Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness and generality in cost forecasting of substation projects.

In this paper, the classical least square (CLS) method with molecular absorption spectrophotometric measurement was used to determine simultaneously of paracetamol (PAR), ibuprofen (IBU), and caffeine (CAF) in tablets. The absorbance spectra of the standard solutions and samples were measured over a wavelength from 220 to 300 nm with a 0.5 nm step. The concentration of PAR, IBU and CAF in the sample solutions were calculated by using a program called CLS-Excel written in Microsoft Excel 2016 and Visual Basic for Applications (VBA). The method and CLS-Excel program were tested on mixed standard laboratory samples with different PAR, IBU, and CAF concentration ratios, and they showed small errors and satisfying repeatability. An analytical procedure for tablets containing PAR, IBU, and CAF was developed. The reliability of the procedure was proved via the recovery and repeatability of the analysis results with an actual tablet sample and comparing the mean contents of active substances in the tablets obtained from the analytical procedure with the from the HPLC method. The procedure is simple with a reduced cost compared with the HPLC standard method.

The Van der Pol oscillator is a chaotic non-linear system. Small perturbations in initial conditions may result in wildly different trajectories. Because of its chaotic nature, controlling, or forcing, the behavior of a Van der Pol oscillator is difficult to achieve through traditional adaptive control methods. Connecting two Van der Pol oscillators together where the output of one oscillator, the driver, drives the behavior of its partner, the responder, is a proven technique for controlling the Van der Pol oscillator. Deterministic AI (DAI) is an adaptive feedback control method that leverages the known physics of the Van der Pol system to learn optimal system parameters for the forcing function. We assessed the performance of DAI employing three different online parameter estimation algorithms. Our evaluation criteria include mean absolute error (MAE) between the target trajectory and the response oscillator trajectory over time. RLS with exponential forgetting (RLS-EF) had the lowest MAE overall, with a 2.46% reduction in error. However, another method was notable. Least Mean Squares with normalized gradient adaptation (LMS-NG) had worse initial error in the first 10% of the simulation, but after that point had consistently better performance. We found that over the last 90% of the simulation, DAI with LMS-NG had a 48.7% reduction in MAE compared to feedforward alone.

Based on the satellite attitude control method, this paper proposes an attitude control method based on neural network disturbance compensation. The paper firstly analyzes the neural network algorithm and proposes an orthogonal least squares algorithm to implement network learning. In this paper, a set of high-precision directional neural network compensation controllers is designed for the attitude control of acupuncture small satellites. The feasibility of the improved orthogonal least-squared algorithm combined with the neural network supplementary control method in satellite attitude control is verified by experiments.

Since the electro-hydraulic servo shaking table exists many nonlinear elements, such as, dead zone, friction and blacklash, its acceleration response has higher harmonics which result in acceleration harmonic distortion, when the electro-hydraulic system is excited by sinusoidal signal. For suppressing the harmonic distortion and precisely identify harmonics, a combination of the adaptive linear neural network and least mean M-estimate (ADALINE-LMM), is proposed to identify the amplitude and phase of each harmonic component. Namely, the Hampel’s three-part M-estimator is applied to provide thresholds for detecting and suppressing the error signal. Harmonic generators are used by this harmonic identification scheme to create input vectors and the value of the identified acceleration signal is subtracted from the true value of the system acceleration response to construct the criterion function. The weight vector of the ADALINE is updated iteratively by the LMM algorithm, and the amplitude and phase of each harmonic, even the results of harmonic components, can be computed directly online. The simulation and experiment are performed to validate the performance of the proposed algorithm. According to the experiment result, the above method of harmonic identification possesses great real-time performance and it has not only good convergence performance but also high identification precision.

Intelligent transportation systems (ITS) have recently evolved rapidly, which requires development of highly trustworthy and effective communication technologies for uses, including vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication; where Orthogonal Frequency Division Multiplexing (OFDM) is regarded as a strong candidate and highly popular option technique among these methods. However, the movement of vehicles introduces Doppler frequencies which produce inter-carrier interference (ICI), that is frequently occurs in V2X channels. This interference has the potential to compromise the integrity of subcarrier orthogonality within OFDM, leading to lower-quality communication and an increased likelihood of data transmission errors. When employing channels with doubly dispersive fading, OFDM necessitates the usage of a complex equalization based on the minimum mean-square error (MMSE) equalizer, which requires channel matrix inversion. Several low-complexity equalizers for OFDM have been developed and are based on band factorization, time domain LSQR (Least-Square QR) iterative computing, and banded minimum mean squared error (BMMSE). This paper proposes Conjugate Gradient Least Squares (CGLS), which is a novel iterative computation algorithm integrated with nonlinear equalizers. The suggested nonlinear equalization technique determines the trade-off between computations and performance. According to simulation data, the suggested nonlinear equalizer performs better than the current BMMSE and linear CGLS algorithms across doubly dispersive fading channels.

This paper presents a) the novel hybrid learning method to train the type-1 non-singleton interval type-3 (IT3) fuzzy logic systems (IT3 NSFLS-1) and b) the novel method named enhanced Wagner-Hagras (EWH) IT3 NSFLS-1 fuzzy systems which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the back propagation (BP) method to train the antecedent parameters. This proposal dynamically changes the parameters of only the level alpha 0 minimizing some criterion function as new information becomes available to each level alpha k. The antecedent sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the EWH IT3 NSFLS-1 (OLS-BP) system. The algorithm was tested in a hot strip mill facility to predict the transfer bar surface temperature showing its superior capability to obtain the industrial pyrometer’s knowledge uncertainty for tuning and its better performance when compared with IT2 SFLS, IT2 NSFLS-1, GT2 SFLS, GT2 NSFLS-1, IT3 SFLS, and IT3 NSFLS-1 trained with the BP-BP algorithm.

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

Introducing the Moving Least-Squares Aided Finite Element Method (MLS-FEM), we present a robust computational technique that effectively evaluates the development of mixing in polymeric mixing devices. Our approach uses the Moving Least-Squares (MLS) interpolant to enhance the classic finite element shape functions. As a result, it becomes possible to treat fixed or moving components, such as rotors of a mixer, as discontinuities within the flow domain. This allows analysis of the flow field with a background mesh independent of the rotors' shape, orientation, and position. We compared the MLS-FEM results with the classical boundary-fitted mesh FEM, which comprises the mesh regeneration steps by the movement of the rotors. We then showed that the new method provides accurate results for the flow field parameters (i.e., the velocity components and the pressure values) and the mixing evolution without requiring mesh regeneration and without compromising accuracy.

This current study examines the economics of yam marketing in Bosso local government area of Niger State Nigeria, to this end, statistical tools such as descriptive statistics, marketing margin model, and regression analysis procedures was utilized as estimation techniques. The study relies on random sampling technique of selected 80 yam marketers. The motivation for this study, rest on the dominance of yam marketing operation in the study area as well as the need to chart a better and more efficient system of agricultural marketing for yam marketers in the sampled area. The empirical result reveals that an overwhelming majority (95.1%) of the marketers were 21-50 years of age with 6 years and above of marketing experience dominating (90%). However, most of the yam marketers started the business with capital base of N1,000- N20,000, it was also discovered was that majority (58.8%) of the marketers were female and within their active age with mean age of 35 years which goes to show that female can contribute to the responsibility of the family. The study also seeks to explore marketer’s preference for source of funding either from the formal institutions such as (Banks and Co-operatives) or the informal institutions (from friends or relatives) which is key in the effective and efficient in yam marketing. Finally, the study is set out to find out potential constraints to the attainment of the full gains of yam marketing in the study area and offer workable and visible remedy.

In this paper, the optimal approximation algorithm is proposed to simplify non-linear functions and/or discrete data as piecewise polynomials by using the constrained least squares. In time-sensitive applications or in embedded systems with limited resources, the runtime of the approximate function is as crucial as its accuracy. The proposed algorithm search to find the Optimal Piecewise Polynomial (OPP) with minimum computational cost while ensuring the error below a specified threshold. This was accomplished by using smooth piecewise polynomials with optimal order and number of intervals. The computational cost only depends on polynomial complexity, i.e., the order and the number of intervals at runtime function call. For optimal approximation, computational costs for all the possible combinations of piecewise polynomials were calculated and tabulated as ascending order for the specific target CPU off-line. Each combination was optimized through constrained least squares and random selection method for given sample points. Afterward, whether the approximation error is below the predetermined value is examined. When the error is permissible, the combination is selected as the optimal approximation or the next combination was examined. To verify the performance, several representative functions were examined and analyzed.

