Scaling Method Research Articles

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

ABSTRACT The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very well with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, the delta change method seems to have with no biases for maximum temperature, but increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

A comparative assessment of machine learning algorithms in the IoT-based network intrusion detection systems

The rapid increase in online risks is a reflection of the exponential growth of Internet of Things (IoT) networks. Researchers have proposed numerous intrusion detection techniques to mitigate the harm caused by these threats. Enterprises use intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) to keep their networks safe, stable, and accessible. Network intrusion detection solutions have lately integrated powerful Machine Learning (ML) techniques to safeguard IoT networks. Selecting the proper data features for effectively training such ML models is critical to maximizing detection accuracy and computational efficiency. However, the efficiency of these systems degrades in high-dimensional data spaces, and it is crucial to have a suitable feature extraction method to eliminate extraneous data from the classification procedure. The detection accuracy and false positive rate of many ML-based IDSs also rise when the samples used to train the models are unbalanced. This study provides a detailed overview of the UNSW-NB15(DS-1) and NF-UNSWNB15(DS-2) datasets for intrusion detection, which will be utilized to develop and evaluate our models. In addition, this model uses the MaxAbsScaler algorithm to implement a filter-based feature scaling strategy . Then, use the condensed feature set to perform several ML techniques, including Support Vector Machines (SVM), K-nearest neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF), considering multiclass classification. Accuracy tests for the multiclass classification scheme were improved from 60% to 94% using the MaxAbsScaler-based feature scaling method.

A Novel Neutrosophic Likert Scale Analysis of Perceptions of Organizational Distributive Justice via a Score Function: A Complete Statistical Study and Symmetry Evidence Using Real-Life Survey Data

In this study, ten questions measuring distributive justice on classical Likert and neutrosophic Likert scales consisting of two subdimensions—distributive and procedural justice—were used. Participants responded to the same questions for both the classical Likert and neutrosophic Likert scales within a single survey, with the neutrosophic method applied, for the first time, to the questions included in the scale. The neutrosophic scale responses were answered in percentages to resemble natural language, and the answers received for each question were reduced to the range [−1, 1] to grade the agreement approach through a score function used in neutrosophic decision-making theory. In this study, the neutrosophic scale, a scaling method with strong theoretical foundations, was compared with the traditional Likert scale. The results of the statistical analyses (exploratory factor analysis, reliability analysis, neural network analysis, correlation analysis, paired samples t-test, and one-way and two-way ANOVAs) and evaluations of the scales were compared to measure organizational justice within a single study. In this article, the symmetric and non-symmetric properties of statistical analysis that are specific to this paper in addition to general symmetric and non-symmetry properties are discussed. These symmetric and non-symmetric features are conceptualized according to the features on which each statistical analysis focuses. Finally, although this study presents a new area of research in the social sciences, we believe that the neutrosophic Likert scale and survey approach will contribute to collecting detailed and sensitive information on many topics, such as economics, health, audience perceptions, advertising responses, and product, market, and service purchase research, through the use of score functions.

Characterizing Parameter Scaling with Quantization for Deployment of CNNs on Real-Time Systems

Modern deep-learning models tend to include billions of parameters, reducing real-time performance. Embedded systems are compute-constrained while frequently used to deploy these models for real-time systems given size, weight, and power requirements. Tools like parameter-scaling methods help to shrink models to ease deployment. This research compares two scaling methods for convolutional neural networks, uniform scaling and NeuralScale, and analyzes their impact on inference latency, memory utilization, and power. Uniform scaling scales the number of filters evenly across a network. NeuralScale adaptively scales the model to theoretically achieve the highest accuracy for a target parameter count. In this study, VGG-11, MobileNetV2, and ResNet-50 models were scaled to four ratios: 0.25×, 0.50×, 0.75×, 1.00×. These models were benchmarked on an ARM Cortex-A72 CPU, an NVIDIA Jetson AGX Xavier GPU, and a Xilinx ZCU104 FPGA. Additionally, quantization was applied to meet real-time objectives. The CIFAR-10 and tinyImageNet datasets were studied. On CIFAR-10, NeuralScale creates more computationally intensive models than uniform scaling for the same parameter count, with relative speeds of 41% on the CPU, 72% on the GPU, and 96% on the FPGA. The additional computational complexity is a tradeoff for accuracy improvements in VGG-11 and MobileNetV2 NeuralScale models but reduced ResNet-50 NeuralScale accuracy. Furthermore, quantization alone achieves similar or better performance on the CPU and GPU devices when compared with models scaled to 0.50×, despite slight reductions in accuracy. On the GPU, quantization reduces latency by 2.7× and memory consumption by 4.3×. Uniform-scaling models are 1.8× faster and use 2.8× less memory. NeuralScale reduces latency by 1.3× and dropped memory by 1.1×. We find quantization to be a practical first tool for improved performance. Uniform scaling can easily be applied for additional improvements. NeuralScale may improve accuracy but tends to negatively impact performance, so more care must be taken with it.

