Parameter optimization and experiment of the negative pressure precision seed-metering device for wheat

In order to ensure the most reasonable distribution of wheat seeds in the field to improve seeding quality and uniformity,a set of negative pressure precision seed-metering device was designed,which shares a hollow shaft.Every seed-metering device can sow two rows of wheat.By the STAR-CCM+,the analysis of nephogram,vectogram and streamline graph showed that more ideal structural parameters of the seed-metering device are 0.5 mm width of the slit sucking seed(WSS),150-200 mm diameter of the seed-metering disc(DSD),2.0 mm axial depth of air chamber in the seed-metering disc(ADS),and arc-shaped cross-section shape of the ring groove sucking seed(CSGS).Single-factor test on the JPS-12 test-bed analyzed the influence of the CSGS,WSS,DSD,and ADS on the qualified index(Iq),multiple index(Imul),miss index(Imiss)and coefficient of variation of qualified seed spacing(CV).Through the orthogonal on the JPS-12 test-bed,it is found that the influence of vacuum negative pressure and seed-metering device shaft speed is significant on the Iq,Imiss and Imul.Based on these,the structural parameters of the seed-metering device were optimized.The DSD is 180 mm,the WSS is 0.7 mm,the ADS is 2.5 mm,and the CSGS is arc-shaped.The optimization seed-metering device was tested on the JPS-12 test-bed.The Iq is 86.66%,the Imiss is 5.09%,the Imul is 8.25%,and the CV is 24.50%.These testing results fully coincide with the standard JB/T 10293-2013 Specifications of single seed drill(precision drill).The seed-metering device meets fully the requirements for wheat precision seeding.

Optimized design of the pneumatic precision seed-metering device for carrots

The small size, light weight, irregular shape, and impurities that characterize seed groups increase clogging tendencies in the traditional seed-metering device thereby making it difficult to achieve high-speed precision seeding. A pneumatic seed-metering device with good seed-filling performance for carrot was designed in this study. By analyzing the movement state of seeds in the device under the theoretical condition, it was concluded that the minimum critical negative pressure value of air chamber was 0.32 kPa, which provided a theoretical basis for simulation and testing. ANSYS 17.0 Software was used to simulate the shape of the seeding plate hole. By comparing the pressure, air flow stability between the suction surface and the plug removal surface of the convection field, it was concluded that the conical hole was optimal. A bench verification test was conducted on the device. The average qualified rate, missing rate, and replaying rate were 81.48%, 4.07%, and 14.45%, respectively, which provided a strong reference for the design of carrot precision seed-metering device.

Design of motor-driven precision seed-metering device with improved fuzzy PID controller for small peanut planters

Traditional passive ground wheel drive of peanut planters has displayed poor high-speed seeding performance and the slippage caused in case of sticky and wet soil.Given this,an integrated electric-driven precision seed metering device and controller were designed,which features the application of improved fuzzy PID algorithm.Based on a small peanut planter with one ridge width and duplicate rows,the servo motor drive is used to replace the traditional passive ground wheel.In addition,the satellite speed measurement is employed to complete the electric driving and controlling modification of the seed meter and precise seeding control.A working process mathematical model for the peanut metering device was established to conduct motor speed and field tests which aim at comparing performances between the conventional and the improved fuzzy PID controls.The motor speed trial shows that the average error of the actual speed of the improved fuzzy PID motor was±1 rad/min,and the coefficient of variation was less than 1%.Against the conventional one,it can better suppress overshoot and improve the response speed.The stable output speed can still be obtained even in case of step changes.Field tests show that when working at medium and low speeds,the qualified rate of plant spacing was greater than 98%,and the rate of missed sowing is<2%;while working at high speed,the qualified rate was greater than 94%,and the rate of missed sowing was less than 4%.The average plant spacing qualification rate of the seed device increased by 6.72%;compared with other electric-driven peanut seed meters,the plant spacing qualification rate increased by 4%during high-speed sowing.In summary,this study has provided an effective technical reference for high-speed precision planting of peanuts.

