Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

A 16-bit 18-MSPS flash-assisted SAR ADC with hybrid synchronous and asynchronous control logic

This paper presents a 16-bit,18-MSPS(million samples per second)flash-assisted successive-approximation-register(SAR)analog-to-digital converter(ADC)utilizing hybrid synchronous and asynchronous(HYSAS)timing control logic based on an on-chip delay-locked loop(DLL).The HYSAS scheme can provide a longer settling time for the capacitive digital-to-analog converter(CDAC)than the synchronous and asynchronous SAR ADC.Therefore,the issue of incomplete settling or ringing in the DAC voltage for cases of either on-chip or off-chip reference voltage can be solved to a large extent.In addition,the fore-ground calibration of the CDAC’s mismatch is performed with a finite-impulse-response bandpass filter(FIR-BPF)based least-mean-square(LMS)algorithm in an off-chip FPGA(field programmable gate array).Fabricated in 40-nm CMOS process,the proto-type ADC achieves 94.02-dB spurious-free dynamic range(SFDR),and 75.98-dB signal-to-noise-and-distortion ratio(SNDR)for a 2.88-MHz input under 18-MSPS sampling rate.

An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks.Traditional approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally intensive.This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy.Conventional classification methods are ill-suited for incomplete medical data.To enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data.Initially,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification algorithm.The effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and sensitivity.The encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

Caspase-8 promotes scramblase-mediated phosphatidylserine exposure and fusion of osteoclast precursors

Efficient cellular fusion of mononuclear precursors is the prerequisite for the generation of fully functional multinucleated bone-resorbing osteoclasts.However,the exact molecular factors and mechanisms controlling osteoclast fusion remain incompletely understood.Here we identify RANKL-mediated activation of caspase-8 as early key event during osteoclast fusion.

SREBP2 restricts osteoclast differentiation and activity by regulating IRF7 and limits inflammatory bone erosion

Osteoclasts are multinucleated bone-resorbing cells,and their formation is tightly regulated to prevent excessive bone loss.However,the mechanisms by which osteoclast formation is restricted remain incompletely determined.Here,we found that sterol regulatory element binding protein 2(SREBP2)functions as a negative regulator of osteoclast formation and inflammatory bone loss.Cholesterols and SREBP2,a key transcription factor for cholesterol biosynthesis,increased in the late phase of osteoclastogenesis.

Data-Driven Learning Control Algorithms for Unachievable Tracking Problems

For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

Ab initio density response and local field factor of warm dense hydrogen

We present quasi-exact ab initio path integral Monte Carlo(PIMC)results for the partial static density responses and local field factors of hydrogen in the warm dense matter regime,from solid density conditions to the strongly compressed case.The full dynamic treatment of electrons and protons on the same footing allows us to rigorously quantify both electronic and ionic exchange–correlation effects in the system,and to compare the results with those of earlier incomplete models such as the archetypal uniform electron gas or electrons in a fixed ion snapshot potential that do not take into account the interplay between the two constituents.The full electronic density response is highly sensitive to electronic localization around the ions,and our results constitute unambiguous predictions for upcoming X-ray Thomson scattering experiments with hydrogen jets and fusion plasmas.All PIMC results are made freely available and can be used directly for a gamut of applications,including inertial confinement fusion calculations and the modeling of dense astrophysical objects.Moreover,they constitute invaluable benchmark data for approximate but computationally less demanding approaches such as density functional theory or PIMC within the fixed-node approximation.

Influence of Incomplete Soil Plugs on Bearing Capacities of Bucket Foundations in Clay

Due to the uneven seabed and heaving of soil during pumping,incomplete soil plugs may occur during the installation of bucket foundations,and the impacts on the bearing capacities of bucket foundations need to be evaluated.In this paper,the contact ratio(the ratio of the top diameter of the soil plug to the diameter of the bucket)and the soil plug ratio(the ratio of the soil heave height to the skirt height)are defined to describe the shape and size of the incomplete soil plug.Then,finite element models are established to investigate the bearing capacities of bucket foundations with incomplete soil plugs and the influences of the contact ratios,and the soil plug ratios on the bearing capacities are analyzed.The results show that the vertical bearing capacity of bucket foundations in homogeneous soil continuously improves with the increase of the contact ratio.However,in normally consolidated soil,the vertical bearing capacity barely changes when the contact ratio is smaller than 0.75,while the bearing capacity suddenly increases when the contact ratio increases to 1 due to the change of failure mode.The contact ratio hardly affects the horizontal bearing capacity of bucket foundations.Moreover,the moment bearing capacity improves with the increase of the contact ratio for small aspect ratios,but hardly varies with increasing contact ratio for aspect ratios larger than 0.5.Consequently,the reduction coefficient method is proposed based on this analysis to calculate the bearing capacities of bucket foundations considering the influence of incomplete soil plugs.The comparison results show that the proposed reduction coefficient method can be used to evaluate the influences of incomplete soil plug on the bearing capacities of bucket foundations.