Ranking the oil contribution of individual layers in a lacustrine shale oil system based on non-hydrocarbon analysis by FT-ICR MS

Identifying the dominating oil-producing layer(s) within a shaly system, typically characterized by multiple layers with similar properties, is always a critical yet formidable task, as the oil component disparities among these closely adjacent layers are too minor to be resolved by the traditional geochemical fingerprints. This challenge is now likely addressed by high-resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS), which can resolve thousands of non-hydrocarbons that could serve as new fingerprints. Taking a typical shaly system in the Ordos Basin in China as an example, the specific proportions of non-hydrocarbon compounds from the retained petroleum of different source rock layers (rock extracts) and the produced oil at the wellhead were identified by FT-ICR MS. Their compositional similarity was calculated using a multidimensional scaling (MDS) method, and the layers hosting the retained petroleum with higher compositional similarity to the produced oils are considered to be the main contributors to oil production. The results show that the main producing layers identified by FT-ICR MS differ from those proposed based on traditional fingerprints like the Rock-Eval parameters. A typical example is that the thick low-TOC shales, conventionally proposed to be unessential to oil production, seem to have a similar contribution, if not higher, to their thick silty counterparts. This divergence may be attributed to the fact that the conventional fingerprints primarily rely on the rock extracts from a vertical well section, which may only represent a limited lateral area, while the non-hydrocarbon similarity approach involving the produced oil appears to be more realistic, as it can consider the engineering processes, like the horizontal well track and the hydraulic fracturing effects. The current approach provides a novel route for identifying the dominating producing layer(s) in a shale oil system, which may have extensive potential for optimizing the production strategy of shale oil wells.

Predictive Modeling of Extreme Weather Forecasting Events: an LSTM Approach

For a variety of industries, including agriculture, water resource management, and flood forecasting, accurate rainfall prediction is crucial. The purpose of this research work is to improve rainfall forecast system by employing the Long Short-Term Memory (LSTM) based system. The LSTM utilized in the aforementioned study made predictions by using meteorological input variables such as temperature, humidity, and rainfall. Numerous elements affect the LSTM network's performance, such as the kind and volume of data, the suitability of the model architecture, and the tuning of hyperparameters. The dataset used for model training spans from January 2015 to December 2021 and includes rainfall data collected from the Zonal Agricultural Research Station (ZARS), Shenda Park, Kolhapur. Prior to model training, the input data undergoes rigorous preprocessing. This preprocessing involves data correction, achieved through moving averages, followed by feature scaling and normalization methods. These steps are crucial to align the dataset with the unique capabilities of the LSTM model. The total dataset has a R squared (R2) value 0.23517 and a mean squared error (MSE) value 92.1839, according to the simulated findings. These metrics affirm the robust performance of the LSTM model, suggesting a high probability of accurate rainfall predictions, particularly in non-linear and complex scenarios. Decision-makers in flood predictions, agriculture, and water resource management will find the knowledge gathered from this study to be useful. They emphasize how crucial it is to use cutting-edge techniques like LSTM to increase rainfall forecast accuracy and guide strategic planning in associated industries.

Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables.

