基于构型优选的5G集群无人机协同导航方法

针对现有基于测距的集群UAV协同导航方法普遍忽略了空间构型对定位定能的影响,难以获得精确的导航定位结果,提出一种基于空间构型优选的5G集群UAV协同导航方法。构建了复杂环境下基于5G信号的UAV相对测距误差模型,基于最小几何精度因子(geometric dilution of precision,GDOP)准则建立了协同导航节点寻优策略,实现了协同导航空间构型的实时优选;设计了基于5G测距网络的协同导航滤波器,对UAV导航信息进行在线估计和实时补偿,提高集群UAV的协同定位精度。仿真结果表明:该方法从机定位精度平均提升了约42.05%,为集群UAV实现在卫星不可用条件下的自主导航提供了一种有效的新方法。

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis(TB),especially multidrug-resistant TB and extensively drug-resistant TB,has strongly motivated the research and development of new anti-TB drugs.New strategies to facilitate drug combinations,including pharmacokinetics-guided dose optimization and toxicology studies of first-and second-line anti-TB drugs have also been introduced and recommended.Liquid chromatography-mass spectrometry(LC-MS)has arguably become the gold standard in the analysis of both endo-and exo-genous compounds.This technique has been applied successfully not only for therapeutic drug monitoring(TDM)but also for pharmacometabolomics analysis.TDM improves the effectiveness of treatment,reduces adverse drug reactions,and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window.Based on TDM,the dose would be optimized individually to achieve favorable outcomes.Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs,aiding in the discovery of potential biomarkers for TB diagnostics,treatment monitoring,and outcome evaluation.This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades.Besides,we discussed the advantages and disadvantages of this technique in practical use.The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted.Lastly,we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies(pharmacometrics,drug and vaccine developments,machine learning/artificial intelligence,among others)to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

21st century critical care medicine:An overview

Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units(ICUs).This abstract provides a concise summary of the latest developments in critical care,highlighting key areas of innovation.Recent advancements in critical care include Precision Medicine:Tailoring treatments based on individual patient characteristics,genomics,and biomarkers to enhance the effectiveness of therapies.The objective is to describe the recent advancements in Critical Care Medicine.Telemedicine:The integration of telehealth technologies for remote patient monitoring and consultation,facilitating timely interventions.Artificial intelligence(AI):AI-driven tools for early disease detection,predictive analytics,and treatment optimization,enhancing clinical decision-making.Organ Support:Advanced life support systems,such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support.Infection Control:Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections.Ventilation Strategies:Precision ventilation modes and lung-protective strategies to minimize ventilatorinduced lung injury.Sepsis Management:Early recognition and aggressive management of sepsis with tailored interventions.Patient-Centered Care:A shift towards patient-centered care focusing on psychological and emotional wellbeing in addition to medical needs.We conducted a thorough literature search on PubMed,EMBASE,and Scopus using our tailored strategy,incorporating keywords such as critical care,telemedicine,and sepsis management.A total of 125 articles meeting our criteria were included for qualitative synthesis.To ensure reliability,we focused only on articles published in the English language within the last two decades,excluding animal studies,in vitro/molecular studies,and non-original data like editorials,letters,protocols,and conference abstracts.These advancements reflect a dynamic landscape in critical care medicine,where technology,research,and patient-centered approaches converge to improve the quality of care and save lives in ICUs.The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.

