Fast Evaluation Method Research Articles

Development and Application of a Quantitative Model for Proximate and Ultimate Analysis of Flue-Cured Tobacco Based on Near-Infrared Spectroscopy.

A methodology for predicting proximate and ultimate analysis data was developed by using near-infrared spectroscopy (NIR) combined with chemometric methods. The quantitative model has high accuracy, as evidenced by low root-mean-square-error of prediction (RMSEP) values (e.g., 0.41% for volatile matter and 0.29% for carbon). The model was further applied to tobaccos with distinct aroma profiles, and the predicted ultimate and proximate data lead to aroma classification with 86.6% accuracy. This methodology can be expanded to the aroma discrimination of imported tobaccos from Brazil, the United States, Canada, and Zimbabwe, demonstrating its broad reliability. Compared with traditional analyses, this NIR-based approach offers a fast and accurate method for large-scale tobacco evaluation, highlighting its potential for enhancing tobacco quality characterization through a quantifiable, digital, and high-throughput process.

The Ability of Physical Education Teachers in Implementing Learning at Elementary Schools in Southwest Aceh Regency

Physical education teachers are responsible for preparing quality Learning Implementation Plans and implementing effective learning processes so that learning objectives are achieved. A good Learning Implementation Plan will make it easier for teachers to implement structured and directed learning. Physical education teachers face challenges in implementing effective learning processes. The physical education learning process must actively involve students and train their motor skills. However, in practice, physical education teachers often face difficulties in managing classes and making students actively engaged in learning. The approach used by the author in this study was a mixed-method approach (combined method) with an explanatory sequential design type. The research sample comprised nine physical education teachers working in elementary schools throughout Aceh Barat Daya Regency. The data collection techniques used by the researcher were observation and interview techniques. 1) Physical education teachers' ability to implement learning in elementary schools in Aceh Barat Daya Regency obtained the "Good" category with an average score of 6.5. Teachers can display personality aspects that include indicators of being charming, authoritative, firm, full of calling, and alert. In learning activities, teachers can carry out activities to open lessons, master materials, determine learning steps (activities), evaluate the process and learning outcomes, and close learning well. 2) The obstacles felt by physical education teachers in implementing the learning process at Elementary Schools in Southwest Aceh Regency are related to the limited availability of physical education learning facilities and infrastructure at schools. This has an impact on the less-than-optimal implementation of learning and evaluation activities. In addition, the limited time in learning activities, especially to evaluate the learning process of each student as a whole, so that teachers choose a fast and simple evaluation method. 1) The ability of physical education teachers to implement learning in Elementary Schools in Aceh Barat Daya Regency obtained the "Good" category. Teachers can display personality aspects and implement learning well. 2) The obstacles felt by physical education teachers in implementing the learning process at SD Negeri Aceh Barat Daya Regency are related to the limited availability of physical education learning facilities and infrastructure at the school and limited time in learning activities, especially to evaluate the learning process of each student as a whole.

Advances in Spectroscopic Methods for Predicting Cheddar Cheese Maturity: A Review of FT-IR, NIR, and NMR Techniques

The quest for reliable techniques to predict Cheddar cheese maturity has gained momentum to ensure quality and consistency in large-scale production. Given the complexity of cheese ripening and the industry’s need for fast and reliable evaluation methods, this review addresses the challenge by scrutinising the application of spectroscopic techniques such as Fourier transform infrared (FT-IR), near-infrared (NIR), and nuclear magnetic resonance (NMR). These methods are evaluated for their noninvasive and rapid on-site analysis capabilities, which are essential for ensuring quality in cheese production. This review synthesises current research findings, discusses the potential and limitations of each technique, and highlights future research directions. Overall, NIR spectroscopy emerges as the most promising, offering quick, nondestructive assessments and reasonably accurate compositional predictions, crucial for real-time maturation monitoring. It provides rapid results within minutes, making it significantly faster than FT-IR and NMR. While FT-IR also offers high accuracy, it typically requires longer analysis times due to extensive calibration and can be sensitive to sample conditions, while NMR, although highly accurate, involves complex and time-consuming procedures. Nonetheless, further studies are necessary to refine these spectroscopic techniques, enhance their predictive accuracy, and deepen the understanding of the correlations between chemical attributes and sensory qualities in Cheddar cheese.

