Accurate Method For Evaluation Research Articles

Modified Method for Accurate Evaluation of Overturning Limit on Restrainer-Reinforced Single-Column Pier Bridges

Modified Method for Accurate Evaluation of Overturning Limit on Restrainer-Reinforced Single-Column Pier Bridges

Monitoring Helicoverpa armigera Damage with PRISMA Hyperspectral Imagery: First Experience in Maize and Comparison with Sentinel-2 Imagery

The cotton bollworm (CBW) poses a significant risk to maize crops worldwide. This study investigated whether hyperspectral satellites offer an accurate evaluation method for monitoring maize ear damage caused by CBW larvae. The study analyzed the records of maize ear damage for four maize fields in Southeast Hungary, Csongrád-Csanád County, in 2021. The performance of Sentinel-2 bands, PRISMA bands, and synthesized Sentinel-2 bands was compared using linear regression, partial least squares regression (PLSR), and two-band vegetation index (TBVI) methods. The best newly developed indices derived from the TBVI method were compared with existing vegetation indices. In mid-early grain maize fields, narrow bands of PRISMA generally performed better than wide bands, unlike in sweet maize fields, where the Sentinel-2 bands performed better. In grain maize fields, the best index was the normalized difference of λA = 571 and λB = 2276 (R2 = 0.33–0.54, RMSE 0.06–0.05), while in sweet maize fields, the best-performing index was the normalized difference of green (B03) and blue (B02) Sentinel-2 bands (R2 = 0.54–0.72, RMSE 0.02). The findings demonstrate the advantages and constraints of remote sensing for plant protection and pest monitoring.

Development of a fuzzy logic-based model for assessing the reliability of relay protection systems

This article presents a reliability assessment model for relay protection devices using fuzzy logic. It introduces an algorithm for simulating these devices, employing the Mamdani model with an "if-then" rule base. Inputs include the percentage of correct operation and the frequency of correct and incorrect operations. Implemented in Matlab with 21 rules, the fuzzy logic model uses triangular membership functions for input and output variables, with defuzzification via the center of gravity method. The model was tested using statistical data from SIEMENS SIPROTEC terminals, specifically distance protection and differential protection devices for transformers and autotransformers. The assessment considers operation frequencies for 1 to 3 devices, combining statistical and simulation data for a comprehensive analysis. Results show that including operation frequencies improves evaluation accuracy. The proposed model not only assesses current reliability but also predicts future behavior, aiding in the planning and optimization of relay protection systems. This model is valuable for professionals in generating companies, grid organizations, and operational dispatch control entities, helping them analyze relay protection performance and develop strategies to ensure reliable operation. The research focuses on the reliability of relay protection devices in power systems, addressing the need for a more accurate and comprehensive evaluation method. Traditional methods may not fully account for the complexities in modern microprocessor-based protections. This study aims to enhance reliability assessments through a model that integrates statistical data and simulation techniques, ultimately supporting better planning and optimization of relay protection systems

Empirical analysis of the influence of vegetation photosynthetic productivity on negative air ions in forest ecosystems driven by solar radiation

Air pollution stands as the single largest environmental risk to human health globally, and negative air ions (NAIs) serve as essential indicators for measuring regional air quality. They play a crucial role in alleviating the pressure and risks posed by air pollution. NAIs are notably abundant in forests, where the photosynthesis of vegetation serves as a significant factor influencing their presence. However, a dearth of quantitative research exists regarding the relationship between vegetation photosynthesis and NAIs. Current literature only offers qualitative insights, suggesting that vegetation photosynthesis may foster NAIs production. The mechanisms through which the photosynthetic process influences NAIs and the potential contribution of forest vegetation to NAIs remain unclear, lacking quantitative investigation. In this study, Quercus variabilis, a typical tree species in the warm temperate zone, was chosen as the research subject. We examined the dynamic changes in NAIs and net ecosystem productivity (NEP) from 2019 to 2021. The results demonstrated a strong correlation between NAIs and NEP at the ecosystem scale during the primary vegetation growing season. NEP effectively tracked daily changes in NAIs and exhibited a good fit. Notably, the scatter plots of NAIs and NEP exhibited clear stratification under varying photosynthetically active radiation (PAR) intensities. The correlation between NAIs and NEP was significantly higher under high PAR conditions compared to low PAR conditions, with R2 values of 0.458 and 0.367, respectively. Consequently, NEP emerges as a superior indicator for tracking NAIs variation characteristics at the ecosystem scale compared to PAR. This finding offers a rapid and accurate evaluation method for discerning the spatial and temporal distribution patterns of NAIs within forest ecosystems. Such insights provide a theoretical foundation for further assessing the potential contribution of forest vegetation to NAIs.