Re-identifying beef cattle using improved AlignedReID++

Accurate and continuous identification of individual cattle is crucial to precision farming in recent years.It is also the prerequisite to monitor the individual feed intake and feeding time of beef cattle at medium to long distances over different cameras.However,beef cattle can tend to frequently move and change their feeding position during feeding.Furthermore,the great variations in their head direction and complex environments(light,occlusion,and background)can also lead to some difficulties in the recognition,particularly for the bio-similarities among individual cattle.Among them,AlignedReID++model is characterized by both global and local information for image matching.In particular,the dynamically matching local information(DMLI)algorithm has been introduced into the local branch to automatically align the horizontal local information.In this research,the AlignedReID++model was utilized and improved to achieve the better performance in cattle re-identification(ReID).Initially,triplet attention(TA)modules were integrated into the BottleNecks of ResNet50 Backbone.The feature extraction was then enhanced through cross-dimensional interactions with the minimal computational overhead.Since the TA modules in AlignedReID++baseline model increased the model size and floating point operations(FLOPs)by 0.005 M and 0.05 G,the rank-1 accuracy and mean average precision(mAP)were improved by 1.0 percentage points and 2.94 percentage points,respectively.Specifically,the rank-1 accuracies were outperformed by 0.86 percentage points and 0.12 percentage points,respectively,compared with the convolution block attention module(CBAM)and efficient channel attention(ECA)modules,although 0.94 percentage points were lower than that of squeeze-and-excitation(SE)modules.The mAP metric values were exceeded by 0.22,0.86 and 0.12 percentage points,respectively,compared with the SE,CBAM,and ECA modules.Additionally,the Cross-Entropy Loss function was replaced with the CosFace Loss function in the global branch of baseline model.CosFace Loss and Hard Triplet Loss were jointly employed to train the baseline model for the better identification on the similar individuals.AlignedReID++with CosFace Loss was outperformed the baseline model by 0.24 and 0.92 percentage points in the rank-1 accuracy and mAP,respectively,whereas,AlignedReID++with ArcFace Loss was exceeded by 0.36 and 0.56 percentage points,respectively.The improved model with the TA modules and CosFace Loss was achieved in a rank-1 accuracy of 94.42%,rank-5 accuracy of 98.78%,rank-10 accuracy of 99.34%,mAP of 63.90%,FLOPs of 5.45 G,frames per second(FPS)of 5.64,and model size of 23.78 M.The rank-1 accuracies were exceeded by 1.84,4.72,0.76 and 5.36 percentage points,respectively,compared with the baseline model,part-based convolutional baseline(PCB),multiple granularity network(MGN),and relation-aware global attention(RGA),while the mAP metrics were surpassed 6.42,5.86,4.30 and 7.38 percentage points,respectively.Meanwhile,the rank-1 accuracy was 0.98 percentage points lower than TransReID,but the mAP metric was exceeded by 3.90 percentage points.Moreover,the FLOPs of improved model were only 0.05 G larger than that of baseline model,while smaller than those of PCB,MGN,RGA,and TransReID by 0.68,6.51,25.4,and 16.55 G,respectively.The model size of improved model was 23.78 M,which was smaller than those of the baseline model,PCB,MGN,RGA,and TransReID by 0.03,2.33,45.06,14.53 and 62.85 M,respectively.The inference speed of improved model on a CPU was lower than those of PCB,MGN,and baseline model,but higher than TransReID and RGA.The t-SNE feature embedding visualization demonstrated that the global and local features were achieve in the better intra-class compactness and inter-class variability.Therefore,the improved model can be expected to effectively re-identify the beef cattle in natural environments of breeding farm,in order to monitor the individual feed intake and feeding time.

A novel self-alignment method for high precision silicon diffraction microlens arrays preparation and its integration with infrared focal plane arrays

Silicon(Si)diffraction microlens arrays are usually used to integrating with infrared focal plane arrays(IRFPAs)to improve their performance.The errors of lithography are unavoidable in the process of the Si diffrac-tion microlens arrays preparation in the conventional engraving method.It has a serious impact on its performance and subsequent applications.In response to the problem of errors of Si diffraction microlens arrays in the conven-tional method,a novel self-alignment method for high precision Si diffraction microlens arrays preparation is pro-posed.The accuracy of the Si diffractive microlens arrays preparation is determined by the accuracy of the first li-thography mask in the novel self-alignment method.In the subsequent etching,the etched area will be protected by the mask layer and the sacrifice layer or the protective layer.The unprotection area is carved to effectively block the non-etching areas,accurately etch the etching area required,and solve the problem of errors.The high precision Si diffraction microlens arrays are obtained by the novel self-alignment method and the diffraction effi-ciency could reach 92.6%.After integrating with IRFPAs,the average blackbody responsity increased by 8.3%,and the average blackbody detectivity increased by 10.3%.It indicates that the Si diffraction microlens arrays can improve the filling factor and reduce crosstalk of IRFPAs through convergence,thereby improving the perfor-mance of the IRFPAs.The results are of great reference significance for improving their performance through opti-mizing the preparation level of micro nano devices.