A fast evaluation method for surface area, volume fraction, and hydraulic diameter of TPMS with different geometric characteristics

A fast evaluation method for surface area, volume fraction, and hydraulic diameter of TPMS with different geometric characteristics

Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks

Abstract argumentation has become one of the important fields of artificial intelligence. This paper proposes a dual-neighborhood tabu search (DNTS) method specifically designed to find a single stable extension in abstract argumentation frameworks. The proposed algorithm implements an improved dual-neighborhood strategy incorporating a fast neighborhood evaluation method. In addition, by introducing techniques such as tabu and perturbation, this algorithm is able to jump out of the local optimum, which significantly improves the performance of the algorithm. In order to evaluate the effectiveness of the method, the performance of the algorithm on more than 300 randomly generated benchmark datasets was studied and compared with the algorithm in the literature. In the experiment, DNTS outperforms the other method regarding time consumption in more than 50 instances and surpasses the other meta-heuristic method in the number of solved cases. Further analysis shows that the initialization method, the tabu strategy, and the perturbation technique help guarantee the efficiency of the proposed DNTS.

Study of blade type effect on the performance of drag-based in-pipe hydro-turbines for improving energy harvesting

Extracting power from the excess pressure of urban water pipelines by in-pipe drag-based turbines is a newly developed method for clean energy production on a small scale. The performance improvement of these turbines as a sustainable and clean energy production device is subjected in this research by studying the effect of turbine blade geometry. In this regard, the focus of this research is on studying the performance of three common blade profiles for free flow drag-based turbines, including the S-Shaped, Semi-Elliptic, and Bach profiles. Firstly, using an existing test ring, the fabricated turbines with different blade profiles are experimentally tested. Numerical study is also performed and simulation accuracy is validated with the experimental results. After that a theoretical method is introduced for evaluation of the produced torque by turbines with different blade profiles. The outcome of these three methods unanimously proved that in case of in-pipe turbines, the turbine with Bach profile blades has better performance than the others. Also, the developed theoretical approach proved to be a fast and reliable method for performance evaluation of in-pipe turbines. Further analysis, which is performed by numerical simulations is used to show the flow field behavior around turbine in more detail. This is to distinguish the reasons for existing difference among the performance of the studied turbines.

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

A Fast Method for the Off-Boundary Evaluation of Laplace Layer Potentials by Convolution Sums

In off-boundary computations of layer potentials, the near-singularities in integrals near the boundary presents challenges for conventional quadrature methods in achieving high precision. Additionally, the significant complexity of O(n2) interactions between n targets and n sources reduces the efficiency of these methods. A fast and accurate numerical algorithm is presented for computing the Laplace layer potentials on a circle with a boundary described by a polar curve. This method can maintain high precision even when evaluating targets located at a close distance from the boundary. The radial symmetry of the integral kernels simplifies their description. By exploiting the polar form of the boundary and applying a one-dimensional exponential sum approximation along the radial direction, an approximation of layer potentials by the convolution sum is obtained. The algorithm uses FFT convolution to accelerate computation and employs a local quadrature to maintain accuracy for nearly singular terms. Consequently, it achieves spectral accuracy in regions outside of a sufficiently small neighborhood of the boundary and requires O(nlogn) arithmetic operations. With the help of this algorithm, layer potentials can be efficiently evaluated on a computational domain.

Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources

As advancements in transportation equipment intelligence continue, the job shop scheduling problem integrating finite transportation resources (JSPIFTR) has attracted considerable attention. Within the domain of shop scheduling, the neighborhood structure serves as a cornerstone for enabling intelligent optimization algorithms to effectively navigate and discover optimal solutions. However, current algorithms for JSPIFTR rely on generalized neighborhood structures, which incorporate operators like insertion and swap. While these structures are tailored to the encoding vectors, their utilization often leads to suboptimal optimization efficacy. To address this limitation, this paper introduces novel neighborhood structures specifically designed to the distinctive properties of JSPIFTR. These innovative structures leverage the intrinsic structural information in integrated scheduling, thereby enhancing the optimization effectiveness of the algorithm. Firstly, two theorems are presented to demonstrate the feasibility of the neighborhood solution. Secondly, different neighborhood structures for critical transportation and processing tasks are subsequently designed based on the analysis of the problem properties and constraints. Thirdly, an efficient fast evaluation method is developed to expediently calculate the objective value of the neighborhood solution. Finally, the novel neighborhood structures are combined with the tabu search (TS_NNS) and compared with other state-of-the-art methods on EX and NEX benchmarks. The comparative results demonstrate the remarkable performance of the neighborhood structure, with the TS_NNS enhancing the best solutions across 23 instances.