A Fast Evaluation Method for Spatial Point Measurement Accuracy in a Large-Scale Measurement System

A Fast Evaluation Method for Spatial Point Measurement Accuracy in a Large-Scale Measurement System

Evaluation of maize varieties via multivariate analysis: Roles of ionome, antioxidants, and autophagy in salt tolerance.

Salt stress presents a major obstacle to maize (Zea mays L.) production globally, impeding its growth and development. In this study, we aimed to identify salt-tolerant maize varieties through evaluation using multivariate analysis and shed light on the role of ionome, antioxidant capacity, and autophagy in salt tolerance. We investigated multiple growth indices, including shoot fresh weight, shoot dry weight, plant height, chlorophyll content, electrolyte leakage, potassium and sodium contents, and potassium-to-sodium ratio, in 20 maize varieties at the V3 stage under salt stress (200 mm NaCl). The results showed significant differences in the growth indices, accompanied by a wide range in their coefficient of variation, suggesting their suitability for screening salt tolerance. Based on D values, clustering analysis categorized the 20 varieties into 4 distinct groups. TG88, KN20, and LR888 (group I) emerged as the most salt-tolerant varieties, while YD9, XD903, and LH151 (group IV) were identified as the most sensitive. TG88 showcased nutrient preservation and redistribution under salt stress, surpassing YD9. It maintained nitrogen and iron levels in roots, while YD9 experienced decreases. TG88 redistributed more nitrogen, zinc, and potassium to its leaves, outperforming YD9. TG88 preserved sulfur levels in both roots and leaves, unlike YD9. Additionally, TG88 demonstrated higher enzymatic antioxidant capacity (superoxide dismutase, peroxidase, ascorbate peroxidase, and glutathione reductase) at both the enzyme and gene expression levels, upregulation of autophagy-related (ATG) genes (ZmATG6, ZmATG8a, and ZmATG10), and increased autophagic activity. Overall, this study offers insights into accurate maize varieties evaluation methods and the physiological mechanisms underlying salt tolerance and identifies promising materials for further research.

Research on an Accurate Simulation Modeling and Charge Motion Quantitative Evaluation Method for Ball Mill in Confined Space

A ball mill is a type of complex grinding device. Having knowledge of its charge-load behavior is key to determining the operating conditions that provide the optimum mill throughput. An elaborate description of the charge movement inside the ball mill is essential. This study focuses on a laboratory-scale ball mill and utilizes a discrete element simulation model to investigate the impact of mill speed and ball filling on charge-load behavior. Initially, the EDEM 2.7 (Engineering Discrete Element Method) software contact parameters were calibrated through heap-angle experiments. Subsequently, four charge-motion characteristic parameters were defined and analyzed based on Powell’s theory to understand the variations in charge-load behavior. This research proposes a theoretical calculation model for predicting power in a ball mill, highlighting the significance of the CoC (Center of Circulation) and CoM (Center of Mass) in reflecting changes in charge-load behavior. The theoretical model for mill-power prediction is effective and aligns well with the EDEM simulation and experimental results, providing valuable insights for optimizing large-scale ball mill structures and controlling charge motion during production.

Evaluation of crankshaft phenomenon after posterior fusion for early onset scoliosis using an inverse trigonometric function: a multicenter retrospective cohort study.