The paper attempts to explore the relationship between federalism and rural development. Federalism is a division of power, responsibility and accountability to bring the administrative and political power closer to the ground and essentially to increase the good governance. On the other hand, rural development is a complex and multidimensional issue- especially much demanding for least developed and developing countries. A descriptive and qualitative approach was carried out to study the complex relationship between rural development and federalism. Similarly, a SWOT analysis was carried out to have a better understanding of the relationship. The study found that there is significant potentiality for accelerated development of rural landscape in federalism if carefully executed. However, on the other hand, federalism may pose several risks on rural development and may restrict the development pace if not executed with appropriate care and understanding. Therefore, the study concludes that cooperation and coordination among the federal structures are crucial for better development of the rural economy.

A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the direction of sampling time and batch, a new definition region of support (ROS), k-i-back-extended region of support (KIBROS), is proposed to establish the extended window, which including the data of the previous sampling times in the same batch, the data of the same sampling time in the historical batches, the data of the previous sampling times in the historical batches and the data of the later sampling times in the historical batches. According to these regions of support, extended windows are established, and different models are proposed based on the extended windows for batch processes quality prediction. Further, using a typical batch process as an example, the injection molding process, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and the prediction stability is also improved.

This paper studies single change-point detection in the volatility of a class of parametric conditional heteroscedastic autoregressive nonlinear (CHARN) models. The conditional least-squares (CLS) estimators of the parameters are defined and are proved to be consistent. A Kolmogorov-Smirnov type-test for change-point detection is constructed and its null distribution is provided. An estimator of the change-point location is defined. Its consistency and its limiting distribution are studied in detail. A simulation experiment is carried out to assess the performance of the results which are also applied to two sets of real data.

This paper uses the complex regression analysis method to establish the customer’s load regression models, which consider economic indicators, temperature and rainfall. Furthermore, the proposed models are used to study the forecasting feasibility of the future energy sales and summer peak load demand. At first, this paper used least-squares techniques to derive regression models for considering economic indicators and temperature of 34 customer energy sales and total energy sales. Besides, the AMI high voltage customer demand data and system generating capacity for 24 hours were adopted to forecast summer peak load. The above-mentioned data analysis tool is used by EViews software to achieve, in order to verify the feasibility of the research framework. The study found that although its forecasting model accuracy is low only when mixed with temperature and high voltage demands. So, when mixed with high voltage demand data and system generating capacity for 24 hours to forecast peak load, the average error is ± 0.87% and in the majority of its energy sales forecasting model of average error is ±3%. This result can provide power company as future reference.

This case report investigates 5 real cases which followed legal channels and were judged by Mato Grosso Court in Brazil. Audio systems served as elements of key evidence on those lawsuits. The goal here is to analyze the cases by using a methodology based on the forensic speaker verification by using the Ordinary Least Squares (OLS) algorithm and to compare results with analyses obtained on real cases. The comparative analysis is assessed for time elapsed for obtaining results, as well as results quality. In Brazil, the lawsuit duration is very important, since the Penal Code foresees prescription after a given time, and it may lead to impunity. Results show that the analysis, by using OLS, generates immediate, effective results when compared to those obtained with traditional methodologies on the studied Brazilian lawsuits.

In tele-monitoring, wireless body area networks (WBANs), sleep analysis and other applications involving electroencephalogram (EEG) signal, due to the high number of EEG recording channels, long recording time and several repetition of recordings to reach the highest signal-to-noise ratio, the amount of acquired data by the sensors is too large, demanding use of some compression procedure. Compressed sensing can be considered as one of the most effective compression methods in terms of compression ratio, which needs the underlying signal be sparse or have sparse representation in an appropriate domain. EEG signal is not sparse in time domain, therefore, in this paper correlation based weighted recursive least squares dictionary learning algorithm (CBW-RLS) is proposed that uses between-channel correlations to sparsify EEG signals. Due to the low-rank structure of EEG signals, exploiting between-channel correlations increase the sparsity level of the model while reducing the computational cost of dictionary learning procedure. This is done by merely updating the dictionary atoms which are involved in the sparse model of the previous data, reducing the total number of data used at each iteration and speeding up the dictionary learning algorithm. The simulation results show that the proposed method has better performance in terms of both quality of the EEG reconstruction and the computational cost compared to the other methods.

This study was undertaken to derive and analyze the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) sea surface temperature (SST) footprint associated with the Remote Sensing Systems (RSS) Level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed to each 4  4 km square contributing to the SST value of a given AMSR-E pixel. High-resolution L2 SST fields obtained from the MODerate-resolution Imaging Spectroradiometer (MODIS), carried on the same spacecraft as AMSR-E, are used as the sub-resolution “ground truth“ from which the AMSR-E footprint is determined. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a constrained least square approximation based on the bootstrap sampling procedure. The method yielded an elliptic-like Gaussian kernel with an aspect ratio  1.58, very close to the AMSR-E 6.93GHz channel aspect ratio,  1.7. (The 6.93GHz channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be alignedwith the instantaneous field-of-view of the sensor as expected fromthe geometric characteristics of AMSR-E. Footprintswere also analyzed year-by-year and as a function of latitude and found to be stable – no dependence on latitude or on time. Precise knowledge of the footprint is central for any satellite-derived product characterization and, in particular, for efforts to deconvolve the heavily oversampled AMSR-E SST fields and for studies devoted to product validation and comparison. A preliminarly analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained.

The static formation temperature (SFT) is required to determine the thermophysical properties and production parameters in geothermal and oil reservoirs. However, the SFT is not easy to be obtained by both experimental and physical methods. In this paper, a mathematical approach to predicting SFT based on a new model describing the relationship between bottom hole temperature (BHT) and shut-in time was proposed. The unknown coefficients of the model were derived from least squares fit by Particle Swarm Optimization (PSO) algorithm. Besides, the ability to predict SFT based on a few BHT data (such as first 3, 4, or 5 ones of a data set) was evaluated. The accuracy of the proposed method to predict SFT was testified with a deviation percentage less than ±4% and high values of regression coefficient R² (>0.98). The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells.

Omega-3 fatty acids (ω-3 FAs) are important fatty acids having the beneficial roles in human health including reducing blood pressure, lowering the risk of cardiovascular disease and exerting anti-inflammation activities. Omega-3 FAs were mainly found in fish oils, therefore, determination of these FAs is very important. This study highlighted the employment of FTIR spectroscopy combined with multivariate data analysis for determination of ω-3 FAs in fish oils. Fish oils were obtained from the extraction of corresponding fishes and subjected to purification. The oils were further subjected to FTIR spectroscopic measurement at mid infrared region (4000-450 cm-1). Fatty acid compositions of ω-3 FAs namely eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were determined using gas chromatography with flame ionization (GC-FID), and the results from GC-FID were used as actual values. Two multivariate regressions along with wavenumbers regions or their combinations were optimized and compared to provide the best condition for prediction of EPA and DHA in fish oils. The results showed that partial least square regression (PLSR) was suitable for prediction of DHA applying the variable of absorbance values of the second derivative spectra, with the values of coefficient of determination (R2) of 0.9916 and 0.9316 in calibration and validation models, respectively. The values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) obtained were 0.789 and 2.53. While, prediction of EPA was performed using principal component regression with R2 value of > 0.72 and low values of RMSEC and RMSEC. It can be concluded that the combination of FTIR spectra and multivariate regression provides the effective tools and alternative GC-FID method for the prediction of EPA and DHA in fish oils.

Acrylamide is probably carcinogenic to humans (International Agency for Research on Cancer, group 2A) with major occurrence in heated, mainly carbohydrate-rich foods. For roasted coffee, a European Union benchmark level of 400 µg/kg acrylamide is of importance. Regularly, the acrylamide contents are controlled using liquid chromatography combined with tandem mass spectrometry (LC-MS/MS). This reference method is reliable and precise but laborious because of the necessary sample clean-up procedure and instrument requirements. This research investigates the possibility of predicting the acrylamide content from proton nuclear magnetic resonance (NMR) spectra that are already recorded for other purposes of coffee control. In the NMR spectrum acrylamide is not directly quantifiable, so that the aim was to establish a correlation between the reference value and the corresponding NMR spectrum by means of a partial least squares (PLS) regression. Therefore, 40 commercially available coffee samples with already available LC-MS/MS data and NMR spectra were used as calibration data. To test the accuracy and robustness of the model and its limitations, 50 coffee samples with extreme roasting degrees and blends were additionally prepared as test set. The PLS model shows an applicability for the varieties C. arabica and C. canephora, which were medium to very dark roasted using drum or infrared roasters. The root mean square error of prediction (RMSEP) is 79 µg/kg acrylamide (n=32). The PLS model is judged as suitable to predict the acrylamide values of commercially available coffee samples. On the other hand, very light roasts containing more than 1000 µg/kg acrylamide are currently not suitable for PLS prediction.