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

Characteristics of jet noise: A synthesis

It is a distinct privilege to produce this article to honor Professor Tam, the foremost authority on jet aeroacoustics. The fundamental characteristics of jet noise have been studied for 70 years, since the pioneering work of Lighthill in the 1950s. The acoustic analogy, with many variants, has served as the leading theory for nearly 50 years. Many leading researchers in the 1970s formulated theories to interpret the measured trends from subsonic and supersonic jets, using acoustic analogy and flow features as the framework. Quadrupoles, dipoles and monopoles were believed to constitute the sources of noise. The discovery of large-scale organized structures in free shear layers and jets sparked a different avenue of thinking about their importance for noise generation. Tam was the first to clearly demonstrate that these structures are efficient generators of noise and constitute the dominant noise sources, especially in the downstream direction. Now, two schools of thought emerged on the sources of jet noise. Experimental measurements showed that the mean flow as well as the turbulence statistics exhibit a self-similarity in the mixing layer and another similarity in the fully developed jet. Based on these observations, Tam proposed that since noise is generated by the turbulence of the jet, the noise spectra generated by fine-scale and large-scale turbulence should also exhibit self-similarity. By examining a large set of supersonic jet noise data acquired at NASA Langley, Tam offered evidence that the turbulent mixing noise of high-speed jets does consist of two independent self-similar components. In this paper, experimental evidence is compiled from an extensive database that quantifies the effect of several parameters that affect jet spectra. A new scaling method is developed and extended to noise predictions for realistic dual-stream nozzle geometries. The objectives of this paper are to serve as a synthesis of noise characteristics and to focus on application to real-world problems. Results from five different experimental measurements are examined: (1) farfield spectral characteristics; (2) azimuthal and polar correlations in the farfield; (3) correlations of jet turbulence fluctuations and farfield sound; (4) measurement of source distributions with an elliptic mirror; and (5) space-time correlation measurements in the nearfield with a cage array, and nearfield-farfield correlations. Two distinctly different trends are observed in the angular ranges of 50° – ∼120° and ∼120° – 165° for all the parameters investigated with the above five approaches. The salient observations are mutually supporting, and the cumulative weight lends credence to the proposition that there are two distinct sources of turbulent mixing noise.

Impact of Obesity on Hepatic Drug Clearance: What are the Influential Variables?

Drug clearance in obese subjects varies widely among different drugs and across subjects with different severity of obesity. This study investigates correlations between plasma clearance (CLp) and drug- and patient-related characteristics in obese subjects, and evaluates the systematic accuracy of common weight-based dosing methods. A physiologically-based pharmacokinetic (PBPK) modeling approach that uses recent information on obesity-related changes in physiology was used to simulate CLp for a normal-weight subject (body mass index [BMI] = 20) and subjects with various severities of obesity (BMI 25–60) for hypothetical hepatically cleared drugs with a wide range of properties. Influential variables for CLp change were investigated. For each drug and obese subject, the exponent that yields perfect allometric scaling of CLp from normal-weight subjects was assessed. Among all variables, BMI and relative changes in enzyme activity resulting from obesity proved highly correlated with obesity-related CLp changes. Drugs bound to α1-acid glycoprotein (AAG) had lower CLp changes compared to drugs bound to human serum albumin (HSA). Lower extraction ratios (ER) corresponded to higher CLp changes compared to higher ER. The allometric exponent for perfect scaling ranged from -3.84 to 3.34 illustrating that none of the scaling methods performed well in all situations. While all three dosing methods are generally systematically accurate for drugs with unchanged or up to 50% increased enzyme activity in subjects with a BMI below 30 kg/m2, in any of the other cases, information on the different drug properties and severity of obesity is required to select an appropriate dosing method for individuals with obesity.Graphical

Tackling the curse of dimensionality with physics-informed neural networks

The curse-of-dimensionality taxes computational resources heavily with exponentially increasing computational cost as the dimension increases. This poses great challenges in solving high-dimensional partial differential equations (PDEs), as Richard E. Bellman first pointed out over 60 years ago. While there has been some recent success in solving numerical PDEs in high dimensions, such computations are prohibitively expensive, and true scaling of general nonlinear PDEs to high dimensions has never been achieved. We develop a new method of scaling up physics-informed neural networks (PINNs) to solve arbitrary high-dimensional PDEs. The new method, called Stochastic Dimension Gradient Descent (SDGD), decomposes a gradient of PDEs’ and PINNs’ residual into pieces corresponding to different dimensions and randomly samples a subset of these dimensional pieces in each iteration of training PINNs. We prove theoretically the convergence and other desired properties of the proposed method. We demonstrate in various diverse tests that the proposed method can solve many notoriously hard high-dimensional PDEs, including the Hamilton–Jacobi-Bellman (HJB) and the Schrödinger equations in tens of thousands of dimensions very fast on a single GPU using the PINNs mesh-free approach. Notably, we solve nonlinear PDEs with nontrivial, anisotropic, and inseparable solutions in less than one hour for 1000 dimensions and in 12 h for 100,000 dimensions on a single GPU using SDGD with PINNs. Since SDGD is a general training methodology of PINNs, it can be applied to any current and future variants of PINNs to scale them up for arbitrary high-dimensional PDEs.

