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Without fundamentally altering how humans generate and utilise energy, there is no effective strategy to safeguard the environment. The motivation behind this study was to analyse the effectiveness of renewable energy in addressing climate change, as it is one of the most pressing global issues. This study involved the analysis of panel data covering 138 nations over a 27 year period, from 1995 to 2021, making it the latest addition to the existing literature. We examined the extent of the impact of renewable energy on carbon dioxide over time using panel, linear, and non-linear regression approaches. The results of our analysis, revealed that the majority of countries with the exception of Canada, exhibited a downward trend, underscoring the potential of increasing renewable energy consumption as an effective method to reduce carbon dioxide emissions and combat climate change. Furthermore, to reduce emissions and combat climate change, it is advisable for nations with the highest carbon dioxide emissions to adopt and successfully transition to renewable energy sources. [ABSTRACT FROM AUTHOR]

This study investigates the impact mechanism of the government's strategic leadership (SL) and environmental optimisation (EO) on the improvement of regional innovation performance (RIP) under the effects of the regional innovation system coupling synergy degree (RISCSD). Based on the nonlinear threshold regression model, the coupling synergy model and entropy value method, the threshold regression analysis is conducted by calculating the RISCSD. The results indicate that: under the effect of the RISCSD, the SL, EO, and RIP have distinct single and double threshold effects. With the improvement of RISCSD, the influence of SL on RIP has changed from inhibition to promotion; while the promotion effect of EO on RIP shows an upward trend at first, and then slightly weakening. This paper provides relevant policy suggestions for local public administrations to enhance RIP. [ABSTRACT FROM AUTHOR]

Quantitatively predicting phenotypic variables using biomarkers is a challenging task for several reasons. First, the collected biological observations might be heterogeneous and correspond to different biological mechanisms. Second, the biomarkers used to predict the phenotype are potentially highly correlated since biological entities (genes, proteins, and metabolites) interact through unknown regulatory networks. In this paper, we present a novel approach designed to predict multivariate quantitative traits from biological data which address the 2 issues. The proposed model performs well on prediction but it is also fully parametric, with clusters of individuals and regulatory networks, which facilitates the downstream biological interpretation. [ABSTRACT FROM AUTHOR]

The bond behaviour between fibre-reinforced polymer (FRP) composites and concrete is a complex problem that is influenced by material properties, joint geometric configuration and the surrounding environment. The three fundamental bond performance indicators of concern are interfacial fracture energy, bond strength and debonding strain. This paper introduces a new semi-empirical model for predicting interfacial fracture energy (Gf) between externally bonded FRP and intact concrete in terms of the most influential material and geometric parameters, namely concrete's compressive strength and maximum aggregate size, stiffness of FRP composites and bond width and length of FRP composites relative to the dimensions of the concrete member. The prediction of Gf helped generate accurate predictions for the other two bond performance indicators. The present model was developed using non-linear regression analysis before validation using almost one-third of the total database consisting of 425 points, collected from credible publications. The accuracy of the present model of Gf outperformed those of well-known literature. The excellent agreement in trend behaviour of the present model with those reported in related literature works postulated further the model's validity. The estimation of debonding strain and bond strength in terms of fracture energy demonstrated superior accuracy over those provided by different relevant literature models. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R2 coefficient of determination of 0.92. [ABSTRACT FROM AUTHOR]

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%. [ABSTRACT FROM AUTHOR]

Growth modelling is of interest to scientists in various disciplines. In our article, we will collect 17 models designed for growth modelling, appraise these models and contribute to the discussion of their applicability. The merit of the paper lies in studying the convergence properties of nonlinear regression in selected models. Our studies will be performed mainly concerning the quality of the obtained estimates, which are closely related to the intrinsic curvature of the model according to Bates and Watts. This curvature determines the size of the linearization domains. Only if the initial solution is in this domain, then the convergence of the estimate in nonlinear regression is guaranteed. The primary goal is to design a methodology for selecting a growth model. We will demonstrate fruitfulness of our methodology on the weight measurements of 10 calves under 25 months of age from cowsheds in the village Záluží in the Czech Republic. Estimated parameters of growth curves, sizes of linearization domain, calculated residues and coefficients of determination indices will be the subject of discussion. [ABSTRACT FROM AUTHOR]

