy patients with no past medical history and no regular med-ications.Results:ECGs from 95 participants from one month to 21 years of age(mean 9.7 years)were included with a mean HR of 91 beats per minute(bpm).The two-sample paired t-test or Wilcoxon signed-rank test assessed for any difference between QTc methods.A statistically significant difference was observed between every combination of two QTc formulae.The Spearman’s rank correlation analysis explored the QTc/HR and QTc/RR relationships for each formula.Fridericia method was most independent of HR and RR with the lowest absolute value of correlation coefficients.Bazett and Computer had moderate correlations,while Framingham and Rautaharju exhibited strong correlations.Correlations were positive for Bazett and Computer,reflecting results from prior studies demonstrating an over-correction of Bazett at higher HRs.In the linear QTc/HR regression analysis,Bazett had the slope closest to zero,although Computer,Hodges,and Fridericia had comparable values.Alternatively,Fridericia had the linear QTc/RR regression coefficient closest to zero.The Bland-Altman method assessed for bias and the limits of agreement between correction formulae.Bazett and Computer exhibited good agreement with minimal bias along with Framingham and Rautaharju.To account for a possible skewed distri-bution of QT,all the above analyses were also performed excluding the top and bottom 2%of data as sorted by heart rate ranges(N=90).Results from this data set were consistent with those derived from all participants(N=95).Conclusions:Overall,the Fridericia correction method provided the best rate correction in our pedia-tric study cohort.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

Rice University Rule to Determine the Number of Bins

This study aims to establish a rationale for the Rice University rule in determining the number of bins in a histogram. It is grounded in the Scott and Freedman-Diaconis rules. Additionally, the accuracy of the empirical histogram in reproducing the shape of the distribution is assessed with respect to three factors: the rule for determining the number of bins (square root, Sturges, Doane, Scott, Freedman-Diaconis, and Rice University), sample size, and distribution type. Three measures are utilized: the average distance between empirical and theoretical histograms, the level of recognition by an expert judge, and the accuracy index, which is composed of the two aforementioned measures. Mean comparisons are conducted with aligned rank transformation analysis of variance for three fixed-effects factors: sample size (20, 35, 50, 100, 200, 500, and 1000), distribution type (10 types), and empirical rule to determine the number of bins (6 rules). From the accuracy index, Rice’s rule improves with increasing sample size and is independent of distribution type. It outperforms the Friedman-Diaconis rule but falls short of Scott’s rule, except with the arcsine distribution. Its profile of means resembles the square root rule concerning distributions and Doane’s rule concerning sample sizes. These profiles differ from those of the Scott and Friedman-Diaconis rules, which resemble each other. Among the seven rules, Scott’s rule stands out in terms of accuracy, except for the arcsine distribution, and the square root rule is the least accurate.

Precision in detecting colon lesions:A key to effective screening policy but will it improve overall outcomes?

Colonoscopy is the gold standard for the screening and diagnosis of colorectal cancer,resulting in a decrease in the incidence and mortality of colon cancer.However,it has a 21%rate of missed polyps.Several strategies have been devised to increase polyp detection rates and improve their characterization and delimi-tation.These include chromoendoscopy(CE),the use of other devices such as Endo cuffs,and major advances in endoscopic equipment[high definition,magnification,narrow band imaging,i-scan,flexible spectral imaging color enhancement,texture and color enhancement imaging(TXI),etc.].In the retrospective study by Hiramatsu et al,they compared white-light imaging with CE,TXI,and CE+TXI to determine which of these strategies allows for better definition and delimitation of polyps.They concluded that employing CE associated with TXI stands out as the most effective method to utilize.It remains to be demonstrated whether these results are extrapolatable to other types of virtual CE.Additionally,further investigation is needed in order to ascertain whether this strategy could lead to a reduction in the recurrence of excised lesions and potentially lower the occurrence of interval cancer.

Measures of Variability for Qualitative Variables Using the R Software

Although there are many measures of variability for qualitative variables, they are little used in social research, nor are they included in statistical software. The aim of this article is to present six measures of variation for qualitative variables of simple calculation, as well as to facilitate their use by means of the R software. The measures considered are, on the one hand, Freemans variation ratio, Morals universal variation ratio, Kvalseths standard deviation from the mode, and Wilcoxs variation ratio which are most affected by proximity to a constant random variable, where the measures of variability for qualitative variables reach their minimum value of 0. On the other hand, the Gibbs-Poston index of qualitative variation and Shannons relative entropy are included, which are more affected by the proximity to a uniform distribution, where the measures of variability for qualitative variables reach their maximum value of 1. Point and interval estimation are addressed. Bootstrap by the percentile and bias-corrected and accelerated percentile methods are used to obtain confidence intervals. Two calculation situations are presented: with a sample mode and with two or more modes. The standard deviation from the mode among the six considered measures, and the universal variation ratio among the three variation ratios, are particularly recommended for use.