The purpose of this paper is threefold: (i) to introduce and study the Euler-Lagrange prolongations of flatness PDEs solutions (best approximation of flatness) via associated least squares Lagrangian densities and integral functionals on Riemannian manifolds; (ii) to analyze some decomposable multivariate dynamics represented by Euler-Lagrange PDEs of least squares Lagrangians generated by flatness PDEs and Riemannian metrics; (iii) to give examples of explicit flat extremals and non-flat approximations.

In this paper, the methods and their analysis for estimating the location of three dimension (3-D) single source buried in lossy medium are presented with uniform circular array (UCA). The mathematical model of the signal in the lossy medium is proposed. Using location information in the covariance matrix obtained by sensors’ output, the equations of the source location (azimuth angle, elevation angle and range) are obtained. The phase and amplitude of the covariance matrix function are used to process these equations respectively. Then, the phase and amplitude of the covariance matrix function are used to process the source localization in the conductive medium together. By analyzing the characteristics of the proposed methods and MUSIC method, the computational complexity and the valid range of these methods are given. From the results, whether the loss is known, we can choose the appropriate method to process the issues (localization in lossless medium or lossy medium).

The parameter estimation problem for polynomial phase signals (PPSs) arises in a number of fields, including radar, sonar, biology, etc. In this paper, a fast algorithm of parameter estimation for monocomponent PPS is considered. We propose the so-called LSU-EKF estimator, which combines the least squares unwrapping (LSU) estimator and the extended Kalman filter (EKF). First, the coarse estimates of the parameters of PPS are obtained by the LSU estimator using a small number of samples. Subsequently, these coarse estimates are used to initial the EKF. Monte-Carlo simulations show that the computation complexity of the LSU-EKF estimator is much less than that of the LSU estimator, with little performance loss. Similar to the LSU estimator, the proposed algorithm is able to work over the entire identifiable region. Moreover, in the EKF stage, the accurate estimated results can be output point-by-point, which is useful in real applications.

This paper investigates the secular decline in real interest rates and the potential influence of central bank monetary policy on long-term government bond yields, focusing particularly on the US Federal Reserve (Fed). While various demand-side and supply-side explanations, such as ageing populations, lack of investment opportunities, and reduced productivity growth, have been proposed for this decline. A recent study highlighted the significant role of 3-day time windows around Federal Open Market Committee (FOMC) meetings in capturing yield changes in 10-year Treasuries. To explore if similar patterns exist in other advanced economies, this study constructs 3-day monetary policy decision windows around the 10-year government bond yields for selected countries, using both domestic central bank decision dates and those of the Fed. The findings suggest that, despite heterogeneity among advanced economies and factors like unconventional monetary policy tools and exchange rate interventions, there is substantial evidence supporting a stronger influence of the Fed's decisions on global bond yields compared to national monetary policies. By employing a simple Ordinary Least Squared (OLS) method to analyze the covariance between bond yields and policy decisions, this study establishes a connection between the literature on the global financial cycle and secular stagnation. The results imply that the Fed's influence extends beyond US borders, potentially contributing to a global monetary policy dynamic.

Clustering is a very common analysis of data present in large datasets, with the aims of understanding and summarizing data, and discovering similarities, among others. However, despite the present success of subsymbolic methods for data clustering, the description of the obtained clusters cannot rely on the intricacies of the subsymbolic processing. For clusters of data expressed in the Resource Description Framework (RDF) we extend and implement an optimized previously proposed logic-based methodology which computed an RDF structure — called a Common Subsumer — describing the commonalities among all resources. We tested our implementation with two open, and very different RDF datasets: one devoted to Public Procurement, and the other devoted to drugs in Pharmacology. For both datasets, we were able to provide reasonably concise and readable descriptions of clusters up to 1800 resources. Our analysis shows the viability of our methodology and computation, and paves the way for general cluster explanations to lay users.

Factor analysis, a staple of correlational psychology, faces challenges with ordinal variables like Likert scales. The validity of traditional methods, particularly maximum likelihood (ML), is debated. Newer approaches, like using polychoric correlation matrices with weighted least squares estimators (WLS), offer solutions. This paper compares ML with WLS for ordinal variables. While WLS generally outperforms ML, especially with non-bell-shaped distributions, it may yield artefactual estimates with severely skewed data. ML tends to underestimate true loadings, while WLS may overestimate them. Simulations and case studies highlight the importance of item psychometric distributions. Despite advancements, ML remains robust, underscoring the complexity of analyzing ordinal data in factor analysis. There's no one-size-fits-all approach, emphasizing the need for sensitivity analyses and careful consideration of data characteristics.

In the study, a lipid mixture was developed and optimized using a broad range of excipients of different po-larities. Initially, the excipients were classified according to their behavior in water. Then binary mixtures were prepared using cetyl palmitate (Crodamol™ CP pharma) as the solid lipid. The miscibility between the excipients was evaluated using Raman chemical images obtained by classical least squares (CLS). Based on the results, the excipients Crodamol™ CP pharma (cetyl palmitate, hydrophobic), Super Refined™ DMI (di-methyl isosorbide; hydrophilic) and Super Refined™ Lauryl Lactate (medium polarity) were chosen to com-pose the lipid core. The ideal proportion of these excipients was determined using a Mixture Design and the standard deviation of image histograms as responses. The main goal was to develop a versatile lipid core for ‘brick-dust type of drugs’. After statistical evaluation of the DOE results, the final composition was deter-mined and drugs with different logP (0 to 10) and physiochemical characteristics were evaluated in the opti-mized mixture. The drugs butamben, tacrolimus, atorvastatin calcium and resveratrol presented a homoge-neous distribution in the optimized lipid core, indicating that the core has indeed the capability to be used in NLC formulations of such type of drugs.

A non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; and ash content) for a variety of chipped and ground biomasses was investigated through a combination of thermogravimetric analysis (TGA) and near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). In this study, the thermogravimetric method was employed as a tool to determine the proximate analysis data of biomass samples through TG and DTG curves, which were generated by tracking changes in biomass mass loss over time or temperature. NIRS non−destructively scanned the chipped biomass in diffuse reflectance while ground biomass using transflectance mode, covering the wavenumber range of 3594.87 to 12,489.48 cm−1. The PLSR−based models: Full−PLSR, genetic algorithm (GA)−PLSR, successive projection algorithm (SPA)−PLSR, the spectral multi−preprocessing (MP) PLSR− 5 range method, and the MP PLSR−3 range method were developed. Model performance was assessed, compared, and selected based on the coefficient of determination in the prediction set (R2P), root mean square error in the validation set (RMSEP), and the ratio of prediction to deviation (RPD). Based on the comprehensive analysis of model performance, MC and FC models of chip biomass have demonstrated satisfactory performance which makes it applicable cautiously in any application, including research. The optimum models for MC and FC in chip biomass were constructed using GA−PLSR with 2nd derivative and Full−PLSR with a constant offset, resulting in R2P values of 0.8654 and 0.8773, RMSEP values of 0.85% and 2.12%, and RPD values of 2.9 and 3.0, respectively. Other parameters such as MC and FC in ground biomass as well as VM and ash content in both chip and ground biomass were found suitable for rough screening. The sensitivity of the selected model was assessed by calculating the limit of quantification (LOQ), and the results indicated that the calculated LOQ values for VM in both chip and ground biomass and FC in chip biomass are lower than the minimum reference values used during model development, signifying a high level of sensitivity. However, for the remaining parameters, the LOQ values surpass the established minimum reference value, suggesting the model could not predict the future samples with reference value lower than the calibration range. Nevertheless, enhancing the models by incorporation an ample number of representative biomass samples and consistently validating the models with unknown samples is imperative to ensure accurate predictions.

