基于Ladybug Tools对日光温室的方位角与光照模拟

日光温室是重要的农业基础设施,温室的方位角是评判温室结构设计是否合理的基础之一。选择较好的温室方位角拥有更优的采光性能是本文研究的意义。为此,选择三维可视化技术模拟分析日光温室的光照值,为更好地研究分析日光温室采光性能及光照变化提供较为直观的途径和方法,为优化设计高产、高效、节能的日光温室提供一定的辅助作用。通过实测数据和模拟数据的对比,说明Ladybug Tools软件模拟不同方位角的变化及和田日光温室的光照的对比准确度较高,误差不超过5%,为研究日光温室在光照方面的设计提供了一定的理论依据和参考。

Nutritional Assessment Tools for Patients with Cancer:A Narrative Review

Cancer patients are at high risk of malnutrition,which can lead to adverse health outcomes such as prolonged hospitalization,increased complications,and increased mortality.Accurate and timely nutritional assessment plays a critical role in effectively managing malnutrition in these patients.However,while many tools exist to assess malnutrition,there is no universally accepted standard.Although different tools have their own strengths and limitations,there is a lack of narrative reviews on nutritional assessment tools for cancer patients.To address this knowledge gap,we conducted a non-systematic literature search using PubMed,Embase,Web of Science,and the Cochrane Library from their inception until May 2023.A total of 90 studies met our selection criteria and were included in our narrative review.We evaluated the applications,strengths,and limitations of 4 commonly used nutritional assessment tools for cancer patients:the Subjective Global Assessment(SGA),Patient-Generated Subjective Global Assessment(PG-SGA),Mini Nutritional Assessment(MNA),and Global Leadership Initiative on Malnutrition(GLIM).Our findings revealed that malnutrition was associated with adverse health outcomes.Each of these 4 tools has its applications,strengths,and limitations.Our findings provide medical staff with a foundation for choosing the optimal tool to rapidly and accurately assess malnutrition in cancer patients.It is essential for medical staff to be familiar with these common tools to ensure effective nutritional management of cancer patients.

On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

A review on the multi-scaled structures and mechanical/thermal properties of tool steels fabricated by laser powder bed fusion additive manufacturing

The laser powder bed fusion(LPBF) process can integrally form geometrically complex and high-performance metallic parts that have attracted much interest,especially in the molds industry.The appearance of the LPBF makes it possible to design and produce complex conformal cooling channel systems in molds.Thus,LPBF-processed tool steels have attracted more and more attention.The complex thermal history in the LPBF process makes the microstructural characteristics and properties different from those of conventional manufactured tool steels.This paper provides an overview of LPBF-processed tool steels by describing the physical phenomena,the microstructural characteristics,and the mechanical/thermal properties,including tensile properties,wear resistance,and thermal properties.The microstructural characteristics are presented through a multiscale perspective,ranging from densification,meso-structure,microstructure,substructure in grains,to nanoprecipitates.Finally,a summary of tool steels and their challenges and outlooks are introduced.

Phosphorylated protein chip combined with artificial intelligence tools for precise drug screening

We have developed a protein array system,named"Phospho-Totum",which reproduces the phosphorylation state of a sample on the array.The protein array contains 1471 proteins from 273 known signaling pathways.According to the activation degrees of tyrosine kinases in the sample,the corresponding groups of substrate proteins on the array are phosphorylated under the same conditions.In addition to measuring the phosphorylation levels of the 1471 substrates,we have developed and performed the artificial intelligence-assisted tools to further characterize the phosphorylation state and estimate pathway activation,tyrosine kinase activation,and a list of kinase inhibitors that produce phosphorylation states similar to that of the sample.The Phospho-Totum system,which seamlessly links and interrogates the measurements and analyses,has the potential to not only elucidate pathophysiological mechanisms in diseases by reproducing the phosphorylation state of samples,but also be useful for drug discovery,particularly for screening targeted kinases for potential drug kinase inhibitors.

Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.