The crankshaft phenomenon (CSP) is a corrective loss after posterior surgery for early onset scoliosis (EOS). However, an accurate method for CSP evaluation has yet to be developed. In this study, we evaluated pedicle screw (PS) length and rotation angle using an inverse trigonometric function and investigated the prevalence of the CSP. Fifty patients from nine institutions (mean age 10.6years, male/female ratio 4:46) who underwent early definitive fusion surgery at ≤ 11years of age were included. The rotation angle was calculated as arctan (lateral/frontal PS length) using radiography. Measurements were taken at the apex and lower instrumented vertebra (LIV) immediate, 2-, and 5-year postoperatively. CSP was defined as a rotation angle progression ≥ 5°. We divided patients into CSP and non-CSP groups and measured the demographic parameters, Risser grade, state of the triradiate cartilage, major coronal Cobb angle, T1-T12 length, T1-S1 length, and presence of distal adding-on (DAO). We compared these variables between groups and investigated the correlation between the measured variables and vertebral rotation. Logistic regression analysis investigated factors associated with CSP. The rotation angle progressed by 2.4 and 1.3° over 5years for the apex and LIV, respectively. CSP occurred in 15 cases (30%), DAO in 11 cases (22%), and CSP and DAO overlapped in 4 cases (8%). In the CSP group, the T1-T12 length was low immediate postoperatively. The rotation angle was negatively correlated with preoperative height (r = -0.33), T1-T12 length (r = -0.35), and T1-S1 length (r = -0.30). A lower preoperative T1-T12 length was associated with CSP (odds ratio: 0.996, p = 0.048). CSP occurred in 30% of patients with EOS who underwent definitive fusion. The presence of CSP was associated with a lower preoperative T1-T12 length. Diagnosis, level IV.

Nonlinear chatter and reliability analysis of milling Ti-6Al-4V with slender ball-end milling cutter

Slender ball-end milling cutters with high length/diameter ratio can offer increased accessibility in the deep cavity machining of parts in the aviation industry. In this paper, a comprehensive nonlinear machining dynamics model for milling Ti-6Al-4V with the slender ball-end milling cutter is proposed to predict and understand on cutting stability, vibration characteristics and dynamic reliability. The tool tip dynamics model of the slender ball-end milling cutter is established including the structural nonlinearity of bearing joints, the contact flexibility of joint interfaces and the influence of tool geometry. Generated nonlinear dynamic cutting force is calculated considering the multiple regenerative effect, the nonlinearity of tool away from cutting and the run-out of tool. The effect of process damping is included in the nonlinear time-delayed differential equation of the machining dynamics model. Furthermore, the reliability evaluation method for the dynamic accuracy of milling Ti-6Al-4V workpieces is established with the application of Monte Carlo method and active learning Kriging model. Some milling experiments on Ti-6Al-4V workpiece are designed to validate the proposed model by comparing the predicted and measured results. The milling stability, vibration characteristics and dynamic accuracy reliability of milling Ti-6Al-4V with the slender ball-end milling cutter are discussed. The increase in the fractal dimension of joint interfaces can lead to the improvement of milling stability, and increased fractal roughness results in reduced milling stability. The reliability in the dynamic accuracy of milling Ti-6Al-4V workpiece is 99.26 %, and the distribution parameters in the fractal dimension of joint interfaces have the greatest influence on the reliability of dynamic accuracy.

Field isolation and capture of fluoroquinolones in waters with porous monolith-based multiple channels in-tip microextraction technique prior to HPLC quantification