Design and experiment of conical diversion virus-free potato minituber precision seed-metering device

In order to accelerate the development of sowing mechanization of virus-free potato minituber,a conical diversion virus-free potato minituber precision seed-metering Device was designed according to the structural characteristics and agronomic requirements of virus-free potato minituber.The device is mainly composed of conical turntable,transmission shaft,outer baffle of seed-metering Device,baffle,seed outlet,seed cleaning device,type hole,etc.The working principle of conical diversion precision seed-metering device for virus-free potato minituber was expounded,and the stress analysis of virus-free potato minituber in each region was carried out.By EDEM discrete element simulation software,the structure of the type hole is optimized to determine the optimal structure of the type hole and according to the physical characteristics of virus-free potato minituber,the single factor experiments of the effects of length of type hole,cone disc speed and cone disc angle on seed filling performance were completed.The orthogonal regression test was carried out with the length of type hole,cone disc speed,and cone disc angle as the test objects,and the leakage rate and qualified rate as the response indexes.The regression models of leakage rate,replay rate,and qualified rate were established,and the parameters of the regression model were optimized.The optimal parameter combination is that the length of type hole is 33.61 mm,the cone disc speed is 6.35 r/min,and the cone disc angle is 26.59°.Bench test was carried out under the optimal conditions,the leakage rate was 3.80%,the replay rate was 0.80%,and the qualified rate was 95.40%,which was basically consistent with the prediction results of the regression model,and met the requirements of precision sowing of virus-free potato minituber.

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.

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.

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.

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.

Application of the Electrical Resistivity Method in Precision Agriculture of Coffee Cultivation, in the Kabiri Area, Ícolo e Bengo Township, Luanda, Angola

The electrical resistivity method is a geophysical tool used to characterize the subsoil and can provide an important information for precision agriculture. The lack of knowledge about agronomic properties of the soil tends to affect the agricultural coffee production system. Therefore, research related to geoelectrical properties of soil such as resistivity for characterization the region of the study for coffee cultivation purposes can improve and optimize the production. This resistivity method allows to investigate the subsurface through different techniques: 1D vertical electrical sounding and electrical imaging. The acquisition of data using these techniques permitted the creation of 2D resistivity cross section from the study area. The geoelectrical data was acquired by using a resistivity meter equipment and was processed in different softwares. The results of the geoelectrical characterization from 1D resistivity model and 2D resistivity electrical sections show that in the study area of Kabiri, there are 8 varieties of geoelectrical layers with different resistivity or conductivity. Near survey in the study area, the lowest resistivity is around 0.322 Ω·m, while the highest is about 92.1 Ω·m. These values illustrated where is possible to plant coffee for suggestion of specific fertilization plan for some area to improve the cultivation.

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.

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.

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.

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%.

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.

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.

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.

Comparison of the precision of glacier flow rates derived from offset-tracking using Sentinel-2 and Landsat-8/9 imagery

Offset-tracking is an essential method for deriving glacier flow rates using optical imagery.Sentinel-2(S2)and Landsat-8/9(L8/9)are popular optical satellites or constellations for polar studies,offering high spatial resolution with relatively short revisit time,wide swath width,and free accessibility.To evaluate and compare the precision of offset-tracking results yielded with these two kinds of data,in this study S2 and L8/9 imagery observed in Petermann Glacier in Greenland,Karakoram in High-Mountains Asia,and Amery Ice Shelf in the Antarctic are analyzed.Outliers and various systematic error sources in the offset-tracking results including orbital and strip errors were analyzed and eliminated at the pre-process stage.Precision at the off-glacier(bare rock)region was evaluated by presuming that no deformation occurred;then for both glacierized and the off-glacier regions,precision of velocity time series was evaluated based on error propagation theory.The least squares method based on connected components was used to solve flow rates time series based on multi-pair images offset-tracking.The results indicated that S2 achieved slightly higher precision than L8/9 in terms of both single-pair derived displacements and least square solved daily flow rates time series.Generally,the RMSE of daily velocity is 26%lower for S2 than L8/9.Moreover,S2 provided higher temporal resolution for monitoring glacier flow rates.

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.

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.