Climate change impacts threaten sustainable development efforts. The magnitude of the impacts, however, varies with socio-ecological characteristics of locations. This is the reason there is con-sensus on the necessity for climate change adaptive capacity building that is country driven, based on and responsive to local needs. However, information on context specific capacity building needs in developing countries is not readily available. The objective of this study was to establish location specific awareness, training, educational, research and technology capacity building needs for climate change adaptation among small-holder farmers in Uganda. Structured interviews were undertaken with 465 households from five agro-ecological zones selected based on the level of vulnerability of agricultural systems to the main climate variation and change hazards. Results reveal substantial capacity building needs in all the zones. Majority of the farmers needed capacity building for interventions on soil water conservation practices for adapting to drought and un-predictable rainfall. For all zones, education, research, and technology were perceived as key needs. However, the needs varied among zones. These results demonstrate the importance of context specificity in adaptation efforts. The study provides agro-ecological and social system specific in-formation for climate change adaptation planning and policy interventions for effective capacity building

Rice farming households having limited capital do various combinations of the capital to get diversified livelihoods in continuing their lives. The purpose of this study was to analyze the effect of the household capital of rice farmers on livelihood diversification in Indramayu District. Survey method with data sources from 214 rice farming households taken by proportional simple random sampling technique. Data analysis used the partial least square method. The results found that the household capital of rice farmers has a positive and significant effect on the livelihood diversification. Government policy recommendations were determined in order of priority are physical capital with the help of agricultural tools and machinery, natural capital by anticipating climate change, financial capital by increasing support for capital sources, social capital by social networks, and human capital by improving farming skills.

In this study, by using Least Square Method, the dataset for the Gross Domestic Product per capita is modeled as a function satisfying the fractional differential equation. The function itself is assumed to be the finite summation of its previous values and the derivatives with unknown coefficients. Then, the prediction for the upcoming years is done by having an approach dividing the dataset into 4 regions corresponding to four different tasks. The mathematical model of the Gross Domestic Product (GDP) per capita of the countries (and union) which are Brazil, China, European Union (EU), India, Italy, Japan, UK, the USA, Spain, and Turkey is constructed with a new methodology called as the deep assessment method which comes from the expressing an arbitrary function modeling the dataset as the finite summation of its previous values and the derivatives with unknown coefficient. The method uses the fractional calculus properties combining with Least Square Method and is compared to Long short-term memory (LSTM) algorithm which is a special type of neural network used for time sequences in general.

The fixed and random effects models are popular methods for identifying the causal effects of treatment. First, I introduce the two-way fixed effects model and show a class of the estimators for the random effects models. Misidentification may occur in these models due to the treatment effect heterogeneity. Then, I propose the differential error component models to address such problems while the models exclude the fixed effects of units and time periods and consider the random effects of units and time periods. Meanwhile, I present a simulation of a staggered design and revisit the application in (De Chaisemartin and D'Haultfoeuille, 2020). The results demonstrate that one additional newspaper increases the 0.31% average effect of the presidential turnout and the 1.05% average effect when the newspapers’ changes are non-negative, where the proposed estimators are robust and efficient.

Nepal, primarily an agricultural country, heavily relies on agricultural residue and fuelwood for daily energy requirements. In 2022, total energy consumption was 640 PJ, with traditional sources accounting for 64.17%, and fuelwood comprising 58.53% of total fuel consumption. The estimated potential supply of agricultural residue is 26 million tonnes, yielding about 442 million GJ of energy in 2021. Biomass trading often emphasizes volume or weight, necessitating a rapid and non-destructive assessment of energy properties for mutual benefit, aiding in the identification, management, and utilization of biomass sources. In this study, 200 biomass samples were collected in two batches (126 and 74 samples) from various locations in Nepal. Using Partial Least Squares Regression (PLSR), a model was developed correlating higher heating value (HHV) from a bomb calorimeter and spectral data from Fourier Transform near-infrared spectroscopy (FT-NIRS) (3595 – 12,489 cm-1) sensor. PLSR models incorporated raw spectra, eight preprocessing techniques, the multi-preprocessing five-range method, and a genetic algorithm. Outliers in the first batch were identified, and the first batch divided into an 80% calibration set and a 20% validation set, while the second batch was designated as an unknown sample set. The optimum PLSR model, utilizing first derivative preprocessing, improved accuracy by 6.77%, with coefficients of determination in the calibration set, validation set, and unknown set as 0.9694, 0.9578, and 0.8089, respectively. Root mean square errors were 132.4790 J/g, 189.4800 J/g, and 360.8845 J/g for the calibration set (RMSEC), validation set (RMSEP), and unknown set (RMSEUN), respectively. The prediction to deviation ratio (RPD) for the validation set and unknown set was 4.9 and 2.4, respectively. The cross validation model of combined sample data of every sets showed the R2CV of 0.95 and RPDCV of 4.6 indicating the model could serve as a reliable and swift non-destructive alternative for evaluating biomass HHV using NIRS and ready for updating for industrial use. However, incorporating a larger number of representative samples is crucial to enhance accuracy and develop a more comprehensive global model for predicting biomass HHV.

This paper shows the firsts results of a methodological approach in which Remote Sensing technologies (Satellite imagery and LiDAR) combined with Least Cost Path algorithm on GIS are being tested like tools to overcome locally-based interpretations about populational patterns and communication routes during Flavian municipality in the Comarca de El Condado region. The identification of fourteen hypothetical communication routes between already known archaeological sites thanks to a recent archaeological prospection, and the confirmation of three sections of them and a new archaeological site demonstrate that the combined use of the aforementioned technics has a very useful to delve more deeply, more widely and at a variety of different scales into the relation between the twelve known/selected Early Roman Empire sites and the surrounding landscape in the right bank of Guadalén River. It is expected that, in next phases, this methodological approach will allow us to go deeper in analysing and understanding spatial distribution, exploitation, and interrelation between different Early Roman Empire sites, helping us to fill the aforementioned gaps in our understanding of the archaeological continuum in the region reducing time and costs.

Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. Spatial monitoring of fluke infection at the small basin analysis scale is important because this can enable analysis at the level of the spatial factors involved and influencing infections. The spatial mathematical model was weighted by the nine spatial factors by dividing the analysis into two levels. 1) sub-basin boundary level analyzed with ordinary least square (OLS) model used to analyze spatial factors of liver fluke aimed at analyzing spatial factors related to human liver fluke infection according to sub-basin boundaries, and 2) infection risk positional analysis level with machine learning-based forest classification and regression (FCR) and displaying predictive results of infection risk locations along stream lines. The analysis results show 4 prototype models that import different independent variable factors. The results show that Model-1 and Model-2 give the most AUC = 0.964 and the variables that influence infection risk the most were distance to stream lines, and distance to water bodies, NDMI and NDVI factors rarely affect accuracy. This FCR machine learning application approach can be applied to the analysis of infection risk areas at the sub-basin level, but independent variables must be screened with a preliminary mathematical model weighted to the spatial units in order to obtain the most accurate predictions.

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes for BPA concentration (R2 = 0:996; Standard deviation to prediction error's root mean square ratio (RPD) = 3.41; and a Pearson's r value of 0.998). With these values of R2 and Pearson's r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the methods were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average (MA) colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher-order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.

Empirical models for intensive or extensive margins of trade that relate measures of exports to firm characteristics are usually estimated by variants of (generalized) linear models. Usually, the firm characteristics that explain these export margins enter the empirical model in linear form, sometimes augmented by quadratic terms or higher order polynomials, or interaction terms, to take care or test for non-linear relationships. If these non-linear relationships do matter and if they are ignored in the specification of the empirical model this leads to biased results. Researchers, however, can never be sure that all possible non-linear relationships are taken care of in their chosen specifications. This note uses for the first time the Kernel-Regularized Least Squares (KRLS) estimator to deal with this issue in empirical models for margins of exports. KRLS is a machine learning method that learns the functional form from the data. Empirical examples show that it is easy to apply and works well. Therefore, it is considered as a useful addition to the box of tools of empirical trade economists.

In a dual-inertia system based on permanent magnet synchronous motor (PMSM), the elastic transmission mechanism, such as reducer and coupling, is prone to mechanical resonance, which reduces the precision of servo system transmission. Model prediction and introduction of state feedback are effective methods to suppress mechanical resonance, but the control parameters de-pend on the mechanical parameters of the system. Therefore, mechanical parameter identification is very necessary. In this paper, a forgetting factor recursive least square method is used to identify the mechanical parameters of the dual-inertia elastic system. Firstly, the mathematical model of the dual-inertia system is established and reasonably simplified. Then, the discrete function of the identification algorithm is constructed, and the motor speed and electromagnetic torque are sam-pled to achieve the on-line identification of the main mechanical parameters of the dual-inertia system, and the influencing factors of the identification accuracy are analyzed. Finally, simulations and experiments are carried out to verify the effectiveness of the algorithm.

