Scaling Method Research Articles

Modeling of collision-induced excitation and quenching of atomic nitrogen

Excited atomic nitrogen atoms play an important role in plasma formation in hypersonic shock-waves, as happens during spacecraft reentry and other high velocity vehicle applications. In this study, we have thoroughly studied collision induced excitation associated with two colliding nitrogen atoms in the N(4S), N(2D), and N(2P) states at collision energies up to 6 eV, using time-independent scattering calculations to determine cross sections and temperature-dependent rate coefficients. The calculations are based on potential curves and couplings determined in earlier multireference configuration interaction calculations with large basis sets, and the results are in good agreement with experiments where comparisons are possible. To properly consider the spin–orbit coupling matrix, we have developed a scaling method for treating transitions between different fine-structure components that only require calculations with two coupled states, and with this, we define accurate degeneracy factors for determining cross sections and rate coefficients that include all states. The results indicate that both spin–orbit and derivative coupling effects can play important roles in collisional excitation and quenching, and that although derivative coupling is always much stronger than spin–orbit, there are many transitions where only spin–orbit can contribute. As part of this, we identify two distinct pathways associated with N(2P) relaxation and one Auger-like mechanism leading to two N(2D) that could be important at high temperatures.

Critical behavior of quasi-two-dimensional ferromagnet Cr1.04Te2

Abstract The self-intercalation of Cr into pristine two-dimensional (2D) van der Waals ferromagnetic CrTe2, which forms chromium tellurides (Cr x Te2), has garnered interest due to their remarkable magnetic characteristics and the wide variety of chemical compositions available. Here, comprehensive basic characterization and magnetic studies are conducted on quasi-2D ferromagnetic Cr1.04Te2 crystals. Measurements of the isothermal magnetization curves are conducted around the critical temperature to systematically investigate the critical behavior. Specifically, the critical exponents β = 0.2399, γ = 0.859, and δ = 4.3498, as well as the Curie temperature T C = 249.56 K, are determined using various methods, including the modified Arrott plots, the Kouvel–Fisher method, the Widom scaling method, and the critical isotherm analysis. These results indicate that the tricritical mean-field model accurately represents the critical behavior of Cr1.04Te2. A magnetic phase diagram with tricritical phenomenon is thus constructed. Further investigations confirm that the critical exponents obtained conform to the scalar equation near T C, indicating their self-consistency and reliability. Our work sheds light on the magnetic properties of quasi-2D Cr1.04Te2, broadening the scope of the van der Waals crystals for developments of future spintronic devices operable at room temperature.

Gamer and Non-gamer Students in Listening Skills: A Comparative Study of Third Semester Students in Academic Year 2023/2024

This study aims to reveal if there are significant differences in extensive listening skills between gamer and non-gamer of 3rd-semester students at the University of Mataram. The subject of the study involves the 3rd-semester students in the English department of Mataram University which consists of 10 students in total, by using a questionnaire to divide between gamer and non-gamer students, the researcher found that 5 of them are gamers and 5 others are non-gamers. After dividing the students into two groups, they were given TOEIC listening tests to check their listening scores, and the data collected were checked using the independent sample t-test scaling method. From the result of the analysis, it was found that the critical t-value is 2.306 which is smaller than the t-value which is 3.952. Therefore the null hypothesis is rejected which means there are significant differences between gamer and non-gamer of 3rd-semester students at the University of Mataram.

