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%.

Re-evaluating the role of pelvic radiation in the age of modern precision medicine and systemic therapy

The efficacy of pelvic radiation in the management of locally advanced stage rectal cancer has come under scrutiny in the context of modern precision medicine and systemic therapy as evidenced by recent clinical trials such as FOWARC(J Clin Oncol 2019;37:3223-3233),NCT04165772(N Engl J Med 2022;386:2363-2376),and PROSPECT(N Engl J Med 2023;389:322-334).In this review,we comprehensively assess these pivotal trials and offer additional insights into the evolving role of pelvic radiation in contemporary oncology.

Evaluating Global Navigation Satellite System (GNSS) Constellation Performance for Unmanned Aerial Vehicle (UAV) Navigation Precision

Accurate localization is paramount for unmanned aerial vehicles (UAVs) spanning various technical and industrial domains, necessitating a comprehensive assessment of global navigation satellite system (GNSS) precision. This study investigates the performance of distinct GNSS constellations in determining the precise location of a building utilizing a high-precision GNSS receiver. The receiver, incorporating advanced multi-frequency and full-constellation positioning capabilities, was integrated with a smartphone via Bluetooth to enable the UAV’s acquisition of centimeter-level positioning data. Sequential utilization of single satellite systems—such as GPS-only, GLONASS-only, Galileo-only, SBAS-only, and BeiDou-only—facilitated the documentation of latitude and longitude coordinates for the designated building. Subsequent comparison of these coordinates with a specialized Geographic Information System (GIS) was conducted to evaluate their positional accuracy. The comparative analysis underscores significant variability in the precision offered by each satellite constellation, providing valuable insights for optimizing UAV navigation across GIS, IoT, construction, and other sectors requiring high-precision localization. This research underscores the significance of high-precision GNSS receivers in enhancing UAV-based geospatial assessments, emphasizing the critical selection of appropriate satellite systems for tailored localization tasks. The study contributes to advancing UAV navigation strategies, ensuring robust and accurate geospatial data collection within diverse operational frameworks.

High Precision Guidance System of the Gravity-1 Launch Vehicle

In the development of the Gravity-1 launch vehicle, solid rocket motors without a thrust termination mechanism created great challenges for ascent guidance. To resolve this problem, the Gravity-1 GNC system used cross product guidance in the core 2nd stage, and a nonlinear adaptive guidance algorithm in core 3rd stage, in order to achieve high orbit injection precision. On January 11, 2024, the Gravity-1 launch vehicle successfully carried out its maiden flight from a mobile sea platform off the coast of Haiyang in Shandong province, inserting its payload into a low earth orbit at an altitude of 500 kilometers, validating the guidance algorithm.

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.

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.

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.

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.

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.

Precision based approach to tailoring radiotherapy in the multidisciplinary management of pediatric central nervous system tumors

Modern day survivorship from childhood malignancies is estimated to be over 80%.However,central ner-vous system tumors remain the leading cause of cancer mortality in children and is the most common solid tumor in this population.Improved survivorship is,in part,a result of improved multidisciplinary care,of-ten with a combination of surgery,radiation therapy,and systemic therapy.With improved survival,long term effects of treatment and quality of life impacts have been recognized and pose a challenge to maxi-mize the therapeutic ratio of treatment.It has been increasingly more apparent that precise risk stratifica-tion,such as with the inclusion of molecular classification,is instrumental in efforts to tailor radiotherapy for appropriate treatment,generally towards de-intensification for this vulnerable patient population.In ad-dition,advances in radiotherapy techniques have allowed greater conformality and accuracy of treatment for those who do require radiotherapy for tumor control.Ongoing efforts to tailor radiotherapy,including de-escalation,omission,or intensification of radiotherapy,continue to improve as increasing insight into tumor heterogeneity is recognized,coupled with advances in precision medicine employing novel molecularly-targeted therapeutics.

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.