In wireless sensor networks (WSNs) deployed in smart agriculture systems, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a novel method for localizing unknown sensor nodes in WSN-based smart agriculture systems using range measurements. The proposed method utilizes the Levenberg-Marquardt optimization algorithm to solve a nonlinear least squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the approach, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes, effectively enhancing the overall performance of the WSN-based smart agriculture system. Furthermore, the method demonstrates robustness against measurement noise and scalability for large-scale networks. The practical implications of this research extend beyond smart agriculture systems, as the proposed method can be applied to various WSN applications where precise node localization is essential.

Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform path characterizes this error. In response to this challenge, we propose a linear, parameter-free model, which uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established from the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation with substantial pose differences and efficient pose optimization in larger circuits.

Due to an urgent need for reducing the fast progressing climate changes, a rapid, standardized and replicable solution for the infrastructure restructuring of heat demand coverage of buildings at district and city level must be developed. As more and more communities in Europe, especially in Germany, are obligated to have plans for coverage of heat demand, city planners, energy agencies seek a tool, which will help them to design the first drafts of heating network routes, which can supply whole districts and cities with renewable energy. ArcGIS Pro Tools like Least-Cost-Path-Analysis (LCPA) and Closest Facility (CF) allow to find the shortest and “cheapest” way between the heat source and heat consumers in the analyzed areas. Starting from the community level, through the district, county, voivodeship and whole country levels, the replication of the methods for generating district heating (DH) network infrastructure can be achieved. The proposed LCPA and CF based methods help to design the most suitable and efficient DH networks in the analyzed areas. As only few open-source data input, like street networks and building footprints are needed, the methods can be implemented in all communities of the country of Poland. In this paper one example of one community of Wroclaw county, called Siechnice and surroundings is presented.

Regression models are mostly used in all fields of sciences for modelling the relationship between a dependent variable and independent variable(s). The least square method is often used to estimate the parameters in a linear model because it is the best linear unbiased estimator. These estimates can only be reliable if the assumption of normality is satisfied. In some cases the dependent variable might be bimodal and shows a non-linear relationship with the independent variable(s). In this case, a non-linear model should be used. In non-linear model, the standard errors are often obtained by linearizing the nonlinear function around the parameter, assuming central limit theorem. After the linearization, the least square parameter estimates are obtained. It should be noted that the error of the non-linear model is different from that of the transformed linear model. Thus, there is a need to transform back to the original non-linear model. In this note, a novel non-linear function was developed into a non-linear regression model, called Normal-Power model. The least square method was used to estimate the parameter of the transformed model. Its usefulness in regression model was demonstrated using real data of Nigeria Economy-Tourism model.

Reliable access to electricity has become a necessity worldwide, and without it, no country can operate health systems and achieve sustainable development goals (SDGs). However, while developed countries have been successfully electrified for many decades, most developing countries have fallen short, contributing to significant impacts, particularly the health sector. The present study explores the relationship between electrification and health vulnerability in developing countries between 1990 and 2020 using the Feasible Generalized Least Square (FGLS) approach. The varied electrification across countries motivates the use of non-linear associations between electrification and health vulnerability. We demonstrate that electrification and health vulnerability have an inverted U-shaped association. Further analysis indicates that most developing countries have achieved a mature level of an inverted U-shaped curve, showing a reduction in health vulnerability with increasing electrification. There are few developing countries, mainly African countries, struggling to reach maturity. The results of this study indicate the need for increased access to electrification, in combination with a reduction in the use of biomass and fossil fuel feedstocks, to mitigate health vulnerability in developing countries.

Spatial interpolation of meteorological data can have immense implications on risk management and climate change planning. Kriging with external drift (KED) is a spatial interpolation variant that uses auxiliary information in the estimation of target variable at unobserved locations. However, the traditional KED methods with linear trend functions may not be able to capture the complex and non-linear interdependence between target and auxiliary variables, which can lead to an inaccurate estimation. In this work, a novel KED method using least squares support vector regression (LSSVR) is proposed. This machine learning algorithm is employed to construct trend functions regardless of the type of variable interrelations being considered. To evaluate the efficiency of the proposed method (KED with LSSVR) relative to the traditional method (KED with a linear trend function), a systematic simulation study for estimating the monthly means temperature and pressure in Thailand in 2017 was conducted. The KED with LSSVR is shown to have superior performance over the KED with the linear trend function.

To achieve the near zero emission of wastewater in the flue gas desulfurization (FGD) system in coal-fired power plant and better utilize the exhaust heat from flue gas, a feasible technology of spraying FGD wastewater in the flue duct for evaporation is discussed in the present study. A full-scale influencing factor investigation on the wastewater droplet evaporation performance is established under the Eulerian-Lagrangian model numerically. The dominant factors, including the characters of wastewater droplets, flue gas and the spray nozzles were analyzed under different conditions, respectively. Considering the multiple factors and conditions in the process, a Least-Square support vector machine (LSSVM) model is introduced to predict the evaporation rate based on the numerical results. Conclusions are made that the flue gas temperature and droplet diameter are of great importance in the evaporation process. The spray direction of droplet parallel with the flue gas flow direction is profitable for the dispersion of droplet, resulting the maximal evaporation rate. A double-nozzle arrangement optimized with relatively small flow rate is recommended. The LSSVM model can accurately predict the evaporation rate using the numerical results with different conditions.

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (2,4-DNT) with concentrations from 0% to 20% w/w were investigated using three types of soils: bentonite, synthetic soil, and natural soil. A Partial least squares regression model was generated for predicting 2,4-DNT concentrations. To increase its selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as PETN, RDX, and TNT and non-explosives such as benzoic acid and ibuprofen. For detection, mixes of different explosives in soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with those of soils recorded in a database to identify the most similar soils based on QCL spectroscopy. Next, a Classical Least Squares preprocessing (Pre-CLS) was applied to soils spectra selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was then used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A Random Forest model worked best with 0.997 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. Maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture.

This paper presents three approaches to estimating battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted with the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels; whereas both in the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

The impacts of COVID-19 (Novel Coronavirus) epidemic cannot have been more severe, with the globe experiencing both economic and health crises. This study examines the trends and correlations in number of COVID-19-related deaths and number of COVID-19-infected patients in all 37 regions of Tamil Nadu state, India, in the month of August 2020 on the basis of panel regression model,

In today’s digital media data communication over the internet increasing day by day. Therefore the data security becomes the most important issue over the internet. With the increase of data transmission, the number of intruders also increases. That’s the reason it is needed to transmit the data over the internet very securely. Steganography is a popular method in this field. This method hides the secret data with a cover medium in a way so that the intruders cannot predict the existence of the data. Here a steganography method is proposed which uses a video file as a cover medium. This method has five main steps. First, convert the video file into video frames. Then a particular frame is selected for embedded the secret data. Second, the Least Significant Bit (LSB) Coding technique is used with the double key security technique. Third, an 8 characters password verification process. Fourth, reverse the encrypted video. Fifth, signature verification process to verify the encryption and decryption process. These five steps are followed by both the encrypting and decrypting processes.

This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

In this study, we obtained results for the computation of eigen-pairs, singular value decomposition, pseudo-inverse, and the least square problem for elliptic quaternion matrices. Moreover, we established algorithms based on these results and provided illustrative numerical experiments to substantiate the accuracy of our conclusions. In the experiments, it was observed that the p-value in the algebra of elliptic quaternions and elliptic numbers directly affects the performance of the problem under consideration. Selecting the optimal p-value for problem-solving and the elliptic behavior of many physical systems make this number system advantageous in applied sciences.

The design of thin-walled structures is commonly based on the solutions of linear boundary-valued problems, formulated within well-developed theories for elastic plates and shells. However, in modern appliances, especially in MEMS design, it is necessary to take into account non-linear mechanical effects that become decisive for flexible elements. Among substantial non-linear effects, that significantly change the deformation properties of thin plates, are the effects of residual stresses caused by incompatibility of deformations, which inevitably arise during the manufacture of ultra-thin elements. The development of new methods of mathematical modeling of residual stresses and incompatible finite deformations in plates is the subject of the paper. To this end the local unloading hypothesis is used. It makes possible to define smooth fields of local deformations (inverse implant field) for the mathematical formalization of incompatibility. The main outcomes are field equations, natural boundary conditions and conservation laws, derived from least action principle and variational symmetries with account of implant field. The derivations were carried out in the framework of elasticity theory for simple materials and, over and above, within the Cosserat’s theory of a two-dimensional continuum. As illustrative examples the distributions of incompatible deformations in a circular plate are considered.