Field isolation and capture is significant in the accuracy and reliable monitoring of fluoroquinolones (FQs) contents in environmental waters. For this purpose, a new porous monolith-based adsorbent (PMPA) was in-situ fabricated in pipette tip using 1-octyl-3-vinylimidazolium tetrafluoroborate and 1H,1H,2H,2H-heptadecafluorodecyl acrylate as mixed functional monomers. The PMPA prepared under the optimized parameters was characterized by various techniques. In addition, the prepared PMPA was employed as the adsorbent of home-developed multiple channels in-tip microextraction technique (MCIM) for the field sample preparation of FQs. Results revealed that the obtained PMPA exhibited satisfactory capture capability towards studied FQs, and the enrichment factors ranged from 128 to 203. Under the most beneficial extraction conditions, the developed PMPA/MCIM was employed to on-site extract trace FQs in a variety of aqueous samples and followed by quantification with HPLC equipped with fluorescence detector. The achieved limits of detection, precision and spiked recoveries were in the ranges of 0.22–0.78 ng/L, 1.4–9.9 % and 80.0–118 %, respectively. Furthermore, confirmation experiments with reported approach were utilized to evaluate method accuracy. In comparison with documented approaches, the current study exhibits some features such as high sensitivity, convenience, low cost and environmental friendliness. At the same time, the established method overcomes the dilemmas encountered in traditional lab-based sample preparation procedure.

Methodology and accuracy evaluation of global calibration for Multi-Line-Scan camera system in rail transit tunnel

Vision-based inspection techniques have improved the efficiency of tunnel inspection. This paper presents a rail transit tunnel multi-line-scan camera inspection system that requires global calibration to obtain the spatial relationships among all line-scan cameras, which is crucial for tunnel panoramic image mosaicking and 3D reconstruction. A flexible global calibration method is proposed, a hybrid calibration target is designed that includes triangular patterns based on the cross-ratio(CR) invariance principle and target holes. Establishing a virtual target field and using coplanarity as a constraint to refit a new view plane and globally optimizing the initial coordinates, which would ensure the calibration accuracy. In addition, an accuracy evaluation method based on CR triangles is proposed to assess the calibration results. The experimental results demonstrate the feasibility and reliability of the proposed calibration method and that the developed accuracy evaluation method is feasible compared with the traditional reprojection error method.

Collaborative optimization of reference powers and droop coefficients in DC distribution networks considering flexibility enhancement

In droop-controlled DC distribution networks (DCDNs), the uncertainties of loads and renewable energy powers worsen the system performances. This paper proposes a collaborative optimization method of reference powers and droop coefficients to optimize more objectives that including reducing line losses, improving the voltage level, and enhancing the flexibility of DCDNs. Firstly, a fast and accurate flexibility margin evaluation method is presented for DCDNs, in which the allowable power variation intervals under operational constraints are used to quantify the flexibility margins. The analytic relations of allowable power variation intervals and operational constraints are established by the linear power flow model, and the flexibility margins can be calculated quickly and directly. Secondly, the bi-level collaborative optimization method of reference powers and droop coefficients is proposed to optimize more objectives simultaneously. The reference powers are optimized to improve the voltage level and reduce line power losses in the upper-level optimization, and the droop coefficients are optimized to enhance the system flexibility in the lower-level optimization. Finally, multiple test systems with different topologies are simulated in detail. The case study demonstrates that: 1) The flexibility evaluation method can quantify the margins of operational constraints quickly and accurately. Compared with the bisection method, the calculation time of proposed flexibility evaluation method reduce 99.52% with the error of 1.68%. 2) The proposed collaborative optimization of reference powers and droop coefficients can effectively reduce line losses and the risks of voltage off-limit and VSC overload of DCDN under uncertainties.

Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines

Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.

A high-accuracy system model and accuracy evaluation method for transfer alignment

With the increasing demand for high-accuracy navigation, distributed payloads put forward higher requirements for the initial alignment accuracy of their equipped slave inertial navigation systems (INSs). However, the existing transfer alignment models have poor compensation effect on flexible deformation, resulting in low alignment accuracy. Moreover, these models cannot accurately evaluate the accuracy of transfer alignment due to ignoring the errors of the master INS. To address these two problems, a new high-accuracy transfer alignment system model is established by comprehensively considering errors of the master and slave INSs. Further, the propagation characteristics of the master and slave inertial sensor errors in transfer alignment are analyzed, and the relationship expression between the master and slave inertial sensor accuracy and the transfer alignment accuracy is derived. Thus, the accuracy of transfer alignment can be pre-evaluated according to the master and slave inertial sensor accuracy. The experiment results show that the alignment accuracy of the proposed method is improved by more than 71.56% compared with the traditional methods, and the relative errors between the proposed accuracy evaluation method and the Monte Carlo simulations are less than 9.14%. Thus, the correctness, effectiveness and superiority of the proposed method are verified.