Dietary eubiotics of microbial muramidase and glycan improve intestinal villi,ileum microbiota composition and production trait of broiler

Background Optimal gut health is important to maximize growth performance and feed efficiency in broiler chickens.A total of 1,365 one-day-old male Ross 308 broiler chickens were randomly divided into 5 treatments groups with 21 replicates,13 birds per replicate.The present research investigated effects of microbial muramidase or a precision glycan alone or in combination on growth performance,apparent total tract digestibility,total blood carotenoid content,intestinal villus length,meat quality and gut microbiota in broiler chickens.Treatments included:NC:negative control(basal diet group);PC:positive control(basal diet+0.02%probiotics);MR:basal diet+0.035%microbial muramidase;PG:basal diet+0.1%precision glycan;and MRPG:basal diet+0.025%MR+0.1%PG,respectively.Results MRPG group increased the body weight gain and feed intake(P<0.05)compared with NC group.Moreover,it significantly increased total serum carotenoid(P<0.05)and MRPG altered the microbial diversity in ileum contents.The MRPG treatment group increased the abundance of the phylum Firmicutes,and family Lachnospiraceae,Ruminococcaceae,Oscillospiraceae,Lactobacillaceae,Peptostreptococcaceae and decreased the abundance of the phylum Campilobacterota,Bacteroidota and family Bacteroidaceae.Compared with the NC group,the chickens fed MRPG showed significantly increased in duodenum villus length at end the trial.Conclusion In this study,overall results showed that the synergetic effects of MR and PG showed enhancing growth performance,total serum carotenoid level and altering gut microbiota composition of broilers.The current research indicates that co-supplementation of MR and PG in broiler diets enhances intestinal health,consequently leading to an increased broiler production.

Recent advances on thermosensitive hydrogels-mediated precision therapy

Precision therapy has become the preferred choice attributed to the optimal drug concentration in target sites,increased therapeutic efficacy,and reduced adverse effects.Over the past few years,sprayable or injectable thermosensitive hydrogels have exhibited high therapeutic potential.These can be applied as cell-growing scaffolds or drug-releasing reservoirs by simply mixing in a free-flowing sol phase at room temperature.Inspired by their unique properties,thermosensitive hydrogels have been widely applied as drug delivery and treatment platforms for precision medicine.In this review,the state-of-theart developments in thermosensitive hydrogels for precision therapy are investigated,which covers from the thermo-gelling mechanisms and main components to biomedical applications,including wound healing,anti-tumor activity,osteogenesis,and periodontal,sinonasal and ophthalmic diseases.The most promising applications and trends of thermosensitive hydrogels for precision therapy are also discussed in light of their unique features.

Accuracy of tree mapping based on hand-held laser scanning comparing leaf-on and leaf-off conditions in mixed forests

The use of mobile laser scanning to survey forest ecosystems is a promising,scalable technology to describe forest 3D structures at high resolution.To confirm the con-sistency in the retrieval of forest structural parameters using hand-held laser scanning(HLS),before operationalizing the method,confirming the data is crucial.We analyzed the per-formance of tree-level mapping based on HLS under differ-ent phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species,Alnus glutinosa and Quercus pyrenaica.The area was surveyed twice during the growing season(July 2022)and once in the deciduous season(February 2022)using several scan-ning paths.Ground reference data(418 trees,15 snags)was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attrib-utes(DBH,height and volume).The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology.Ninety-six percent of all pairs matched below 65 cm.For DBH,phenology barely altered estimates.We observed a strong agreement when comparing HLS-based tree height distributions.The values exceeded 2 m when comparing height measurements,confirming height data should be carefully used as reference in remote sensing-based inventories,especially for deciduous species.Tree volume was more precise for pines(r=0.95,and rela-tive RMSE=21.3–23.8%)compared to deciduous species(r=0.91–0.96,and relative RMSE=27.3–30.5%).HLS data and the forest structural complexity tool performed remark-ably,especially in tree positioning considering mixed forests and mixed phenology conditions.

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

Bronchoalveolar lavage fluid assessment facilitates precision medicine for lung cancer

Lung cancer is the most common and fatal malignant disease worldwide and has the highest mortality rate among tumor-related causes of death.Early diagnosis and precision medicine can significantly improve the survival rate and prognosis of lung cancer patients.At present,the clinical diagnosis of lung cancer is challenging due to a lack of effective non-invasive detection methods and biomarkers,and treatment is primarily hindered by drug resistance and high tumor heterogeneity.Liquid biopsy is a method for detecting circulating biomarkers in the blood and other body fluids containing genetic information from primary tumor tissues.Bronchoalveolar lavage fluid(BALF)is a potential liquid biopsy medium that is rich in a variety of bioactive substances and cell components.BALF contains information on the key characteristics of tumors,including the tumor subtype,gene mutation type,and tumor environment,thus BALF may be used as a diagnostic supplement to lung biopsy.In this review,the current research on BALF in the diagnosis,treatment,and prognosis of lung cancer is summarized.The advantages and disadvantages of different components of BALF,including cells,cell-free DNA,extracellular vesicles,and micro RNA are introduced.In particular,the great potential of extracellular vesicles in precision diagnosis and detection of drug-resistant for lung cancer is highlighted.In addition,the performance of liquid biopsies with different body fluid sources in lung cancer detection are compared to facilitate more selective studies involving BALF,thereby promoting the application of BALF for precision medicine in lung cancer patients in the future.