Emerging molecules,tools,technology,and future of surgical knife in gastroenterology

The 21^(st) century has started with several innovations in the medical sciences,with wide applications in health care management.This development has taken in the field of medicines(newer drugs/molecules),various tools and technology which has completely changed the patient management including abdominal surgery.Surgery for abdominal diseases has moved from maximally invasive to minimally invasive(laparoscopic and robotic)surgery.Some of the newer medicines have its impact on need for surgical intervention.This article focuses on the development of these emerging molecules,tools,and technology and their impact on present surgical form and its future effects on the surgical intervention in gastroenterological diseases.

Research Progress of Measuring Tools for Patient Medication Compliance

This paper introduced the content, compilation process, reliability and validity, scoring method of the evaluation tool for patients’ medication compliance at home and abroad, and reviewed the research progress of the tool. The evaluation method, dimension, scoring method, evaluation content and application scope of the tool were compared, so as to provide reference for nurses to comprehensively and accurately evaluate patients’ medication status.

Further Analysis of Machine Tool Dimensional Accuracy and Thermal Stability under Varying Floor Temperature

Machining is as old as humanity, and changes in temperature in both the machine’s internal and external environments can be of great concern as they affect the machine’s thermal stability and, thus, the machine’s dimensional accuracy. This paper is a continuation of our earlier work, which aimed to analyze the effect of the internal temperature of a machine tool as the machine is put into operation and vary the external temperature, the machine floor temperature. Some experiments are carried out under controlled conditions to study how machine tool components get heated up and how this heating up affects the machine’s accuracy due to thermally induced deviations. Additionally, another angle is added by varying the machine floor temperature. The parameters mentioned above are explored in line with the overall thermal stability of the machine tool and its dimensional accuracy. A Robodrill CNC machine tool is used. The CNC was first soaked with thermal energy by gradually raising the machine floor temperature to a certain level before putting the machine in operation. The machine was monitored, and analytical methods were deplored to evaluate thermal stability. Secondly, the machine was run idle for some time under raised floor temperature before it was put into operation. Data was also collected and analyzed. It is observed that machine thermal stability can be achieved in several ways depending on how the above parameters are joggled. This paper, in conclusion, reinforces the idea of machine tool warm-up process in conjunction with a carefully analyzed and established machine floor temperature variation for the approximation of the machine tool’s thermally stability to map the long-time behavior of the machine tool.

基于Arc Hydro Tools的祁连山国家公园小流域划分

以祁连山国家公园为研究区,基于30 m分辨率数字高程模型数据,利用Arc Hydro Tools水文数据模型与河网密度法,在确定最佳集水面积阈值的基础上提取河网并划分小流域,统计小流域的部分特征参数.结果表明,集水面积阈值与河网密度呈幂函数关系,祁连山国家公园最佳集水面积阈值为6000栅格数;河网提取结果与全国1∶250000三级水系流域数据集的河网套合差为2.62%,叠加至遥感图像对比,发现提取结果相较于全国1∶250000三级水系流域数据集更接近真实水系;共划分祁连山国家公园小流域5717个,约80%的小流域面积在3~50 km2.从提取效率和结果精度来看,Arc Hydro Tools模型与河网密度法相结合可较准确提取山地区域河网及小流域.

基于Wordsmith Tools石油化工英语句法特征及其翻译探究

石油行业全球化的日益普及使得我国对该领域文本翻译的需求与日俱增,石油化工英语的翻译在石油行业的发展中也发挥着越来越重要的作用,所以它的重要性也显而易见。本文基于Wordsmith Tools语料分析软件进行数据分析,对石油化工英语的句法特征进行了探究;针对这些特征,提出了相应的翻译技巧,以期提高石油化工英语的翻译质量,使译文更加符合石油领域的标准。

Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

In-process damage to a cutting tool degrades the surface􀀀nish of the job shaped by machining and causes a signi􀀀cant􀀀nancial loss.This stimulates the need for Tool Condition Monitoring(TCM)to assist detection of failure before it extends to the worse phase.Machine Learning(ML)based TCM has been extensively explored in the last decade.However,most of the research is now directed toward Deep Learning(DL).The“Deep”formulation,hierarchical compositionality,distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform eciently in a high-noise environment of cross-domain machining.With this motivation,the design of dierent CNN(Convolutional Neural Network)architectures such as AlexNet,ResNet-50,LeNet-5,and VGG-16 is presented in this paper.Real-time spindle vibrations corresponding to healthy and various faulty con􀀀gurations of milling cutter were acquired.This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form,i.e.,spectrogram.The model is trained,tested,and validated considering dierent datasets and showcased promising results.