STATUS OF SUSTAINABILITY OF ORGANIC RICE COMMODITIES AS RURAL ECONOMIC POTENTIAL IN BONDOWOSO REGENCY

The complexity of organic farming system problems starts from the increasingly scarce availability of land resources, farmers' business capital which is still very limited and marketing access and the organic rice market chain, population growth continues to increase every year and the low quality of community resources, and knowledge in the application of technology and quality standardization will impact on the sustainability of organic rice commodities. The aim of this research is to analyze the sustainability status of organic rice commodities in terms of ecological, economic and social dimensions. The analytical method used in this research is Rapfish which is modified into RAP-Organic rice using the Multidimensional scaling method. The results of research on the multi-dimensional sustainability status of organic rice commodities including ecological, economic, social and infrastructure & technology dimensions show that organic rice commodities have a fairly sustainable index value with an index value of 73.58, meaning that with this index value all dimensions include: ecological dimension, economic, social and infrastructure & technology influence the sustainability status of organic rice commodities. The implication of the analysis results is that the Regional Government needs to increase attention to organic rice farmer groups regarding the importance of organic rice businesses by making special policies for the development of organic rice businesses to continue to increase the sustainability of organic rice commodities.

Connections of the Yamna Culture Population of the Black Sea Region According to Anthropological Data: An Intergroup Analysis of Male Series

The objective of this paper is to examine the population history of the North Pontic Yamna groups through an analysis of phenotypic variation in cranial morphology. To investigate this issue, an intergroup analysis of male craniological samples from Eastern, Central, South-Eastern Europe, Caucasus and Southern Siberia was carried out using the multidimensional scaling method. The analysis suggests that migrations were a significant factor in the formation of the Yamna culture phenomenon in the vast steppe areas of Eurasia. This can explain the anthropological continuity of Yamna-related groups and the central position of the Volga steppe region within it. While there is a variability in anthropological characteristics, most Yamna-related series share a number of common features. The morphological variability observed in Yamna populations can be attributed to the extensive geographical distribution of the Yamna horizon and its interaction with local and neighbouring tribes in various regions. The Yamna horizon likely originated from steppe Eneolithic groups, particularly those with morphological similarities to the Repin and Serednii Stih populations. The Maikop-Novosvobodnaia groups may have also played a role in the formation of the Yamna horizon. The origin of the broad-faced dolichomesocranial craniocomplex, found in Yamna material from the North Pontic region, can be linked to the eastern Yamna groups. A different leptene hyperdolichocranial craniocomplex can be associated with the later groups of the Ural and Cis-Caucasian regions, such as the Tamar-Utkul group and possibly the Novotitorovka culture. However, it can also be assumed that the North Pontic Yamna population was influenced by Central European groups (Funnelbeaker, Globular Amphora, Corded Ware cultures) as well as the Kemi-Oba population, whose origin can also be linked to the Central European cultural circle.

Experiment-guided tuning of muscle–tendon parameters to estimate muscle fiber lengths and passive forces

The workflow to simulate motion with recorded data usually starts with selecting a generic musculoskeletal model and scaling it to represent subject-specific characteristics. Simulating muscle dynamics with muscle–tendon parameters computed from existing scaling methods in literature, however, yields some inconsistencies compared to measurable outcomes. For instance, simulating fiber lengths and muscle excitations during walking with linearly scaled parameters does not resemble established patterns in the literature. This study presents a tool that leverages reported in vivo experimental observations to tune muscle–tendon parameters and evaluates their influence in estimating muscle excitations and metabolic costs during walking. From a scaled generic musculoskeletal model, we tuned optimal fiber length, tendon slack length, and tendon stiffness to match reported fiber lengths from ultrasound imaging and muscle passive force–length relationships to match reported in vivo joint moment–angle relationships. With tuned parameters, muscle contracted more isometrically, and soleus’s operating range was better estimated than with linearly scaled parameters. Also, with tuned parameters, on/off timing of nearly all muscles’ excitations in the model agreed with reported electromyographic signals, and metabolic rate trajectories varied significantly throughout the gait cycle compared to linearly scaled parameters. Our tool, freely available online, can customize muscle–tendon parameters easily and be adapted to incorporate more experimental data.