Scaling analysis of nuclear power plant (APR1400) utilizing ATLAS integral effect test result of A5.1 (Counterpart test for LSTF 1% cold-leg break LOCA)

ATLAS is the thermal hydraulic integral effect test loop that can simulate the same pressure and temperature condition with nuclear power plant. It was designed with a height ratio of 1/2 and a volume ratio of 1/288 with reference to APR1400. The counterpart test against LSTF which was named as A5.1 test was performed within the OECD/NEA ATLAS international joint research project to compare major thermal hydraulic phenomena between two test facilities. The main scenario for the A5.1 test is the LSTF SB-CL-32 test. The SB-CL-32 test is a 1% horizontal small break loss of coolant accident (SBLOCA) at the cold leg with cold leg injection of the emergency core cooling system (ECCS) and accident management (AM) action.From the counterpart test results, the significant differences were found in the viewpoint of the thermal–hydraulic phenomena. It was concluded that the different result were caused by the different design characteristic of each test facility because they referred the different nuclear power plant. Thus, the applicability of the ATLAS test results to its reference nuclear power plant was evaluated by utilizing ATLAS A5.1 test results which was the counterpart test with the LSTF.The transient scenario and initial and boundary conditions of the A5.1 test were adopted to APR1400 after applying scaling ratios of ATLAS. The MARS-KS code was utilized to analyzed thermal hydraulic behaviors of APR1400 and the analysis results were compared with the scaled A5.1 test result. From the result of comparison, it was concluded that the ATLAS test results predicted the thermal hydraulic behavior of the nuclear power plant well. Thus, it was confirmed that ATLAS is a well-designed integral effect test facility which is designed with an appropriate scaling method.

Ultra‐fast finite element analysis of coreless axial flux permanent magnet synchronous machines

AbstractLarge‐scale design optimisation techniques enable the design of high‐performance electric machines. Electromagnetic 3D finite element analysis (FEA) is typically employed in optimisation studies for accurate analysis of axial flux permanent magnet (AFPM) machines, which require extensive computational resources. To reduce the computational burden, a FEA‐based mathematical method relying on the geometric and magnetic symmetry of coreless AFPM machines is proposed to estimate the machine performance indicators using the least number of FEA solutions, thereby significantly lowering the running time. This method is generally applicable to AFPM machines with low saturation effects and cogging torque as exemplified for a printed circuit board (PCB) stator coreless AFPM machine. To further reduce the computation time, a systematically simplified equivalent 3D FEA model for planar PCB coils integrated with this machine is also proposed. The practical implementation of the introduced method is elaborated based on an example optimisation study, and an analytical method for fast design scaling is also discussed. The results of the proposed approach are compared with detailed transient FEA results, and a prototype 26‐pole PCB stator coreless AFPM machine was also used to validate the results experimentally.

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

An overview of icing scaling parameters for supercooled large drop icing conditions in icing wind tunnels

Aircraft icing is a serious threat to flight safety, especially the abnormal ice accumulation on the wing surface under the icing conditions of supercooled large droplets (SLD). Icing wind tunnel test is one of the main means of conducting research on aircraft icing, and the icing scaling law is the theoretical basis for selecting the similarity of characteristic parameters. In order to deeply understand the problems related to icing similarity in icing wind tunnel test under SLD icing conditions, the test capabilities on SLD icing conditions for the existing ice wind tunnel and the common similar parameter scaling methods were reviewed. The icing similarity transformation of SLD icing test was emphasized and the advantages, disadvantages, and applicability of each icing scaling law were analyzed. It can provide some references for the SLD icing wind tunnel test for large civil aircraft.