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 fast and efficient method for screening and evaluation of hypoglycemic ingredients of Traditional Chinese Medicine acting on PTP1B by capillary electrophoresis

As a pivotal enzyme that regulates dephosphorylation in cell activities and participates in the insulin signaling pathway, protein tyrosine phosphatase 1B (PTP1B) is considered to be an important target for the therapy of diabetes. In this work, a rapid and efficient inhibitor screening method of PTP1B was established based on capillary electrophoresis (CE), and used for screening and evaluating the inhibition effect of Traditional Chinese Medicine on PTP1B. Response Surface Methodology was used for optimizing the conditions of analysis. After method validation, the enzyme kinetic study and inhibition test were performed. As a result, the IC50 of PTP1B inhibitors Ⅳ and ⅩⅧ were consistent with reported values measured by a conventional method. It was found that the extracts of Astragalus membranaceus (Fisch) Bunge and Morus alba L. showed prominent inhibition on the activity of PTP1B, which were stronger than the positive controls. Meanwhile, on top of the excellent advantages of CE, the whole analysis time is less than 2 min. Thus, the results demonstrated that a fast and efficient screening method was successfully developed. This method could be a powerful tool for screening inhibitors from complex systems. It can also provide an effective basis for lead compound development in drug discovery.

Honey differentiation with FTIR-ATR spectroscopy – Comparison with physicochemical parameters of a Polish honey sample set

The varietal discrimination of honey is important because its bioactive properties, as well as specific characteristics, depend on the botanical origin. The present study aimed to verify the discriminatory power of Fourier-transform infrared spectroscopy (FT-IR) for differentiating Polish honeys and compare the results with the discrimination power of a model built on the basis of selected physicochemical parameters. For this purpose, buckwheat, rape, acacia, heather, goldenrod, and coniferous honeydew honeys were collected from trustworthy apiaries in different regions of Poland. The samples were further authenticated using pollen analysis and tested by means of FT-IR and the attenuated total reflection (ATR) technique. Basic psychochemical parameters were also assessed. The obtained data were analyzed by means of principal component analysis (PCA) and linear discriminant analysis (LDA). The obtained models were validated with a leave-one-out cross-validation (LOOCV) procedure. PCA analysis confirmed the usefulness of the ATR FT-IR method as a honey differentiation tool. The LDA for the FT-IR model allowed to correctly classify 26 out of 32 samples (81.3% accuracy), which was only slightly lower than for data the model based on the physiochemical characteristics (27 out of 32 samples and 84.4% accuracy), revealing a certain potential of FT-IR as fast evaluation method.

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics.

There are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time-consuming analysis methods. Near-infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium. Quantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K-nearest neighbor (KNN), back-propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high-performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints. Qualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory. The method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.

Fast wavefield evaluation method based on modified proxy-surface-accelerated interpolative decomposition for two-dimensional scattering problems

Fast wavefield evaluation method based on modified proxy-surface-accelerated interpolative decomposition for two-dimensional scattering problems

Passive TDOA Emitter Localization Using Fast Hyperbolic Hough Transform

A fast Hough transform (HT)-based hyperbolic emitter localization system is proposed to process time difference of arrival (TDOA) measurements. The position-fixing problem is provided for cases where the source is known to be on a given plane (i.e., the elevation of the source is known), while the sensors can be deployed anywhere in the three-dimensional space. The proposed solution provides fast evaluation and guarantees the determination of the global optimum. Another favorable property of the proposed solution is that it is robust against faulty sensor measurements (outliers). A fast evaluation method involving the hyperbolic Hough transform is proposed, and the global convergence property of the algorithm is proven. The performance of the algorithm is compared to that of the least-squares solution, other HT-based solutions, and the theoretical limit (the Cramér–Rao lower bound), using simulations and real measurement examples.

Evaluation of stochastic flow lines with provisioning of auxiliary material

Flow lines are often used to perform assembly operations in multi-stage processes. During these assembly operations, components that are relatively small, compared to the work pieces travelling down the flow line, are mounted to the work pieces at a given stage. Those components, or more generally, any kind of auxiliary material, are provisioned to the corresponding production stage in a repetitive but not necessarily deterministic manner using a certain delivery frequency, each time filling the local storage up to a predetermined order-up-to level. Just like random processing times, machine failures, and repairs, the randomness of the provisioning process can impact the long-term throughput of such a flow line. In this paper, we develop a fast and accurate analytical performance evaluation method to estimate the long-term throughput of a Markovian flow line of this type for the practically important case of limited buffer capacities between the production stages. We first give an exact characterization of a two-machine line of that type and show how to determine system state probabilities and aggregate performance measures. Furthermore, we show how to use this two-machine model as the building block of an approximate decomposition approach for longer flow lines. As opposed to previous decomposition approaches, even the state space of the two-machine lines can become so large that an exact solution of the Markov chains can become impractical. We hence show how to set up, train, and use an artificial neural network to replace the Markov chain solver embedded in the decomposition approach, which then leads to an accurate and extremely fast flow line evaluation tool. The proposed methodology is evaluated by a comparison with simulation results and used to characterize the structural patterns describing the behaviour of flow lines of this type. The method can be used to systematically consider the combined impact of the delivery frequency and the local order-up-to levels for the auxiliary material when designing a flow line of this type.