A Study on the Effect of Sterilization of Barber's Tools with Ozone Box

In this paper the authors studied the sterilization effect of barber's tools with ozone box.Theresults showed that the ozone box could kill the bacteria on the surface of hair-cutting tools effectively.and under the experimental conditions the ozone box did not corrode the tools.Therefore,it is feasibleto use ozone genrator to sterilize hair-cutting tools.

Characterization of a novel fluidic friction-reduction tool used in extended-reach well considering periodic particle-laden jet

Fluidic oscillators(FOs)can be used as an efficient fluidic vibration tool to solve high friction problems in extended-reach wells.However,the complex mechanism of FOs makes the design challenging,and the dynamic erosion behavior inside FOs is still unclear.In this paper,new FOs are proposed and the working characteristics under the influence of periodic particle-laden jets are investigated.Firstly,the results reveal the working mechanism of new FOs,showing that the generation of pressure pulses is closely connected with periodic jet switching and the development of vortices.Secondly,the important performance parameters,i.e.,pressure pulse and oscillation frequency,are extensively studied through numerical simulation and experimental verification.It is found that the performance can be optimized by adjusting the tool structure according to different engineering requirements.Finally,the oscillating solid-liquid two-phase flow inside FO is studied.It is demonstrated that the accumulation of particles leads to a significant reduction in performance.The results also reveal five locations that are susceptible to erosion and the erosion behavior of these locations are studied.It has been shown that the periodic jet causes fluctuations in the amount of erosion at the outlet and splitter.This research can provide valuable references for the design and optimization of vibration friction-reduction tools.

Spatial Expression of Assembly Geometric Errors for Multi-axis Machine Tool Based on Kinematic Jacobian-Torsor Model

Assembly geometric error as a part of the machine tool system errors has a significant influence on the machining accuracy of the multi-axis machine tool.And it cannot be eliminated due to the error propagation of components in the assembly process,which is generally non-uniformly distributed in the whole working space.A comprehensive expression model for assembly geometric error is greatly helpful for machining quality control of machine tools to meet the demand for machining accuracy in practice.However,the expression ranges based on the standard quasistatic expression model for assembly geometric errors are far less than those needed in the whole working space of the multi-axis machine tool.To address this issue,a modeling methodology based on the Jacobian-Torsor model is proposed to describe the spatially distributed geometric errors.Firstly,an improved kinematic Jacobian-Torsor model is developed to describe the relative movements such as translation and rotation motion between assembly bodies,respectively.Furthermore,based on the proposed kinematic Jacobian-Torsor model,a spatial expression of geometric errors for the multi-axis machine tool is given.And simulation and experimental verification are taken with the investigation of the spatial distribution of geometric errors on five four-axis machine tools.The results validate the effectiveness of the proposed kinematic Jacobian-Torsor model in dealing with the spatial expression of assembly geometric errors.

Efect of Micro Abrasive Slurry Jet Polishing on Properties of Coated Cemented Carbide Tools

Owing to the popularization of coating technology, physical Vapor Deposition (PVD) coated tools have become indispensable in the cutting process. Additionally, the post-treatment of coated tools applied to industrial production can efectively enhance the surface quality of coating. To improve the processing performance of coated tools, micro abrasive slurry jet (MASJ) polishing technology is frst applied to the post-treatment of coated tools. Subsequently, the efects of process parameters on the surface quality and cutting thickness of coating are investigated via single-factor experiments. In the experiment, the best surface roughness is obtained by setting the working pressure to 0.4 MPa, particle size to 3 μm, incidence angle to 30°, and abrasive mass concentration to 100 g/L. Based on the results of the single-factor experiments, combination experiments are designed, and three types of coated tools with diferent surface qualities and coating thicknesses are obtained. The MASJ process for the post-treatment of coated tools is investigated based on a tool wear experiment and the efects of cutting parameters on the cutting force and workpiece surface quality of three types of cutting tools. The result indicates that MASJ machining can efectively improve the machining performance of coated tools.