Local and Object-Based Perspectives on Atmospheric Rivers Making Landfall on the Western North American Coastline

Abstract We apply the Ralph et al. scaling method to a reanalysis dataset to examine the climatology and variability of landfalling atmospheric rivers (ARs) along the western North American coastline during 1980–2019. The local perspective ranks AR intensity on a scale from 1 (weak) to 5 (strong) at each grid point along the coastline. The object-based perspective analyzes the characteristics of spatially independent and temporally coherent AR objects making landfall. The local perspective shows that the annual AR frequency of weak and strong ARs along the coast is highest in Oregon and Washington and lowest in Southern California. Strong ARs occur less frequently than weak ARs and have a more pronounced seasonal cycle. If those ARs with integrated water vapor transport (IVT) weaker than 250 kg m−1 s−1 are included, there is an enhanced seasonal cycle of AR frequency in Southern California and a seasonal cycle of AR intensity but not AR frequency in Alaska. The object-based analysis additionally indicates that strong ARs at lower latitudes are associated with stronger wind than weak ARs but similar moisture, whereas strong ARs at higher latitudes are associated with greater moisture than weak ARs but similar wind. For strong ARs, IVT at the core is largest for ARs in Oregon and Washington and smaller poleward and equatorward. Both IVT in the AR core and cumulative IVT along the coastline usually decrease after the first day of landfall for weak ARs but increase from the first to second day for strong ARs.

The application of multidimensional scaling of data to determining changes in retailer customers’ preferences

The COVID-19 pandemic has significantly affected several aspects of human activity, including the functioning of different markes, therefore it is important to research its impact. The aim of the study discussed in this paper is to determine which of the three approaches within the method of multidimensional scaling (i.e. multidimensional scaling of classical, symbolic interval-valued or symbolic histogram data) is most adequate for capturing the shifts in retailer customers' preferences that took place during the pandemic. The research concerned the health and beauty market. It was based on the data on orders for beauty products from a Polish producer of cosmetics of a global reach placed by 18 small, mainly family-managed, health and beauty retailers from Lower Silesia. The shops were selected through convenient sampling. Such shops are not a part of large health and beauty retailer chains, therefore they are more vulnerable to all the fluctuations and shifts on the market. The results of this study indicate that in 2020 and 2021, important changes took place on the Lower Silesian health and beauty market as compared to 2019. These changes involved cosmetics for eyes and eybrows gaining popularity at the expense of cosmetics for lips and cheeks. Multidimensional scaling of symbolic histogram data turned out to be the most effective method (in the sense of the measure of fit and the Pearson correlation coefficient) of capturing and analysing changes happening on a market over a period of time.

초등교사의 수업실행 저해요인에 관한 연구

Objectives This study focused on the factors which inhibit the teaching practices as they were perceived by elementary school teachers. Methods Thirty-five elementary school teachers were interviewed, and research materials were analyzed using a concept map approach. From the interview, 44 key sentences were extracted as a final statement, and the concept mapping was completed using the Multidimensional Scaling and Hierarchical Cluster Analysis method. 970 elementary school teachers were asked to answered the 5-point Likert scale questionnaires to check which statement could affect the teaching practices. Results First, the elementary school teachers recognized the factors inhabiting the teaching practices in the “job performance-relationship formation” and “inside-outside the school” dimensions. Second, elementary school teachers recognized the inhabiting factors in 8 clusters; “formalism of educational administration,” “inefficiency of school work,” “lack of trust between teachers and parents,” “lack of efficiency in business processing,” “lack of administrators’ leadership,” “lack of teaching support,” “negative teaching culture,” and “lack of professionalism and teacher’s philosophy” appeared in that order. Conclusions This study will help to identify ways to improve the teaching expertise by identifying the inhibiting factors as perceived by elementary school teachers.