Design methodology of a mini-missile considering flight performance and guidance precision

The design of mini-missiles(MMs)presents several novel challenges.The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision.The miniaturization of the size of MMs makes the design of the guidance,navigation,and control(GNC)have a larger-thanbefore impact on the main-body design(shape,motor,and layout design)and its design objective,i.e.,flight performance.Pursuing a trade-off between flight performance and guidance precision,all the relevant interactions have to be accounted for in the design of the main body and the GNC system.Herein,a multi-objective and multidisciplinary design optimization(MDO)is proposed.Disciplines pertinent to motor,aerodynamics,layout,trajectory,flight dynamics,control,and guidance are included in the proposed MDO framework.The optimization problem seeks to maximize the range and minimize the guidance error.The problem is solved by using the nondominated sorting genetic algorithm II.An optimum design that balances a longer range with a smaller guidance error is obtained.Finally,lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.

User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management

In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding markers.Although most rNETs patients have a favourable prognosis,the median survival period significantly decreases when high-risk factors,such as larger tumours,poorer differentiation,and lymph node metastasis exist,are present.Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care,but their complex calculation formulae limit clinical use.Moreover,the prognostic models that have been developed for rNETs to date still have several limitations,such as insufficient sample sizes and the lack of external validation.A high-quality prognostic model for rNETs would guide treatment and follow-up,enabling the precise formulation of individual patient treatment and follow-up plans.The future development of models for rNETs should involve closer collab-oration with statistical experts,which would allow the construction of clinical prediction models to be standardized and robust,accurate,and highly general-izable prediction models to be created,ultimately achieving the goal of precision medicine.

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.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era

Esophageal squamous cell carcinoma(ESCC)is the most common histological type of esophageal cancer with a poor prognosis.Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients.With the advancement of artificial intelligence(AI)technology and the proliferation of medical digital information,AI has demonstrated promising sensitivity and accuracy in assisting precise detection,treatment decision-making,and prognosis assessment of ESCC.It has become a unique opportunity to enhance comprehen-sive clinical management of ESCC in the era of precision oncology.This review examines how AI is applied to the diagnosis,treatment,and prognosis assessment of ESCC in the era of precision oncology,and analyzes the challenges and potential opportunities that AI faces in clinical translation.Through insights into future prospects,it is hoped that this review will contribute to the real-world application of AI in future clinical settings,ultimately alleviating the disease burden caused by ESCC.

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.

Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.

Diagnosing Thermalization Dynamics of Non-Hermitian Quantum Systems via GKSL Master Equations

The application of the eigenstate thermalization hypothesis to non-Hermitian quantum systems has become one of the most important topics in dissipative quantum chaos, recently giving rise to intense debates. The process of thermalization is intricate, involving many time-evolution trajectories in the reduced Hilbert space of the system. By considering two different expansion forms of the density matrices adopted in the biorthogonal and right-state time evolutions, we derive two versions of the Gorini–Kossakowski–Sudarshan–Lindblad(GKSL)master equations describing the non-Hermitian systems coupled to a bosonic heat bath in thermal equilibrium. By solving the equations, we identify a sufficient condition for thermalization under both time evolutions, resulting in Boltzmann biorthogonal and right-eigenstate statistics, respectively. This finding implies that the recently proposed biorthogonal random matrix theory needs an appropriate revision. Moreover, we exemplify the precise dynamics of thermalization and thermodynamic properties with test models.

Hepatitis B virus genotypes in precision medicine of hepatitis Brelated hepatocellular carcinoma:Where we are now

Hepatitis B virus(HBV)infection is a major player in chronic hepatitis B that may lead to the development of hepatocellular carcinoma(HCC).HBV genetics are diverse where it is classified into at least 9 genotypes(A to I)and 1 putative genotype(J),each with specific geographical distribution and possible different clinical outcomes in the patient.This diversity may be associated with the precision medicine for HBV-related HCC and the success of therapeutical approaches against HCC,related to different pathogenicity of the virus and host response.This Editorial discusses recent updates on whether the classification of HBV genetic diversity is still valid in terms of viral oncogenicity to the HCC and its precision medicine,in addition to the recent advances in cellular and molecular biology technologies.