High school rugby coaches’ knowledge and opinions of concussion in KwaZulu Natal province in South Africa: an ecological cross-sectional study

BackgroundConcussions in Rugby Union are common with an increased risk to adolescent players. Coaches are key to injury prevention and a greater understanding of their knowledge and sentiments may guide future initiatives. There is a lack of data on rugby coaches, especially in South Africa. This study aimed to investigate the knowledge and opinions of high school rugby coaches regarding concussion management.MethodsThis cross-sectional study of 37 high school rugby coaches in South Africa, was conducted via a self-reported questionnaire. Concussion knowledge was scored for correct answers only with closed-question scaling methods to measure the importance of items of concussion management using a graphical rating scale. An attitude scale (Likert) was used to assess self-reported opinions and behaviours. Associations were calculated for participant characteristics and overall concussion injury knowledge.ResultsMore participants showed good overall knowledge of ≥ 75% (n = 22, 59% vs. n = 15, 40%), especially those with greater coaching experience (p = 0.021). Player welfare was perceived more important than player performance (185 vs. 164), with concussion prevention most important (184 of 185). Appealing characteristics of an injury prevention programme were the improvement of player skill (173, SD ± 0.75, mean 4.68), being adaptable (171, ± 0.86, 4.62), and being completed in the warm-up (167, ± 0.93, 4.51). The biggest perceived barriers were duration (138, ± 1.59, mean 3.73), effort (130, ± 1.56, 3.51), compliance and lack of knowledge (both 127, ± 1.68, 3.43).ConclusionThese results support the implementation of ongoing concussion education for rugby coaches and identify areas for promoting awareness and knowledge of concussion injury prevention, identification, and specific management of younger athletes. Appealing characteristics and barriers are highlighted and may allow for improved implementation and adherence to concussion prevention programmes.

Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System

ABSTRACT Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss of groundnut plant growth, which will directly diminish the yield and quality. Therefore, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System (GLDI-WDCGAN-AOA) is proposed in this paper. The pre-processed output is fed to Hesitant Fuzzy Linguistic Bi-objective Clustering (HFL-BOC) for segmentation. By using Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN), the input leaf images are classified into Healthy leaf, early leaf spot, late leaf spot, nutrition deficiency, and rust. Finally, the weight parameters of WDCGAN are optimized by Aquila Optimization Algorithm (AOA) to achieve high accuracy. The proposed GLDI-WDCGAN-AOA approach provides 23.51%, 22.01%, and 18.65% higher accuracy and 24.78%, 23.24%, and 28.98% lower error rate analysed with existing methods, such as Real-time automated identification and categorization of groundnut leaf disease utilizing hybrid machine learning methods (GLDI-DNN), Online identification of peanut leaf diseases utilizing the data balancing method along deep transfer learning (GLDI-LWCNN), and deep learning-driven method depending on progressive scaling method for the precise categorization of groundnut leaf infections (GLDI-CNN), respectively.

Nonadiabatic molecular dynamics with subsystem density functional theory: application to crystalline pentacene

In this work, we report the development and assessment of the nonadiabatic molecular dynamics approach with the electronic structure calculations based on the linearly scaling subsystem density functional method. The approach is implemented in an open-source embedded Quantum Espresso/Libra software specially designed for nonadiabatic dynamics simulations in extended systems. As proof of the applicability of this method to large condensed-matter systems, we examine the dynamics of nonradiative relaxation of excess excitation energy in pentacene crystals with the simulation supercells containing more than 600 atoms. We find that increased structural disorder observed in larger supercell models induces larger nonadiabatic couplings of electronic states and accelerates the relaxation dynamics of excited states. We conduct a comparative analysis of several quantum-classical trajectory surface hopping schemes, including two new methods proposed in this work (revised decoherence-induced surface hopping and instantaneous decoherence at frustrated hops). Most of the tested schemes suggest fast energy relaxation occurring with the timescales in the 0.7–2.0 ps range, but they significantly overestimate the ground state recovery rates. Only the modified simplified decay of mixing approach yields a notably slower relaxation timescales of 8–14 ps, with a significantly inhibited ground state recovery.