Comparison and Evaluation of Five Global Land Cover Products on the Tibetan Plateau

The Tibetan Plateau (TP) region contains maximal alpine grassland ecology at the mid-latitudes. This region is also recognized as an ecologically fragile and sensitive area under the effects of global warming. Regional climate modeling and ecosystem research depend on accurate land cover (LC) information. In order to obtain accurate LC information over the TP, the reliability and precision of five moderate/high-resolution LC products (MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 in 2020) were analyzed and evaluated in this study. The different LC products were compared with each other in terms of areal/spatial consistency and assessed with four reference sample datasets (Geo-Wiki, GLCVSS, GOFC-GOLD, and USGS) using the confusion matrix method for accuracy evaluation over the TP. Based on the paired comparison of these five LC datasets, all five LC products show that grass is the major land cover type on the TP, but the range of grass coverage identified by the different products varies noticeably, from 43.35% to 65.49%. The fully consistent spatial regions account for 43.72% of the entire region of the TP, while, in the transition area between grass and bare soil, there is still a large area of medium-to-low consistency. In addition, a comparison of LC datasets using integrated reference datasets shows that the overall accuracies of MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 are 54.29%, 49.32%, 53.03%, 53.73%, and 60.11%, respectively. The producer accuracy of the five products is highest for grass, while glaciers have the most reliable and accurate characteristics among all LC products for users. These findings provide valuable insights for the selection of rational and appropriate LC datasets for studying land-atmosphere interactions and promoting ecological preservation in the TP.

Spatial Trueness Evaluation of 3D-Printed Dental Model Made of Photopolymer Resin: Use of Special Structurized Dental Model.

(1) Background: Various 3D printers are available for dental practice; however, a comprehensive accuracy evaluation method to effectively guide practitioners is lacking. This in vitro study aimed to propose an optimized method to evaluate the spatial trueness of a 3D-printed dental model made of photopolymer resin based on a special structurized dental model, and provide the preliminary evaluation results of six 3D printers. (2) Methods: A structurized dental model comprising several geometrical configurations was designed based on dental crown and arch measurement data reported in previous studies. Ninety-six feature sizes can be directly measured on this original model with minimized manual measurement errors. Six types of photo-curing 3D printers, including Objet30 Pro using the Polyjet technique, Projet 3510 HD Plus using the Multijet technique, Perfactory DDP and DLP 800d using the DLP technique, Form2 and Form3 using the SLA technique, and each printer's respective 3D-printable dental model materials, were used to fabricate one set of physical models each. Regarding the feature sizes of the simulated dental crowns and dental arches, linear measurements were recorded. The scanned digital models were compared with the design data, and 3D form errors (including overall 3D deviation; flatness, parallelism, and perpendicularity errors) were measured. (3) Results: The lowest overall 3D deviation, flatness, parallelism, and perpendicularity errors were noted for the models printed using the Objet30 Pro (overall value: 45 μm), Form3 (0.061 ± 0.019 mm), Objet30 Pro (0.138 ± 0.068°), and Projet 3510 HD Plus (0.095 ± 0.070°), respectively. In color difference maps, different deformation patterns were observed in the printed models. The feature size proved most accurate for the Objet30 Pro fabricated models (occlusal plane error: 0.02 ± 0.36%, occlusogingival direction error: -0.06 ± 0.09%). (4) Conclusions: The authors investigated a novel evaluation approach for the spatial trueness of a 3D-printed dental model made of photopolymer resin based on a structurized dental model. This method can objectively and comprehensively evaluate the spatial trueness of 3D-printed dental models and has a good repeatability and generalizability.