Near-Field Beam Training for Holographic MIMO Communications: Typical Methods, Challenges and Future Directions

Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.

Rapid diagnostic method for transplutonium isotope production in high flux reactors

Transplutonium isotopes are scarce and need to be produced by irradiation in high flux reactors.However,their production is inefficient,and optimization studies are necessary.This study analyzes the physical nature of transplutonium isotope produc-tion using ^(252)Cf,^(244)Cm,^(242)Cm,and ^(238)Pu as examples.Traditional methods based on the Monte Carlo burnup calculation have the limitations of many calculations and cannot analyze the individual energy intervals in detail;thus,they cannot sup-port the refined evaluation,screening,and optimization of the irradiation schemes.After understanding the physical nature and simplifying the complexity of the production process,we propose a rapid diagnostic method for evaluating radiation schemes based on the concepts“single energy interval value(SEIV)”and“energy spectrum total value(ESTV)”.The rapid diagnostic method not only avoids tedious burnup calculations,but also provides a direction for optimization.The optimal irradiation schemes for producing ^(252)Cf,^(244)Cm,^(242)Cm,and ^(238)Pu are determined based on a rapid diagnostic method.Optimal irradiation schemes can significantly improve production efficiency.Compared with the initial scheme,the optimal scheme improved the production efficiency of ^(238)Pu by 7.41 times;^(242)Cm,11.98 times;^(244)Cm,65.20 times;and ^(252)Cf,15.08 times.Thus,a refined analysis of transplutonium isotope production is conducted and provides a theoretical basis for improving production efficiency.

Enrichment factors of movable hydrocarbons in lacustrine shale oil and exploration potential of shale oil in Gulong Sag,Songliao Basin,NE China

The geological characteristics and production practices of the major middle-and high-maturity shale oil exploration areas in China are analyzed.Combined with laboratory results,it is clear that three essential conditions,i.e.economic initial production,commercial cumulative oil production of single well,and large-scale recoverable reserves confirmed by the testing production,determine whether the continental shale oil can be put into large-scale commercial development.The quantity and quality of movable hydrocarbons are confirmed to be crucial to economic development of shale oil,and focuses in evaluation of shale oil enrichment area/interval.The evaluation indexes of movable hydrocarbon enrichment include:(1)the material basis for forming retained hydrocarbon,including TOC>2%(preferentially 3%-4%),and typeⅠ-Ⅱkerogens;(2)the mobility of retained hydrocarbon,which is closely related to the hydrocarbon composition and flow behaviors of light/heavy components,and can be evaluated from the perspectives of thermal maturity(Ro),gas-oil ratio(GOR),crude oil density,quality of hydrocarbon components,preservation conditions;and(3)the reservoir characteristics associated with the engineering reconstruction,including the main pore throat distribution zone,reservoir physical properties(including fractures),lamellation feature and diagenetic stage,etc.Accordingly,13 evaluation indexes in three categories and their reference values are established.The evaluation indicates that the light shale oil zones in the Gulong Sag of Songliao Basin have the most favorable enrichment conditions of movable hydrocarbons,followed by light oil and black oil zones,containing 20.8×10^(8) t light oil resources in reservoirs with R_(0)>1.2%,pressure coefficient greater than 1.4,effective porosity greater than 6%,crude oil density less than 0.82 g/cm^(3),and GOR>100 m/m^(3).The shale oil in the Gulong Sag can be explored and developed separately by the categories(resource sweet spot,engineering sweet spot,and tight oil sweet spot)depending on shale oil flowability.The Gulong Sag is the most promising area to achieve large-scale breakthrough and production of continental shale oil in China.

PRI modulation recognition and sequence search under small sample prerequisite

Pulse repetition interval(PRI)modulation recognition and pulse sequence search are significant for effective electronic support measures.In modern electromagnetic environments,different types of inter-pulse slide radars are highly confusing.There are few available training samples in practical situations,which leads to a low recognition accuracy and poor search effect of the pulse sequence.In this paper,an approach based on bi-directional long short-term memory(BiLSTM)networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed.The simulation results demonstrate that the proposed algorithm can recognize unilinear,bilinear,sawtooth,and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.