Prediction of cavity inception speed and underwater radiated noise of a full-scale marine propeller based on a cavitation tunnel model test

Propeller cavitation is a dominant source of underwater radiated noise (URN) from shipping, and the appropriate evaluation of propeller noise mitigation measures is important for ecological ship design. In this study, a model test procedure and scaling method to predict the full-scale URN level from hydrodynamic noise sources were introduced. The cavity size based on the empirical vortex model was considered in the proposed scaling method for a quiet cruise condition with isolated tip vortex cavitation. This relation provides information on the equivalent cavitation number for the model test to have the same relative radius as that of the full-scale tip vortex cavitation. The alternative test cavitation number can also be chosen as close as possible to the point that gives the equivalent tip vortex cavity size if sheet cavitation exists in the condition for the equivalent cavity size. In this case, additional noise scaling should be performed using the ratio of the tip vortex cavity radii. The new scaling exponent introduced in this study varies according to the distance from the cavitation inception condition and shows a decreasing trend from the value at the inception condition as the equivalent cavitation number for the model test decreases. The scaled URN levels based on the proposed method showed reasonable agreement with the measurement results of a full-scale ship. Finally, the noise reduction effect of the specially designed wake control fin and propeller was investigated through a comparative model test following the proposed scaling strategy, and significant noise reductions were observed with changes in the design configuration.

Sequence dependence of critical properties for two-letter chains.

Histogram-reweighting grand canonical Monte Carlo simulations are used to obtain the critical properties of lattice chains composed of solvophilic and solvophobic monomers. The model is a modification of one proposed by Larson et al. [J. Chem. Phys. 83, 2411 (1985)], lowering the "contrast" between beads of different types to prevent aggregation into finite-size micelles that would mask true phase separation between bulk high- and low-density phases. Oligomeric chains of lengths between 5 and 24 beads are studied. Mixed-field finite-size scaling methods are used to obtain the critical properties with typical relative accuracies of better than 10-4 for the critical temperature and 10-3 for the critical volume fraction. Diblock chains are found to have lower critical temperatures and volume fractions relative to the corresponding homopolymers. The addition of solvophilic blocks of increasing length to a fixed-length solvophobic segment results in a decrease of both the critical temperature and the critical volume fraction, with an eventual slow asymptotic approach to the long-chain limiting behavior. Moving a single solvophobic or solvophilic bead along a chain leads to a minimum or maximum in the critical temperature, with no change in the critical volume fraction. Chains of identical length and composition have a significant spread in their critical properties, depending on their precise sequence. The present study has implications for understanding biomolecular phase separation and for developing design rules for synthetic polymers with specific phase separation properties. It also provides data potentially useful for the further development of theoretical models for polymer and surfactant phase behavior.

Effect of Using Numerical Data Scaling on Supervised Machine Learning Performance

Before building machine learning models, the dataset should be prepared to be a high quality dataset, we should give the model the best possible representation of the data. Different attributes may have different scales which possibly will increase the difficulty of the problem that is modeled. A model with varying scale values may suffers from poor performance during learning. Our study explores the usage of Numerical Data Scaling as a data pre-processing step with the purpose of how effectively these methods can be used to improve the accuracy of learning algorithms. In particular, three numerical data Scaling methods with four machine learning classifiers to predict disease severity were compared. The experiments were built on Coronavirus 2 (SARS-CoV-2) datasets which included 1206 patients who were admitted during the period between June 2020 and April 2021. The diagnosis of all cases was confirmed with RT-PCR. Basic demographic data and medical characteristics of all participants was collected. The reported results indicate that all techniques are performing well with Numerical Data Scaling and there are significant improvement in the models for unseen data. lastly, we can conclude that there are increase in the classifier performance while using scaling techniques. However, these methods help the algorithms to better understand learn the patterns in the dataset which help making accurate models

Approximate Computing Based Low-Power FPGA Design for Big Data Analytics in Cloud Environments