The ultimate analysis parameters including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass was rarely found to be predicted by nondestructive tests until to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near infrared (NIR) raw spectra of non-wood and wood samples from fast growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5−range, and MP 3−range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples was non-wood samples of 65-67% and wood samples was 33-35%, and the model performance were evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2P), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation values (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA−PLSR, yielding R2P, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR−5 range method with R2P of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR−3 range method, resulting in R2P, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of H model provided R2P, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N, exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for robust model but also they must provide their trend line characteristics in the scatter plot i.e. correlation coefficient (R), slope and intercept (same slope and slope approached to 1 and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope and intercept to obtain the better optimized model were studied. The results show that the different species affected model performance of each parameter prediction in a different manner and by scatter plot analysis which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time of the effect is studied by the principle of scatter plot.

Empirical equations representing, interpolating and smoothing groups of measurement results by regression methods are in widespread use in metrology and other fields of science and engineering. Standard equations for the propagation of measurement uncertainties to values computed from such equations are available but suffer from a lack of general acceptance and are only infrequently applied in practice. One reason for the slow uptake is the lack of clear methods to account for systematic error. In this paper, uncertainty propagation equations in terms of covariance matrices are generalized to allow for systematic errors in least-squares regression, and effects of the resulting uncertainties are investigated analytically. A stochastic ensemble model of systematic error is proposed for the computation of the non-diagonal elements of the weight matrix of the Generalized Least-Squares method (GLS) from the measurement uncertainty. A GLS projector formalism is described which separates the effects of measurement scatter on values calculated by the equation from those on the related “residuals”, i.e., the residual errors in the fitted data. The same projectors act similarly on the associated uncertainties and covariance matrices and permit the effects of systematic errors on the simulation covariance to be quantified. It is demonstrated that, in order to include systematic errors in the uncertainty estimates of GLS, covariance matrices may be substituted by novel, specifically defined dispersion matrices that are not specified yet by the GUM[1]. Systematic measurement errors may be estimated from various sources; a particularly easy way suggested here involves analyzing the structure in the regression residuals. It is demonstrated that GLS projectors exhibit inherent features of “error blindness” also with respect to systematic errors. [1] GUM: Guide to the Expression of Uncertainty in Measurement, http://www.bipm.org/en/publications/guides

The models of partial least squares (PLS) regression and artificial neural network (ANN) for evaluation of texture properties of cooked germinated brown rice (GBR) using the Fourier transform near infrared (NIR) spectra of uncooked whole grain combined with data separation methods, spectral pretreatment methods were investigated in this study. The ANN was outperformed in evaluation of hardness by back extrusion test of cooked GBR using the smoothing combined with standard normal variate pretreated NIR spectra in the range of 12,500-4,000 cm−1 of 188 whole grain samples where the calibration sample set was separated from prediction set by Kennard-Stone method where the best ANN model for hardness from hidden layers of 25 and 8 iteration time provided R2, r2, RMSEC, RMSEP, Bias and RPD of 0.9987, 0.9447, 0.1021 N, 0.7699 N, 0.0216 N and 4.3 respectively. The PLS regression of 64 sample KDML GBR group and 64 sample various variety GBR group, provided models for the hardness of the former and the toughness of the latter which developed by using 7506−5446.3, 4605.4−4242.9 cm−1 which included the amylose vibration band of 6834 cm−1 while toughness model was from 9403.8-6094.3 cm−1 where included 6834 and 8316 cm−1 vibration bands of amylose which influenced the texture of cooked rice. The PLS regression models for hardness and toughness were with the r2 of 0.85 and 0.82, and the RPD of 2.9 and 2.4, respectively. The ANN model for hardness of cooked GBR should be implemented to the practical use in GBR production factories for the quality assurance and further updating using more samples and several brands to obtain the robust models.

In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O models from the mul-ti-preprocessing PLSR 5 range method could be used for most applications, including research, whereas the C and H models from GA-PLSR performance is fair and applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving by 4.1839%, 8.1842%, 3.7587%, and 35.9404%, respec-tively. Therefore, it can be considered a reliable and non-destructive alternative method for rap-idly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for a robust application.

Background: The infant mortality rate (IMR) is an important reflection of the well-being of infants and the overall health of the population. This study aims to examine the macroeconomic (ME), sociodemographic (SD), and health status and resources (HSR) effects on IMR, as well as how they may interact with each other. Methods: A retrospective time-series study using yearly data for Oman from 1980 to 2022. Partial Least Squares-Structural Equation Modelling (PLS-SEM) was utilized to develop the exploratory model of the determinants of IMR. Results: The model indicates that HSR determinants directly but negatively affect IMR (= -0.617, p<0.001). SD directly and positively affects IMR (= 0.447, p<0.001). ME only indirectly affects IMR (=-0.854, p<0.001). ME determinants also exert some direct influences on both HSR (= 0.722, p<0.001) and SD (= -0.916, p<0.001) determinants. Conclusions: These findings indicate that an integrated policy that addresses socioeconomic and health-related factors and the overall ME environment is necessary for the health and well-being of the children and the population overall in Oman.

In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence asserts physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory generation algorithms: sinusoidal function and Pontryagin's based generation algorithm. This thesis aims to simulate model-following and deterministic artificial intelligence methods using sinusoidal and Pontryagin's methods and compare their performance difference when following challenging step function slew maneuver.

Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, adaptive and learning methods are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a method in which the controller is designed to adapt the system with parameters which vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model & measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. Deterministic artificial intelligence, a learning-based approach that uses the process dynamics defined by physics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive & learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output & the reference trajectory and the standard deviation of the error. The first order-hold method of discretization and surprisingly large sample time of seven tenths of a second yields over sixty percent improvement over the results presented in the prequel literature.

As a result of rapid population growth, an exponentially growing human population, and industrial expansion, it has become increasingly difficult to manage municipal solid wastes throughout the world. Decentralized waste management systems have created difficult situations in developing countries such as Malaysia. Wastes generated in the country, due to various cultural, social, and religious activities, organic and contributing to environmental pollution (air, water, and soil) and human health troubles. A questionnaire survey was participated by 220 construction professionals in Malaysia using structured and semi-structured methods. The framework was assessed using A partial least square structural equation modeling (PLS-SEM) to target sustainable development goals (SDG). Statistical analysis results indicate a significant effect between SCW management, since(r(270)=.687, P<0.001). Improving factors has strong relationship with SCW management, since(r(270)=.723, P<0.001). The mediation results also suggested a significant indirect positive effect of improving factors drivers on SCW management through policy-related factors sinceβ=0.688, t=8.254, P<0.001, 95% CI for β=0.536,0.866. Finally, policy-related factors construct has a strong relationship with SCWM) management, since(r(270)=.811, P<0.001) With the R Square of 0.787 and 0.785. The developed framework can improve construction waste management in the construction industry and enhance construction waste management to achieve global sustainable development goals. The findings show that one of the most critical issues of enhancing profitability is using preventive policies to reduce construction waste. This study could guide construction industry stakeholders in identifying the different waste management features during a building project's construction and design stage

The aim of this work is to introduce new ways to model the I-V characteristic of a PV cell or PV module using straight lines and Bézier curves. This is a complete novel approach, Bézier curves being previously used mainly for computer graphics. The I-V characteristic is divided in three sections, modeled with lines and a quadratic Bézier curve in the first case and with three cubic Bézier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: Voc (the open circuit voltage), Isc (the short circuit current), Vmp (the maximum power corresponding voltage) and Imp (the maximum power corresponding current) and the parasitic resistances Rsh0 (shunt resistance at Isc) and Rs0 (series resistance at Voc). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bézier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.

Keywords: Structural Equation Modeling (SEM); economic and social preferences; consumer choices; econometric model; Partial Least Square (PLS).

This paper presents Double Database Decomposition Framework, which integrates vertical and horizontal table decompositions to minimize data leakage due to intrusions. Vertical decompositions segregate sensitive attributes, while horizontal ones partition data based on user access patterns. In this way, the framework ensures that users access only the data necessary for their operations, adhering to the principle of least privilege. This double decomposition approach improves the robustness of the original database against impersonation attacks and limits the blast radius of potential intrusions. Furthermore, the framework significantly mitigates the risks associated with data breaches by confining unauthorized access to specific data subsets and restricting the exposure of sensitive information. Performance analysis highlights the trade-offs between robustness and overhead offered by distinct allocation strategies of the output of the decompositions to, among other, physical machines, virtual machines, and containers, to balance security and resource efficiency. We present a case study in a healthcare environment that confirms both the effectiveness of the framework and its applicability in complex systems where data security is paramount. By integrating advanced security measures and optimising data access, the framework results in a scalable and adaptable solution for enhancing database security and performance in various domains.