A Machine Learning Approach for Early-Stage Autism Spectrum Disorders Detection

Abstract: The undertaking proposes a Machine Learning (ML) system for early ID of Autism Spectrum illness (ASD), recognizing the hardships of taking out the sickness yet endeavoring to diminish its seriousness through early therapies. The proposed system tests four Feature Scaling (FS) methods (Quantile Transformer, Power Transformer, Normalizer, Max Abs Scaler) on four normal ASD datasets from babies to grown-ups. ML calculations (e.g., Ada Boost, Random Forest, Decision Tree, K-Nearest Neighbors, Gaussian Naïve Bayes, Logistic Regression, SVM, LDA) are utilized on included scaled datasets. The best classifiers and FS strategies for each age bunch are recognized utilizing factual estimations. The voting classifier predicts ASD with the greatest accuracy for Babies, Kids, Young people, and Grown-ups. The task incorporates a definite element significance examination utilizing four Component Determination Strategies to underline the significance of calibrating ML techniques in foreseeing ASD across age gatherings and to assist medical services specialists with pursuing ASD screening choices. In contrast with current early ASD discovery strategies, the proposed structure performs well. To further develop ASD identification strength and precision, a group procedure using a Voting Classifier with Random Forest (RF) and AdaBoost achieved 100% accuracy.

Conversion Factors for Physical Property Measurements in different Rest Frames Using a Revised Version of Relativity Theory Based on the Uniform Scaling Method

The history of the development of the Uniform Scaling Method is reviewed. It has its beginning with the Law of Causality and Newton’s First Law of Kinetics (Law of Inertia). On this basis it can be concluded that the rates of clocks do not change spontaneously, i.e. without the application of some external force. Since the ratio Q of the rates of any two such (inertial) clocks must therefore itself be a constant, it follows that whenever they are used to measure a specific time difference between two events, their respective results must always be in the same ratio: Δt’=Δt/Q. This relationship is referred to as Newtonian Simultaneity since it guarantees that if one of the clocks finds that the events occurred simultaneously (Δt’=0), the other must do so as well (Δt=0). A useful way to interpret Q is as a conversion factor between the different units of time employed in the two rest frames. If we assume that the value of the speed of light is the same in both rest frames, it follows that they must agree on the unit of speed, i.e. the conversion factor in this case is unity (Q0 ). It therefore follows that the conversion factor for the unit of distance must be exactly the same as that for time, namely also Q. This claim of the Uniform Scaling Method is thus seen to be in direct contradiction to the length contraction tenet of Einstein’s theory of relativity introduced in 1905. Experiments with accelerated electrons carried out by Bucherer in 1909 indicate that their inertial mass increase in direct proportion to γ (v) = (1-v2 c-2)-0.5 (v is the speed of the electrons and c = 299792458 ms-1 is the speed of light in free space), the same ratio as observed for the periods of atomic lines by Ives and Stilwell in 1938. Consistency therefore requires that the conversion factor for inertial mass is the same (Q) as for time. Since inertial mass, time and distance are the three fundamental quantities on which all other physical properties are defined (mks system), it follows that the conversion factor for any other property must be an integral multiple of Q. An analogous set of definitions can also be justified for the corresponding conversion factors in the same two rest frames caused by gravitational effects. In this case the conversion factors are always integral multiples of the quantity S, which therefore plays as analogous role as Q in kinetic scaling. Since experiments with circumnavigating atomic clocks carried out in 1971 by Hafele and Keating found that the two types of effects are completely independent of one another, it is therefore useful to define a product Z of the corresponding Q and S factors for each physical property, including those that are used to define the results of electromagnetic measurements. These total conversion factors are stored in Tables 1 and 2 of the manuscript. They are shown to guarantee that if a physical law is valid in one of the rest frames, it will also hold in the other. This relationship allows for an Addendum to Galileo’s Relativity Principle: The laws of physics are the same in all inertial rest frames, but the units in which their results are expressed can differ from one rest frame to another.

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

Data input within mixed reality environments poses significant interaction challenges, notably in immersive visual analytics applications. This study assesses five numerical input techniques: three benchmark methods (Touch-Slider, Keyboard, Pinch-Slider) and two innovative multimodal techniques (Bimanual Scaling, Gesture and Voice). An experimental design was employed to compare these techniques’ input efficiency, accuracy, and user experience across varying precision and distance conditions. The findings reveal that multimodal techniques surpass slider methods in input efficiency yet are comparable to keyboards; the voice method excels in reducing cognitive load but falls short in accuracy; and the scaling method marginally leads in user satisfaction but imposes a higher physical load. Furthermore, this study outlines these techniques’ pros and cons and offers design guidelines and future research directions.