Criteria for surgical decision-making in talocalcaneal tarsal coalitions

Assessment and management of talocalcaneal middle facet tarsal coalitions present a complex orthopedic challenge. Various studies have attempted to establish guidelines for surgical intervention based on coalition size and hindfoot alignment. Our paper reviews the historical focus on coalition size, suggesting instead that posterior facet health is a more appropriate consideration for surgical decision making. Additionally, we challenge the traditional method for evaluating hindfoot alignment and propose a more accurate evaluation method. This critical review intends to highlight flaws in historical literature, aiming to trigger a reevaluation of the criteria for surgical intervention in talocalcaneal tarsal coalitions and promote future research studies driven by data and scientifically validated methodologies. Level of evidenceV

Analysis of an efficient liquified natural gas utilization cascade Brayton cycle and an improved cold energy recovery evaluation criterion

The cold energy utilization of liquified natural gas is a promising solution for electricity generation systems to raise power output. In order to enhance cold energy recovery performance, here we propose a cascade Brayton cycle, which can efficiently utilize the waste heat from gas turbine and the cold energy of liquefied natural gas. The temperature pinch in natural gas evaporator is successfully reduced by adopting helium as the working fluid. The optimal working condition is determined through parametric sensitivity analysis, and the exergy and economic performance is assessed and compared. The design case outperforming high energy efficiency (68.61 %), competitive exergy efficiency (58.51 %) and excellent levelized cost of electricity (0.0481$·(kW·h)−1). Moreover, the exergy destruction in natural gas evaporator reduced by 4.05 MW, resulting in 23.2 % improvement compared to conventional systems. Simultaneously, a modified criterion of “real specific work contributed per kg/s liquified natural gas” is proposed, which provides a more accurate cold energy recovery evaluation method for cold energy utilization power systems. The comparison of reported cold energy utilization systems are conducted based on the modified criterion, and our model delivers the best result among them.

Research on Condition Assessment Method of Transformer Based on Cloud Model

The traditional evaluation method of transformer has the problems of strong subjectivity of threshold value and fuzzy correlation of state quantity, which is difficult to accurately evaluate the operation quality of transformer. In order to solve these problems, this paper proposes a comprehensive evaluation method of transformer performance evaluation and operation quality based on cloud model. The evaluation index system of power transformer operation quality considering performance is constructed, including target layer, performance layer and index layer. The triangular fuzzy number is used to optimize the set of expert opinions, and the indexes are combined and weighted by fuzzy analytic hierarchy process and entropy weight method. After the data is degraded, the membership matrix of transformer operation index is constructed by using the cloud model, and the performance state weighting and membership degree are comprehensively considered to realize the performance evaluation of transformer and the comprehensive evaluation of operation quality based on performance. By using this method to predict 10 transformers in operation for a period of one month, it is possible to accurately identify abnormal situations on site, with a prediction accuracy of 96.28%. Compared with the traditional method of “predicting first and then diagnosing”, it has improved by 14.32%, overcoming the shortcomings of subjectivity, ambiguity, and ignoring the correlation of state variables in traditional evaluation methods. This method provides new ideas and methods for accurate evaluation of transformers.

Accurate evaluation method for HRS retention of VCM ReRAM

Long-term retention is one of the major challenges concerning the reliability of redox-based resistive switching random access memories based on the valence change mechanism (VCM). The stability of the programmed state has to be ensured over several years, leaving a sufficient read window between the states, which is even more challenging at large statistics. Thus, the underlying physical mechanisms have to be understood and experimental data have to be evaluated accurately. Here, it shows that the retention behavior of the high resistive state (HRS) is more complex than that of the low resistive state and requires a different evaluation method. In this work, we experimentally investigate the retention behavior of 5M VCM devices via accelerated life testing and show the difficulties of commonly used evaluation methods in view of the HRS. Subsequently, we present a new evaluation method focusing on the standard deviation of the HRS current distribution. Hereby, an activation energy for the degradation process can be extracted, which is essential for the prediction of the devices’ behavior under operating conditions. Furthermore, we reproduce the experimentally observed behavior with our 3D Kinetic Monte Carlo simulation model. We confirm the plausibility of our evaluation method and are able to connect the calculated activation energy to the migration barriers of oxygen vacancies that we implemented in the model and that we believe play a key role in the experimentally observed degradation process.