Evolution of chlorinated paraffin and olefin fingerprints in sewage sludge from 1993 to 2020 of a Swiss municipal wastewater treatment plant

Exposure of humans to chlorinated paraffins (CPs) and chlorinated olefins (COs) can occur via contact with CP-containing plastic materials. Such plastic materials can contain short-chain CPs (SCCPs), which are regulated as persistent organic pollutants (POPs) under the Stockholm Convention since 2017. Municipal wastewater treatment plants (WWTP) collect effluents of thousands of households and their sludge is a marker for CP exposure. We investigated digested sewage sludge collected in the years 1993, 2002, 2007, 2012, and 2020 from a Swiss WWTP serving between 20000 and 23000 inhabitants. A liquid chromatography mass spectrometry (R > 100000) method, in combination with an atmospheric pressure chemical ionization source (LC-APCI-MS), was used to detect mass spectra of CPs and olefinic side products. A R-based automated spectra evaluation routine (RASER) was applied to search for ∼23000 ions whereof ∼6000 ions could be assigned to CPs, chlorinated mono- (COs), di- (CdiOs) and tri-olefins (CtriOs). Up to 230 CP-, 120 CO-, 50 CdiO- and 20 CtriO-homologues could be identified in sludge. Characteristic fingerprints were deduced describing C- and Cl-homologue distributions, chlorine- (nCl) and carbon- (nC) numbers of CPs and COs. In addition, proportions of saturated and unsaturated material were determined together with proportions of different chain length classes including short- (SC), medium- (MC), long- (LC) and very long-chain (vLC) material. A substantial reduction of SCCPs of 84% was observed from 1993 to 2020. Respective levels of MCCPs, LCCPs and vLCCPs decreased by 61, 69 and 58%. These trends confirm that banned SCCPs and non-regulated CPs are present in WWTP sludge and higher-chlorinated SCCPs were replaced by lower chlorinated MCCPs. Combining high-resolution mass spectrometry with a selective and fast data evaluation method can produce characteristic fingerprints of sewage sludge describing the long-term trends in a WWTP catchment area.

XAutn: Blockchain-based cross domain authentication for digital certificates in the education sector

Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy.

Dual-Neighborhood Search for Solving the Minimum Dominating Tree Problem

The minimum dominating tree (MDT) problem consists of finding a minimum weight subgraph from an undirected graph, such that each vertex not in this subgraph is adjacent to at least one of the vertices in it, and the subgraph is connected without any ring structures. This paper presents a dual-neighborhood search (DNS) algorithm for solving the MDT problem, which integrates several distinguishing features, such as two neighborhoods collaboratively working for optimizing the objective function, a fast neighborhood evaluation method to boost the searching effectiveness, and several diversification techniques to help the searching process jump out of the local optimum trap thus obtaining better solutions. DNS improves the previous best-known results for four public benchmark instances while providing competitive results for the remaining ones. Several ingredients of DNS are investigated to demonstrate the importance of the proposed ideas and techniques.

Fast evaluation method of post-impact performance of bridges based on dynamic load test data using Gaussian process regression

Bridges occasionally suffer from the vehicle or ship collision accidents, leading to structural damage and bridge collapse, resulting in severe consequences such as casualties, ship sinking, and vehicle damage. After such accidents, the performance evaluation of bridge structures is significant for bridge maintenance. The bridge's structural performance should be assessed after a collision with a vehicle or ship before regular traffic is resumed. A gray correlation analysis technique was introduced for the swift and efficient assessment of bridge structural performance following impacts. This method aimed to identify the influential parameters associated with bridge structural performance. Utilizing outcomes from dynamic load tests along with the Gaussian process regression model, adjustments were made to the original finite element analysis model. This refinement facilitated precise scrutiny of structural damage and expedited accurate performance evaluations of the bridge. Subsequently, a practical examination was carried out following a ship collision with the Wanjiang Bridge to validate the viability and precision of the proposed approach. A comparison between performance evaluation outcomes derived from the bridge's structural response to ship collision and actual field test results demonstrated the substantial accuracy and computational efficacy of the suggested technique. The proposed method uses a dynamic load test combined with an intelligent algorithm to replace the static load test, effectively solving the expensive, time-consuming, traffic-impeding static load test problem.