As cloud computing continues to evolve, the demand for scalable and energy-efficient infrastructure to handle extensive applications becomes paramount. Traditional transistor scaling and microprocessor design methods no longer suffice to meet the growing scale of cloud usage. This research explores the potential of approximate computing (AC) as an innovative solution to these challenges, particularly in high-demand computational settings. AC, known for its ability to make controlled accuracy trade-offs, is identified as a key strategy for improving both the performance and energy efficiency of cloud infrastructure, with a focus on low-power Field-Programmable Gate Array (FPGA) designs. This paper introduces novel methodologies that harness the strengths of AC, emphasizing its application in neural-based and machine-learning techniques for energy-efficient solutions. By targeting the performance of AC, especially in varied application domains and complex data mining scenarios, we propose two groundbreaking approaches that significantly enhance computational speed and reduce energy consumption. Our empirical analysis demonstrates notable improvements over existing techniques, highlighting the effectiveness of AC in optimizing cloud infrastructure. The proposed model on FPGA through cloud computing attains substantial elevation rates by 89 % and energy reduction by 122%, which had been good outcomes. This study not only confirms the benefits of integrating AC with low-power FPGA designs for cloud environments but also sets a new benchmark for future research in achieving more sustainable and efficient cloud computing via VLSI FGPA design analysis solutions.

Variations in the anatomical structure of the pterion among populations from various global regions

Anatomical variations in the structure of the pterion were examined in various craniological series represent-ing modern populations from Australia, Melanesia, Southeast Asia, North Asia, America, and Europe, encom-passing 2035 adult crania. The comparison of populations based on the frequency of nonmetric traits revealed significant diversity across regions. Notably, the sphenoparietal connection is most frequent in European and South Asian populations, as well as among Native Americans, while it is least common among African groups. The frontotemporal connection predominates among Australian Aborigines and Melanesians and is also common in African populations but is almost non-existent in Europeans. The X-shaped pterion is most frequently observed in the Eskimos of Chukotka and the Chukchi, as well as among Somali and Tanzanian populations. A high fre-quency of epipteric bones is particularly characteristic of Southern Indians, Burmese, Australians, and Melane-sians. Pairwise comparisons of populations were conducted using Smith's Mean Measure of Divergence (MMD). To visualize the data from individual regions, the method of Multidimensional Scaling (MDS) was applied to the matrices of biological distances. The MDS plot for the North Asian region revealed distinct clusters of the Arctic, Baikal, Cen-tral Asian, and Ural groups, aligning with G.F. Debetz's classification based on morphological similarities among populations. In the MDS plot for the South and Southeast Asian, American, and African populations, Australians and Melanesians formed a separate cluster; Pueblo Indians were closely aligned with Peruvians, South Indians with Burmese, Malays merged with Papuans, and African populations were positioned relatively close to each other. In Europe, Caucasian groups formed a distinct cluster. On the combined graph, the populations were clearly divided by regions. The results obtained indicate that the spatial arrangement of populations on the graphs approximately mir-rors their genetic connections and shared origins. This suggests that nonmetric variations in the pterion area are more likely determined by genetic rather than ecological factors and can be considered taxonomically valuable traits at the regional level. Investigating the structure of the pterion in an evolutionary context may shed light on broader questions concerning the evolution of the skull and the mechanisms influencing its formation.