Characterization and Analysis of Inconel 718 Alloy Ground at Different Speeds

Inconel 718(IN718)alloy is widely applied to fabricate high temperature resistant or corrosion resistant parts due to its excellent mechanical performance.However,the machining of IN718 alloy is difficult as it may cause serious tool wear and poor surface quality(SQ)of the workpiece.In this work,grinding experiments on IN718 alloy at different speeds were conducted by using a CBN grinding wheel.The relationship between grinding speed,SQ and subsurface damage(SSD)was well studied.With increasing grinding speed,surface roughness decreased,and SQ was greatly improved.Meanwhile,the microhardness of the grinding surface declined as the grinding speed increased.The SSD depth was almost unchanged when the grinding speed was lower than 15 m/s,then it decreased with higher grinding speeds.It was attributed to the mechanical-thermal synergistic effect in the grinding process.The results indicated that increasing grinding speed can effectively improve the SQ and reduce the SSD of IN718 alloy.The conclusion in the work may also provide insight into processing other hard-to-machining materials.

Current status and challenges for cell-cultured milk technology: a systematic review

Cellular agriculture is an innovative technology for manufacturing sustainable agricultural products as an alternative to traditional agriculture.While most cellular agriculture is predominantly centered on the production of cultured meat,there is a growing demand for an understanding of the production techniques involved in dairy products within cellular agriculture.This review focuses on the current status of cellular agriculture in the dairy sector and technical challenges for cell-cultured milk production.Cellular agriculture technology in the dairy sector has been classified into fermentation-based and animal cell culture-based cellular agriculture.Currently,various companies synthesize milk components through precision fermentation technology.Nevertheless,several startup companies are pursuing animal cell-based technology,driven by public concerns regarding genetically modified organisms in precision fermentation technology.Hence,this review offers an up-to-date exploration of animal cell-based cellular agriculture to produce milk components,specifically emphasizing the structural,functional,and productive aspects of mammary epithelial cells,providing new information for industry and academia.

Intelligent Diagnosis Method for Typical Co-frequency Vibration Faults of Rotating Machinery Based on SAE and Ensembled ResNet-SVM

Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management.To deal with co-frequency vibration faults,a type of typical fault in rotating machinery,this paper proposes a fault diagnosis method based on the stacked autoencoder(SAE)and ensembled ResNet-SVM.Furthermore,the time-and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data.To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study,the following three criteria are required:First,to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation,adding noise,autoencoder(AE)and SAE methods are analyzed in terms of principle and practical effects.Second,ResNet is used as the feature extractor for the ensembled ResNet-SVM model.Feature extraction is carried out twice,and the extracted co-frequency fault features are more comprehensive.Finally,the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods.The experimental results show that the accuracy of the proposed method can exceed 99.9%.

Micron-sized fiber diamond probe for quantum precision measurement of microwave magnetic field

We present a quantitative measurement of the horizontal component of the microwave magnetic field of a coplanar waveguide using a quantum diamond probe in fiber format.The measurement results are compared in detail with simulation,showing a good consistence.Further simulation shows fiber diamond probe brings negligible disturbance to the field under measurement compared to bulk diamond.This method will find important applications ranging from electromagnetic compatibility test and failure analysis of high frequency and high complexity integrated circuits.

Beidou receiver based on anti-jamming antenna arrays with self-calibration for precise relative positioning

Unmanned aerial vehicles(UAVs)may be subjected to unintentional radio frequency interference(RFI)or hostile jamming attack which will lead to fail to track global navigation satellite system(GNSS)signals.Therefore,the simultaneous realization of anti-jamming and high-precision carrier phase difference positioning becomes a dilemmatic problem.In this paper,a distortionless phase digital beamforming(DBF)algorithm with self-calibration antenna arrays is proposed,which enables to obtain distortionless carrier phase while suppressing jamming.Additionally,architecture of high precision Beidou receiver based on anti-jamming antenna arrays is proposed.Finally,the performance of the algorithm is evaluated,including antenna calibration accuracy,carrier phase distortionless accuracy,and carrier phase measurement accuracy without jamming.Meanwhile,the maximal jamming to signal ratio(JSR)and real time kinematic(RTK)positioning accuracy under wideband jamming are also investigated.The experimental results based on the real-life Beidou signals show that the proposed method has an excellent performance for precise relative positioning under jamming when compared with other anti-jamming methods.