Scheduling a Single-Arm Multi-Cluster Tool With a Condition-Based Cleaning Operation

As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required in chemical vapor deposition, coating processes, etc. They increase scheduling complexity in cluster tools. In this paper, we focus on scheduling single-arm multi-cluster tools with chamber cleaning operations subject to wafer residency time constraints. When a chamber is being cleaned, it can be viewed as processing a virtual wafer. In this way, chamber cleaning operations can be performed while wafer residency time constraints for real wafers are not violated. Based on such a method, we present the necessary and sufficient conditions to analytically check whether a single-arm multi-cluster tool can be scheduled with a chamber cleaning operation and wafer residency time constraints. An algorithm is proposed to adjust the cycle time for a cleaning operation that lasts a long cleaning time.Meanwhile, algorithms for a feasible schedule are also derived.And an algorithm is presented for operating a multi-cluster tool back to a steady state after the cleaning. Illustrative examples are given to show the application and effectiveness of the proposed method.

Scheduling Dual-Arm Multi-Cluster Tools With Regulation of Post-Processing Time

As wafer circuit width shrinks down to less than ten nanometers in recent years,stringent quality control in the wafer manufacturing process is increasingly important.Thanks to the coupling of neighboring cluster tools and coordination of multiple robots in a multi-cluster tool,wafer production scheduling becomes rather complicated.After a wafer is processed,due to high-temperature chemical reactions in a chamber,the robot should be controlled to take it out of the processing chamber at the right time.In order to ensure the uniformity of integrated circuits on wafers,it is highly desirable to make the differences in wafer post-processing time among the individual tools in a multicluster tool as small as possible.To achieve this goal,for the first time,this work aims to find an optimal schedule for a dual-arm multi-cluster tool to regulate the wafer post-processing time.To do so,we propose polynomial-time algorithms to find an optimal schedule,which can achieve the highest throughput,and minimize the total post-processing time of the processing steps.We propose a linear program model and another algorithm to balance the differences in the post-processing time between any pair of adjacent cluster tools.Two industrial examples are given to illustrate the application and effectiveness of the proposed method.

Performance of novel 3D printing tools in removing coronary-artery calcification tissue

Rotational atherectomy is an effective treatment for severe vascular calcification obstruction,and relies on high-speed grinding(typically 130,000–210,000 r/min)with miniature grinding tools to remove calcified tissue and restore blood flow.However,reports of intraoperative complications are common because of the grinding force,temperature,and debris directly acting on the body during the grinding process,which can easily cause damage to patients.In this study,three novel grinding tools were designed and fabricated and a series of experiments have been conducted to analyze the effects of tool geometry and parameters on grinding performance,that is,force,temperature,and specimen surface morphology.The results show that these tools can effectively remove simulated calcified tissue and that they have two motions,rotation and revolution,in the tube.At higher rotational speeds,grinding force and temperature increase noticeably,while the amount of debris decreases significantly.In addition,by observing the surface morphology of the specimens,we concluded that the material removal rate per unit time is influenced by both rotational speed and tool geometry,and that high rotational speed and a rough tool surface can improve the material removal rate efficiently.

An evidenced-based diagnostic tool for superior semicircular canal dehiscence syndrome

Purpose:To construct a symptoms-based prediction tool to assess the likelihood of superior canal dehiscence(SSCD)on high-resolution CT.Materials and methods:Mathematical modeling was employed to predict radiologic evidence of SSCD at a tertiary neurotology referral center.Results:A total of 168 patients were included,of which 118 had imaging-confirmed SSCD.On univariate analysis significant predictors of SSCD presence were:sound/pressure-induced vertigo(p?0.006),disequilibrium(p?0.008),hyperacusis(p?0.008),and autophony(p?0.034).Multivariate analysis enabled a 14-point symptom-weighted tool to be developed,wherein a score of6 raised the suspicion of SSCD(70%likelihood of being present),R2?0.853.Conclusions:The likelihood of SSCD on CT scan can be determined with a high degree of certainty based on symptoms recorded at presentation.Using the evidenced-based diagnostic tool validated herein,a score6 with any symptom combination justifies ordering a CT scan.