The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction

In the field of data engineering in machine learning (ML), a crucial component is the process of scaling, normalization, and standardization. This process involves transforming data to make it more compatible with modeling techniques. In particular, this transformation is essential to ensure the suitability of the data for subsequent analysis. Despite the application of many conventional and relatively new approaches to ML, there remains a conspicuous lack of research, particularly in the geotechnical discipline. In this study, ML-based prediction models (i.e., RF, SVR, Cubist, and SGB) were developed to estimate the undrained shear strength (UDSS) of cohesive soil from the perspective of a wide range of data-scaling and transformation methods. Therefore, this work presents a novel ML framework based on data engineering approaches and the Cubist regression method to predict the UDSS of cohesive soil. A dataset including six different features and one target variable were used for building prediction models. The performance of ML models was examined considering the impact of the data pre-processing issue. For that purpose, data scaling and transformation methods, namely Range, Z-Score, Log Transformation, Box-Cox, and Yeo-Johnson, were used to generate the models. The results were then systematically compared using different sampling ratios to understand how model performance varies as various data scaling/transformation methods and ML algorithms were combined. It was observed that data transformation or data sampling methods had considerable or limited effects on the UDSS model performance depending on the algorithm type and the sampling ratio. Compared to RF, SVR, and SGB models, Cubist models provided higher performance metrics after applying the data pre-processing steps. The Box-Cox transformed Cubist model yielded the best prediction performance among the other models with an R2 of 0.87 for the 90% training set. Also, the UDSS prediction model generally yielded the best performance metrics when it was used with the transformed-based models (i.e., Box-Cox, Log, and Yeo-Johnson) than that of scaled-based (i.e., Range and Z-Score) models. The results show that the Cubist model has a higher potential for UDSS prediction, and data pre-processing methods have impacts on the predictive capacity of the evaluated regression models.

Increasing magnitude of color differences amplifies category effects

Previous studies have identified differences in sensitivity characteristics between color discrimination and perception of suprathreshold color differences. However, it remains highly unclear how color difference sensitivity changes with increasing magnitudes of color difference along various color hues. This study aimed to quantify the sensitivity transition across various magnitudes of color differences and uncover the underlying mechanisms. Color discrimination sensitivities were measured using an adaptive staircase method for 32 isoluminant pedestal colors in the u'v' chromaticity diagram. For suprathreshold color differences, we employed the Maximum Likelihood Difference Scaling (MLDS) method to measure sensitivity to various color difference levels for the same 32 colors. Our findings confirmed the differences in sensitivity characteristics between discrimination and suprathreshold color difference perception. Furthermore, we observed increased sensitivities at many color category boundaries in suprathreshold color difference perception. By investigating the relation between the category effects and the color difference size levels through a model simulation, our findings suggest that the influence of color categories on the perception of color differences may become more pronounced as the magnitude of color differences increases.

Nonlinear Reduced Order Modelling for Korteweg-de Vries Equation

Efficient computational techniques that maintain the accuracy and invariant preservation property of the Korteweg-de Vries (KdV) equations have been studied by a wide range of researchers. In this paper, we introduce a reduced order model technique utilizing kernel principle component analysis (KPCA), a nonlinear version of the classical principle component analysis, in a non-intrusive way. The KPCA is applied to the data matrix, which is formed by the discrete solution vectors of KdV equation. In order to obtain the discrete solutions, the finite differences are used for spatial discretization, and linearly implicit Kahan's method for the temporal one. The back-mapping from the reduced dimensional space, is handled by a non-iterative formula based on the idea of multidimensional scaling (MDS) method. Through KPCA, we illustrate that the reduced order approximations conserve the invariants, i.e., Hamiltonian, momentum and mass structure of the KdV equation. The accuracy of reduced solutions, conservation of invariants, and computational speed enhancements facilitated by classical (linear) PCA and KPCA are exemplified through one-dimensional KdV equation.

Ab Initio Electron Propagators with an Hermitian, Intermediately Normalized Superoperator Metric Applied to Vertical Electron Affinities.