Model informed precision medicine of Chinese herbal medicines formulas-A multi-scale mechanistic intelligent model

Recent trends suggest that Chinese herbal medicine formulas(CHM formulas)are promising treatments for complex diseases.To characterize the precise syndromes,precise diseases and precise targets of the precise targets between complex diseases and CHM formulas,we developed an artificial intelligence-based quantitative predictive algorithm(DeepTCM).DeepTCM has gone through multilevel model calibration and validation against a comprehensive set of herb and disease data so that it accurately captures the complex cellular signaling,molecular and theoretical levels of traditional Chinese medicine(TCM).As an example,our model simulated the optimal CHM formulas for the treatment of coronary heart disease(CHD)with depression,and through model sensitivity analysis,we calculated the balanced scoring of the formulas.Furthermore,we constructed a biological knowledge graph representing interactions by associating herb-target and gene-disease interactions.Finally,we experimentally confirmed the therapeutic effect and pharmacological mechanism of a novel model-predicted intervention in humans and mice.This novel multiscale model opened up a new avenue to combine“disease syndrome”and“macro micro”system modeling to facilitate translational research in CHM formulas.

A new method for evaluating the firing precision of multiple launch rocket system based on Bayesian theory

How to effectively evaluate the firing precision of weapon equipment at low cost is one of the core contents of improving the test level of weapon system.A new method to evaluate the firing precision of the MLRS considering the credibility of simulation system based on Bayesian theory is proposed in this paper.First of all,a comprehensive index system for the credibility of the simulation system of the firing precision of the MLRS is constructed combined with the group analytic hierarchy process.A modified method for determining the comprehensive weight of the index is established to improve the rationality of the index weight coefficients.The Bayesian posterior estimation formula of firing precision considering prior information is derived in the form of mixed prior distribution,and the rationality of prior information used in estimation model is discussed quantitatively.With the simulation tests,the different evaluation methods are compared to validate the effectiveness of the proposed method.Finally,the experimental results show that the effectiveness of estimation method for firing precision is improved by more than 25%.

Stability Analysis and Performance Evaluation of Additive Mixed-Precision Runge-Kutta Methods

Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were previously proposed and analyzed.These specially designed methods use reduced precision for the implicit computations and full precision for the explicit computations.In this work,we analyze the stability properties of these methods and their sensitivity to the low-precision rounding errors,and demonstrate their performance in terms of accuracy and efficiency.We develop codes in FORTRAN and Julia to solve nonlinear systems of ODEs and PDEs using the mixed-precision additive Runge-Kutta(MP-ARK)methods.The convergence,accuracy,and runtime of these methods are explored.We show that for a given level of accuracy,suitably chosen MP-ARK methods may provide significant reductions in runtime.

Developing risk models and subtypes of autophagy-associated LncRNAs for enhanced prognostic prediction and precision in therapeutic approaches for liver cancer patients

Background:Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs(ARLncRNAs)on the prognosis of hepatocellular carcinoma(HCC).Methods:We analyzed 371 HCC samples from TCGA,identifying expression networks of ARLncRNAs using autophagy-related genes.Screening for prognostically relevant ARLncRNAs involved univariate Cox regression,Lasso regression,and multivariate Cox regression.A Nomogram was further employed to assess the reliability of Riskscore,calculated from the signatures of screened ARLncRNAs,in predicting outcomes.Additionally,we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis,using consensus clustering to identify subgroups related to ARLncRNAs.Results:The screening process identified 27 ARLncRNAs,with 13 being associated with HCC prognosis.Consequently,a set of signatures comprising 8 ARLncRNAs was successfully constructed as independent prognostic factors for HCC.Patients in the high-risk group showed very poor prognoses in most clinical categories.The Riskscore was closely related to immune cell scores,such as macrophages,and the DEGs between different groups were implicated in metabolism,cell cycle,and mitotic processes.Notably,high-risk group patients demonstrated a significantly lower IC50 for Paclitaxel,suggesting that Paclitaxel could be an ideal treatment for those at elevated risk for HCC.We further identified C2 as the Paclitaxel subtype,where patients exhibited higher Riskscores,reduced survival rates,and more severe clinical progression.Conclusion:The 8 signatures based on ARLncRNAs present novel targets for prognostic prediction in HCC.The drug candidate Paclitaxel may effectively treat HCC by impacting ARLncRNAs expression.With the identification of ARLncRNAsrelated isoforms,these results provide valuable insights for clinical exploration of autophagy mechanisms in HCC pathogenesis and offer potential avenues for precision medicine.