The paper presents the development of cheap and selective Paper-based Analytical Devices (PADs) for Pd(II) determination. The PADs were obtained with an azoic ligand, (2-(tetrazolylazo)-1,8 dihydroxy naphthalene-3,6,-disulphonic acid), termed TazoC, and filter paper as the substrate. The orange TazoC-PADs interact quickly with Pd(II) solutions by forming a complex purple-blue-colored already at a very acidic pH (lower than 2). The dye complexes no other metal ions at such an acidic media, making TazoC-PADs highly selective to Pd(II) detection. Besides, at higher pH values, other cations, for example, Cu(II) and Ni(II), can interact with TazoC through the formation of stable and pink-magenta-colored complexes; however, it is possible to quantify Pd(II) also in the presence of other cations using a multivariate approach. Indeed, by applying Partial Least Square regression (PLS), the spectra of the TazoC-PADs were related to the Pd(II) concentrations both when present alone in solution and also in the presence of Cu(II) and Ni(II). The PLS models correctly predicted Pd(II) concentrations in unknown samples and tap water spiked with the metal cation.

Complex blade responses such as a rotating stall or simultaneous resonances are common in modern engines and their observation can be a challenge even for state-of-the-art tip-timing systems and trained operators. This paper analyses forced vibrations of axial compressor blades, measured during the bench tests of the SO-3 turbojet. In relation to earlier studies conducted in ITWL with a small number of sensors, a multichannel tip-timing system let us observe simultaneous responses or higher-order modes. To find possible symptoms of a failure, blade responses in a healthy and unhealthy engine configuration with an inlet blocker were studied. The used analysis methods covered all-blade spectrum and the circumferential fitting of blade deflections to the harmonic oscillator model. The proposed modal solver can track the vibration frequency and adjust the engine order on the fly. That way, synchronous and asynchronous vibrations are observed and analysed together with an extended variant of least squares. The proposed approach helps to avoid common mistakes and saves a lot of work related to configuring the conventional solver.

Surfactants based on polyfluoroalky ethers are commonly used in fire-fighting foams on airport platforms, including for training sessions. Because of their persistence into the environment, their toxicity and their bioaccumulation, abnormal amounts can be found in ground and surface water following operations of airport platforms. As many other anthropogenic organic compounds, some concerns raised about their biodegradation. That is why the OECD 301 F protocol was implemented to appreciate the oxygen consumption during the biodegradation of a commercial fire-fighting foam. However, a Raman spectroscopic monitoring of the process was also attached to this experimental procedure to evaluate to what extent a polyfluoroalkyl ether disappeared from the environmental matrix. The relevance of our approach is to use chemometrics, including the Principal Component Analysis (PCA) and the Partial Least Square (PLS), in order to monitor the kinetics of the biodegradation reaction of one fire-fighting foam, Tridol S3B, containing a polyfluoroalkyl ether. This study provided a better appreciation of the partial biodegradation of some polyfluoroalkyl ethers by coupling Raman spectroscopy and chemometrics. This will ultimately facilitates the design of a future purification and remediation devices for the airport platforms.

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photo-acoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3) and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a MIR (mid-infrared) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra is a common challenge for gas spectroscopy. To mitigate this, we use a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts-per-million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here in this proof-of-concept experiment we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3 and CH4 and with an absolute sensitivity of approximately 300(±50) ppm/V, 50(±5) ppm/V and 5(±2) ppm/V for water, ammonia and methane, respectively. Thus, demonstrating that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

Reverse-order laws for generalized inverses of matrix products is a classic object of study in the theory of generalized inverses. One of the well-known reverse-order laws for a matrix product AB is (AB)^(i,...,j) = B^(i,...,j)A^(i,...,j), where (·)^i,...,j denotes an {i,...,j}-generalized inverse of matrix. Because {i,...,j}-generalized inverse of a general matrix is not necessarily unique, the relationships between both sides of the reverse-order law can be divided into four situations for consideration. In this article, we first introduce a linear mixed model y = ABβ + Aγ + ε, present two least-squares methodologies to estimate the fixed parameter vector  in the model, and describe the connections between the two least-squares estimators and the reverse-order laws for generalized inverses of the matrix product AB. We then prepare some valued matrix analysis tools, including a general theory on linear or nonlinear matrix identities, a group of expansion formulas for calculating ranks of block matrices, two groups of explicit formulas for calculating the maximum and minimum ranks of B^(i,...,j)A^(i,...,j), as well as necessary and sufficient conditions for B^(i,...,j)A^(i,...,j) to be invariant with respect to the choice of A^(i,...,j) and B^(i,...,j). We then present a unied approach to the 512 set inclusion problems {(AB)^(i,...,j) ⊇ {B^(i,...,j)A^(i,...,j)}for the eight commonly-used types of generalized inverses of A, B, and AB using the block matrix representation method (BMRM), matrix equation method (MEM), and matrix rank method (MRM), where {(·)^(i,...,j)} denotes the collection of all {i,...,j}-generalized inverse of a matrix.

Currently, there are two approaches to the foundations of thermodynamics. One, associated with the mechanistical Clausius-Boltzmann tradition, is favored by the physics community. The other, associated with the post-mechanical Carnot tradition, is favored by the engineering community. The bold hypothesis is that the conceptual foundation of engineering thermodynamics is the more comprehensive. Therefore, contrary to the dominant consensus, engineering thermodynamics (ET) represents the true foundation of thermodynamics. The foundational issue is crucial to a number of unresolved current and historical issues in thermodynamic theory and practice. ET formally explains the limited successes of the ‘rational mechanical’ approaches as idealizing special cases. Thermodynamic phenomena are uniquely dissymmetric and can never be completely understood in terms of symmetry-based mechanical concepts. Consequently, ET understands thermodynamic phenomena in new way, in terms of the post-mechanical formulation of action. The ET concept of action and the action framework trace back to Maupertuis’s Principle of Least Action, both clarified in the engineering worldview research program of Lazare and Sadi Carnot. Despite the intervening Lagrangian ‘mechanical idealization of action’, the original dualistic, indeterminate engineering understanding of action, somewhat unexpectedly, re-emerged in Planck’s quantum of action. The link between engineering thermodynamics and quantum theory is not spurious and each of our current formulations helps us develop our understanding of the other. Both the ET and quantum theory understandings of thermodynamic phenomena, as essentially dissymmetric (viz. embracing complementary), entail that there must be an irreducible, cumulative historical, qualitatively emergent, aspect of reality.

Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested by generalizing the uniform shrinkage of ridge regression to the non-uniform shrinkage, which was shown to perform well under sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (the first version published on 2023-12-07 at https://cran.r-project.org/package=g.ridge) that implements both the ridge estimator and generalized ridge estimators. The package equips with the generalized cross-validation for automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with the normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on “glmnet”, and hence, it can be the most recommended estimator under sparse and high-dimensional models. We demonstrate the package using the intracerebral hemorrhage data.

: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

The main issue in this work is to study limit functions necessary for the reliability assessment of the structural steel with the use of the relative entropy apparatus. This would be done using a few different mathematical theories relevant to this relative entropy, namely those proposed by Bhattacharyya, Kullback-Leibler, Jeffreys, and Hellinger. Probabilistic analysis in the presence of uncertainty in material characteristics would be delivered using three different numerical strategies – Monte-Carlo simulation, stochastic perturbation method as well as the semi-analytical approach. All these methods are based on the Weighted Least Squares Method approximations of the structural response functions versus the given uncertainty source and they would allow efficient determination of the first two probabilistic moments of the structural responses including stresses, displacements, and strains. The entire computational implementation would be delivered using the Finite Element Method system ABAQUS and computer algebra program MAPLE, where relative entropies, as well as polynomial response functions, would be determined. This study would demonstrate that the relative entropies may be efficiently used in reliability assessment close to the widely engaged First Order Reliability Method (FORM).

Delineating chilling and forcing periods is one of the challenging topics in understanding how temperatures drive the timing of budburst and bloom in fruit tree species. Here, we investigated this question on olive trees, using flowering data collected over six years on 331 cultivars in the worldwide collection of Marrakech, Morocco. Using a Partial Least Squares approach on a long-term phenology (29 years) of ‘Picholine Marocaine’ cultivar, we showed that the relevance of delineating the chilling and forcing periods depends more on the variability of inter-annual temperatures than on the long-term datasets. In fact, chilling and forcing periods are similar between those delineated by using datasets of 29 years and those of only 6 years (2014–2019). We demonstrated that the variability of inter-annual temperatures is the main factor explaining this pattern. We then used the datasets of six years to assess the chill and heat requirements of 285 cultivars. We classified Mediterranean olive cultivars into four groups according to their chill requirements. Our results, using the Kriging interpolation method, indicated that flowering dates of most of these cultivars (92%) were governed by both chilling and forcing temperatures. Our investigations provided first insights to select adapted cultivars to global warming.