New-generation ab initio electron propagator methods for calculating electron detachment energies of closed-shell molecules and anions have surpassed their predecessors' accuracy and computational efficiency. Derived from an Hermitian, intermediately normalized superoperator metric, these methods contain no adjustable parameters. To assess their versatility, a standard set (NIST-50-EA) of 50 vertical electron affinities of small closed-shell molecules based on NIST reference data has been created. Errors with respect to reference data on 23 large, conjugated organic photovoltaic (OPV23) molecules have also been analyzed. All final states are valence anions that correspond to electron affinities between 0.2 and 4.2 eV. For a given scaling of the arithmetic bottleneck, the new-generation methods obtain the lowest mean absolute errors (MAEs). The best methods with fifth-power arithmetic scaling realize MAEs below 0.1 eV. Composite models comprising cubically and quintically scaling calculations executed with large and small basis sets, respectively, produce OPV23 MAEs near 0.05 eV. The accuracy of quintically scaling methods executed with large basis sets is thereby procured with reduced computational effort. New-generation results obtained with and without the diagonal self-energy approximation in the canonical Hartree-Fock basis have been compared. These results indicate that Dyson orbitals closely resemble canonical Hartree-Fock orbitals multiplied by the square root of a probability factor above 0.85.

The influence of different forms of black cumin (Nigella sativa L.) on the characteristics of sheep’s curd cheese

The influence of different forms of Nigella sativa (seeds, powder, alcoholic extract, and oil) on the traditional sheep curd was investigated by comparison with a control simple curd considering a series of sensorial, physical–chemical, and microbiological aspects. The analysis was performed on curds freshly prepared and after 10 days of refrigeration. The sensory analysis of the curds was performed using a variety of methods such as scaling method, the method of quality describing, the method of ordering by rank, “triangle” method, and “duo-trio” method. The analyzed curds were assessed and classified according to their sensory characteristics and typicality. The N. sativa supplementing of the sheep curds improved their physical–chemical characteristics by raising the fat content with 0.88–2.82% and decreasing the titratable acidity with 1.42–2.32% compared to the control curd without additive. After 10 days of refrigeration, the titratable acidity increased with 1.58–3.25% and moisture decreased (8.43–13.17%). The microbiological quality of the curd samples was influenced by the addition of black cumin in different forms, with variations in the total number of bacteria (TNB) between 4.33 and 4.39 log CFU/g compared to the control sample 5.03 log CFU/g, Enterobacteriaceae (2.53–2.58 log CFU/g) compared to the control sample 2.60 log CFU/g, and coagulase-positive staphylococci (CPS) 2.30–2.68 log CFU/g compared to the control sample 2.75 log CFU/g. After 10 days of refrigeration, the number of microorganisms decreased, TNB (4.13–4.31 log CFU/g), Enterobacteriaceae (2.34–2.53 log CFU/g), and CPS (2.02–2.55 log CFU/g), while for the control sample the values increased. The most obvious antimicrobial effect was observed in the case of the cold-pressed oil addition (1%), followed by the alcoholic extract (1%), seeds (3%), and powder (3%).

On data efficiency of univariate time series anomaly detection models

In machine learning (ML) problems, it is widely believed that more training samples lead to improved predictive accuracy but incur higher computational costs. Consequently, achieving better data efficiency, that is, the trade-off between the size of the training set and the accuracy of the output model, becomes a key problem in ML applications. In this research, we systematically investigate the data efficiency of Univariate Time Series Anomaly Detection (UTS-AD) models. We first experimentally examine the performance of nine popular UTS-AD algorithms as a function of the training sample size on several benchmark datasets. Our findings confirm that most algorithms become more accurate when more training samples are used, whereas the marginal gain for adding more samples gradually decreases. Based on the above observations, we propose a novel framework called FastUTS-AD that achieves improved data efficiency and reduced computational overhead compared to existing UTS-AD models with little loss of accuracy. Specifically, FastUTS-AD is compatible with different UTS-AD models, utilizing a sampling- and scaling law-based heuristic method to automatically determine the number of training samples a UTS-AD model needs to achieve predictive performance close to that when all samples in the training set are used. Comprehensive experimental results show that, for the nine popular UTS-AD algorithms tested, FastUTS-AD reduces the number of training samples and the training time by 91.09–91.49% and 93.49–93.82% on average without significant decreases in accuracy.