Leveraging Uncertainty for Depth-Aware Hierarchical Text Classification

Hierarchical Text Classification(HTC)aims to match text to hierarchical labels.Existing methods overlook two critical issues:first,some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target.Second,error propagation occurs when a misclassification at a parent node propagates down the hierarchy,ultimately leading to inaccurate predictions at the leaf nodes.To address these limitations,we propose an uncertainty-guided HTC depth-aware model called DepthMatch.Specifically,we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels,classifying them into the corresponding parent node labels.This approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate uncertainty.Experimental results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets:WOS(Web of Science),RCV1-V2(Reuters Corpus Volume I),AAPD(Arxiv Academic Paper Dataset),and BGC.Notably,on the BGC dataset,it improvesMicro-F1 andMacro-F1 scores by at least 1.09%and 1.74%,respectively.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Pharmacodynamic Experiment of Dachengqi Decoction and Separated Decoction on Incomplete Intestinal Obstruction in Rats

[Objectives]To investigate the effect and mechanism of Dachengqi Decoction and separated decoction on incomplete intestinal obstruction in rats.[Methods]80 healthy SD rats were selected to establish incomplete intestinal obstruction model by silk ligation.The dosage was 20 mL/kg for 3 d,and the damage index of ileocecal mucosa was analyzed;the morphology of ileocecal mucosa was observed by HE staining;the serum levels of IL-1α,IL-1β,IL-6,IL-18,Ach,NO,ET,IL-1,TNF-αand ultra-micro Na+-K+-ATPase were detected by ELISA.[Results]Compared with the model group,the mucosal damage index of Dachengqi Decoction and each separated decoction group decreased significantly(P<0.05);compared with the normal group and sham operation group,the serum level of IL-1,IL-6,TNF-αand other factors in the model group increased significantly(P<0.05);compared with the model group,the serum IL-1,IL-6 and TNF-αsecretion levels of rats in Dachengqi Decoction group and separated decoction group decreased(P<0.01).[Conclusions]Dachengqi Decoction and each separated decoction can effectively improve intestinal tissue pathological damage in the incomplete intestinal obstruction model rats,and reduce the inflammatory reaction in the rat body.

China Seeks to Grab Opportunities Brought about by the Low-Altitude Economy

Since the inclusion of the low-altitude economy in the government work report during the National People’s Congress and the Chinese People’s Political Consultative Conference in March,many cities have sought to enter this area first to seize the benefits of the low-altitude economy.According to incomplete statistics,17 provinces(municipalities,autonomous regions)in China have currently written the low-altitude economy into their 2024 government work report.In addition,cities such as Shenzhen,Guangzhou,Chengdu,Suzhou,Zhuhai,and Ganzhou have also included the low-altitude economy in their government work reports.More than 20 provincial and municipal governments,represented by Shenzhen,Hefei,Guangzhou and Chengdu,have introduced also policies to support the development of the low-altitude economy and its business ecosystem.

Legendre-Jacobi’s Elliptic Integrals Shed Light on the Luminosity Distance in Cosmology

This article concerns the integral related to the transverse comoving distance and, in turn, to the luminosity distance both in the standard non-flat and flat cosmology. The purpose is to determine a straightforward mathematical formulation for the luminosity distance as function of the transverse comoving distance for all cosmology cases with a non-zero cosmological constant by adopting a different mindset. The applied method deals with incomplete elliptical integrals of the first kind associated with the polynomial roots admitted in the comoving distance integral according to the scientific literature. The outcome shows that the luminosity distance can be obtained by the combination of an analytical solution followed by a numerical integration in order to account for the redshift. This solution is solely compared to the current Gaussian quadrature method used as basic recognized algorithm in standard cosmology.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome

According to the disease module hypothesis,the cellular components associated with a disease segregate in the same neighborhood of the human interactome,the map of biologically relevant molecular interactions.Yet,given the incompleteness of the interactome and the limited knowledge of disease-associated genes,it is not obvious if the available data have sufficient coverage to map out modules associated with each disease.

Cytoplasmic DNA: sources, sensing, and role in aging and disease

Endogenous cytoplasmic DNA(cytoDNA)species are emerging as key mediators of inflammation in diverse physiological and pathological contexts.Although the role of endogenous cytoDNA in innate immune activation is well established,the cytoDNA species themselves are often poorly characterized and difficult to distinguish,and their mechanisms of formation,scope of function and contribution to disease are incompletely understood.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome

According to the disease module hypothesis,the cellular components associated with a disease segregate in the same neighborhood of the human interactome,the map of biologically relevant molecular interactions.Yet,given the incompleteness of the interactome and the limited knowledge of disease-associated genes,it is not obvious if the available data have sufficient coverage to map out modules associated with each disease.Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases.For example,diseases with overlapping network modules show significant coexpression patterns,symptom similarity,and comorbidity,whereas diseases residing in separated network neighborhoods are phenotypically distinct.These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases,even if they do not share primary disease genes.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.

Gap-free genome assembly and CYP450 gene family analysis reveal the biosynthesis of anthocyanins in Scutellaria baicalensis

Scutellaria baicalensis Georgi,a member of the Lamiaceae family,is a widely utilized medicinal plant.The flavones extracted from S.baicalensis contribute to numerous health benefits,including anti-inflammatory,antiviral,and anti-tumor activities.However,the incomplete genome assembly hinders biological studies on S.baicalensis.This study presents the first telomere-to-telomere(T2T)gap-free genome assembly of S.baicalensis through the integration of Pacbio HiFi,Nanopore ultra-long and Hi-C technologies.A total of 384.59 Mb of genome size with a contig N50 of 42.44 Mb was obtained,and all sequences were anchored into nine pseudochromosomes without any gap or mismatch.In addition,we analysed the major cyanidin-and delphinidin-based anthocyanins involved in the determination of blue-purple flower using a widely-targeted metabolome approach.Based on the genome-wide identification of Cytochrome P450(CYP450)gene family,three genes(SbFBH1,2,and 5)encoding flavonoid 3′-hydroxylases(F3′Hs)and one gene(SbFBH7)encoding flavonoid 3′5′-hydroxylase(F3′5′H)were found to hydroxylate the B-ring of flavonoids.Our studies enrich the genomic information available for the Lamiaceae family and provide a toolkit for discovering CYP450 genes involved in the flavonoid decoration.

Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.