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure-activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared 15 SCs and their derivatives (tetrahydrocannabinol [THC], naphthoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.

The first detection of gypsum (CaSO4.2H2O) by the Mars Science Laboratory (MSL) rover Curiosity in the Gale Crater, Mars created a profound impact on planetary science and exploration. The unique capability of plasma spectroscopy involving in situ elemental analysis in extraterrestrial environments, suggesting the presence of water in the red planet based on phase characterization and providing a clue to Martian paleoclimate. The key to gypsum as an ideal paleoclimate proxy lies in its textural variants, and in this study terrestrial gypsum samples from varied locations and textural types have been analyzed by Laser Induced Breakdown Spectroscopy (LIBS) technique. Petrographic, sub-microscopic and powder X-ray diffraction characterizations confirm the presence of gypsum (hydrated calcium sulphate; CaSO4.2H2O), bassanite (semi-hydrated calcium sulphate; CaSO4.1/2H2O) and anhydrite (anhydrous calcium sulphate; CaSO4) along with accessory phases (quartz and jarosite). The principal component analysis of LIBS spectra from texturally varied gypsums can be differentiated from one another because of the chemical variability in their elemental concentrations. The concentration of gypsum is determined from the partial least-square regressions model. Rapid characterization of gypsum samples with LIBS is expected to work well in extraterrestrial environments.

The article analyzes the problems connected with ensuring the coordinated operation of slipway drives that arise during the launch of a ship. The dynamic model of load of the electric drive of the ship's cart is obtained taking into account the peculiarities of the construction of the ship-lifting complex, which allows to analyze the influence of external factors and random influences during the entire process of launching the ship. A linearized mathematical model of the dynamics of a complex vessel movement in the process of descent in the space of states is developed, which allows to identify the mode of operation of the multi-drive system, taking into account its structure. The analysis of efficiency of application of recurrent methods of identification (stochastic approximation and least squares) of the parameters of the linearized model in the space of states is carried out. A decision support system has been developed in the automated system of operational control by the module for estimating the situation and the control synthesis to ensure a coherent motion of a complex ship-carts object in a two-phase environment.

Previous research has indicated that ultra-high pressure (UHP) technology is useful in speeding up the extraction process of cold brew (CB) coffee. However, the impact of different degrees of roasting on UHP CB coffee is still unclear. Therefore, this study aimed to investigate the effects of three different degrees of roasting (light, medium, and dark) on the physicochemical and flavor characteristics of UHP CB coffee. The results of this study showed that most physicochemical characteristics of UHP coffee were similar to those of conventional CB coffee, regardless of the degree of roasting. However, the majority of physicochemical characteristics, non-volatile components, and sensory evaluation significantly increased with an increase in roasting degree. The HS-SPME-GC/MS analysis showed that most volatile components, such as pyrazines, furans (except furfural), esters, pyrrole, pyridine, and most aldehydes, increased from light to dark roasting, and this was significantly evident in CB coffee compared with UHP. The content of volatile components in UHP coffee was significantly (P<0.05) higher than that in CB coffee at the same degree of roasting, especially with dark roasting. The combination of orthogonal partial least squares-discriminant analysis (OPLS-DA) and principal component analysis (PCA) indicated that the distance between coffee samples at different degrees of roasting under CB coffee was greater than that under UHP coffee. However, the distance between these two extraction methods at medium roasting was evidently closer than that at light and dark roasting. Moreover, 15 representative differential compounds, such as were Hazelnut pyrazine, linalool, butane-2,3-dione, 3- methylbutanal found by the calculation of odor-active values (OAVs) indicating the main contributing compounds to odor. Overall, the effect of roasting degrees on UHP coffee was less than that on CB coffee, which was consistent with the results of physicochemical characteristics, volatile components, and sensory evaluation.

Machine Learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the PLSR model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to be strongly impacting on Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations such as precipitation and wind speed, and the use of non-parametric ML techniques.

This manuscript compares deterministic artificial intelligence to model following control applied to DC motor control, including evaluation of the threshold computation rate to let unmanned underwater vehicles correctly follow the challenging discontinuous square wave command signal. The approaches presented in the main text are validated in MATLAB®, where the motor process is discretized at multiple step sizes, which is inversely proportional to the computation rate. The performance is evaluated by the error mean and standard deviation. With a large step size, discrete deterministic artificial intelligence shows a larger error mean than the model-following self-turning regulator approach (benchmark selection). However, the performance gets optimized with the step size decreased. The error mean is close to the continuous deterministic artificial intelligence when the step size is reduced to 0.2 seconds, which means that the computation rate and the sampling period restrict discrete deterministic artificial intelligence. In that case, the continuous deterministic artificial intelligence is the most feasible and reliable selection for future applications on unmanned underwater vehicles since it is superior to all the approaches with multiple computation rates.

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to use the differential scanning calorimetry (DSC) technique to detect adulterations of cold-pressed flaxseed oil with refined rapeseed oil (RP). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with a RP concentration of 5, 10, 20, 30, 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for h1. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: 1) classification models (Linear Discriminant Analysis, LDA Adaptive Regression Splines, MARS, Support Vector Machine, SVM, Artificial Neural Networks, ANNs); 2) regression models (Multiple Linear Regression, MLR, MARS, SVM, ANNs, PLS) and 3) a combined model of Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN&gt; SVM &gt; MARS was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R= 0.996), while other models showed goodness of fit as following MARS&gt; SVM&gt; MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) =0.986 and Q2 =0.973 were observed with the PLS model than OPLS-DA. These results demonstrate the usefulness of the DSC technique combined with chemometrics for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

We propose that, at equilibrium, statistically equal temperatures as mechanical torques are exerted on each kind of gas phase molecules as rates of translational action [@t=ʃmvds/dt=ʃmr2ωdϕ/dt=mv2, J]. These torques result from the impulsive density of resonant quantum fields with molecules, configuring the trajectories of gas molecules while balancing molecular pressure (p=NkT) against the density of field energy (J/m3). Gibbs energy fields contain no resonant quanta at zero Kelvin, with this measure of chemical potential diminishing in magnitude as translational action of the vapor molecules and quantum field energy increases with temperature. In illustration, we show how impulsive torques from quantum fields drive the reversible thermodynamics of Carnot’s heat-work engine cycle, sustain the decreasing atmospheric temperature gradient of increasing molecular entropy with altitude, support the translational action and field energy of vortical wind flow in anticyclones, frictionally warming the Earth’s surface while recycling greenhouse infrared absorption of surface radiation, generating electrical power from air flow in wind farms and destructive power in tropical cyclones. These cases all distinguish symmetrically between a causal field of impulsive quanta (Σhν) that energizes the action of matter and the resultant vis viva of molecular mechanics (mv2). The quanta of these different fields display mean wavelengths from 10-4 m to 1012 m, with mechanical advantages many orders of magnitude greater than the corresponding translational actions, though with mean quantum frequencies (v) similar to those of radial Brownian movement for independent particles (ω). These energy fields are also thermodynamically reversible reservoirs for heat, optimizing work processes on Earth and delaying the achievement of maximum entropy production from short wave solar radiation in conversion to outgoing long wave radiation to space.

This article presents six probability distributions from the Gamma family with three parameters, for the flood frequency analysis in hydrology. The choice of the Gamma family of statistical dis-tributions was driven by its frequent use in hydrology. In the Faculty of Hydrotechnics, the im-provement of the estimation of maximum flows and including the methodological bases for the realization of a regionalization study with the linear moments method with the corrected pa-rameters was researched, being an element of novelty. The linear moments method is better than MOM because it avoids the choice of skewness depending on the origin of the flows, practiced in Romania. The L-moments method conforms to the current trend for estimating the parameters of statistical distributions. Observed data from hydrometric stations are of relatively short length, so the statistical parameters that characterize them are of a sample that requires correction. The correction of the statistical parameters is proposed, using the method of least squares based on the inverse functions of the statistical distributions expressed with the frequency factor for L-moments. All the necessary elements for their use are presented like, quantile functions, the exact and ap-proximate relations for estimating parameters and frequency factors. A flood frequency analysis case study was carried out for the Ialomita river, to verify the proposed methodology. The per-formance of this distributions is evaluated using Kiling-Gupta and Nash-Sutcliff coefficients.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.