Previous studies on the co-combustion of sludge and coal have not effectively utilized the characteristics of the combustion process to predict thermal behavior. Therefore, focusing on these combustion process characteristics is essential to understanding and predicting thermal behavior during the co-combustion of sludge and coal. In this paper, we use thermogravimetric analysis to study the co-combustion of coal and sludge at different temperatures (300–460 °C, 460–530 °C, and 530–600 °C). Our findings reveal that the ignition improves, but the combustion worsens with more sludge. Then, we further employ curve extraction based on temperature and image segmentation to extract the DTG (weight loss rate) curves. We successfully predicted the DTG curves for different blends using nonlinear regression and curve extraction, achieving an excellent R2 of 99.7%. Moreover, the curve extraction method predicts DTG better than artificial neural networks for two samples in terms of R2 (99.7% vs. 99.1% and 99.7% vs. 94.9%), which guides the application of co-combusting coal and sludge. [ABSTRACT FROM AUTHOR]

Two major characteristics that can impact the frequency and severity of traffic crashes are average speed and speed variance. This paper investigated these characteristics on 49 multilane highway segments in Jordan and developed prediction models for three different conditions: free (no enforcement), camera, and police enforcement. Speed data were collected during off-peak periods with fair weather conditions. Multiple linear regressions and nonlinear regressions were used to develop speed variance and average speed models. For the free condition, speed variance mainly depends on the difference between design speed and speed limit (DS-SL), with the speed variance decreasing as the difference reduces, while the average speed was positively correlated with the design speed. Camera enforcement causes speed variance and average speed to be positively correlated and linearly dependent with the speed limit. A quadratic relationship was found between speed variance during police enforcement and DS-SL, while the average speed during police enforcement depended on the speed limit and design speed. These modeling results, along with analysis of the collected data, can help operating agencies and roadway designers determine how various enforcement strategies affect average speed and speed variance and better set speed limits on existing and future roadways to improve safety. [ABSTRACT FROM AUTHOR]

In challenging circumstances, the estimation performance of integrated navigation parameters for tightly coupled GNSS/SINS is impacted by outlier measurements. An effective solution that employs a novel iterative sigma-point structure with a modified robustness optimization approach for enhancing the error compensation effectiveness and robustness of filters utilized in GNSS challenge conditions is proposed in this paper. The proposed method modifies the CKF scheme by incorporating nonlinear regression and numerous iteration processes for ameliorating error compensation. Subsequently, a loss function and penalty mechanism are implemented to enhance the filter's robustness to outlier measurements. Furthermore, to fully incorporate valid information of the innovation and speed up the operation of the proposed method, the outlier measurement detection criteria are established to bypass the penalty mechanism against measurement weights in the absence of outliers in GNSS measurements. Field experiments demonstrate that the proposed method outperforms traditional methods in mitigating navigation errors, particularly when multipath errors and non-line-of-sight (NLOS) reception are increased. [ABSTRACT FROM AUTHOR]

The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)–least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion. [ABSTRACT FROM AUTHOR]

In this paper probabilistic models are developed for estimating the peak excess pore pressure ratio ( r u ) and peak shear strain ( γ max ), which control liquefaction triggering, in layered granular soils under earthquake loading. The models are developed using non-linear regression applied to a database of 167 352 results from one-dimensional, non-linear, effective stress, site response analyses. A pseudo-parametric regression strategy is adopted to account for the theoretical upper bound on r u at a value of 1. The models include the influence of variables that are specific to the individual layer, to the soil profile and to the ground motion, as well as layer-to-layer interaction and the relationship between r u and γ max . The variability around model predictions is decomposed, and the relative contributions of layer-, profile- and ground motion-specific parameters are evaluated. Epistemic uncertainty related to soil model selection, calibration and validation is also addressed. The total uncertainty around model predictions ranges from 0·5 to 1·6 in natural log units, with smaller values for scenarios that are of particular engineering interest (e.g. high r u ). Finally, the proposed models are used to estimate the probability of liquefaction triggering for case studies from the 2010–2011 Canterbury earthquake sequence. The results are shown to compare favourably with existing deterministic and probabilistic methods in terms of their ability to distinguish between cases where liquefaction was observed in both the Darfield and Christchurch earthquakes and those where no liquefaction was observed in either event. [ABSTRACT FROM AUTHOR]

This paper evaluates linear and nonlinear regression analysis to describe the empirical adsorption kinetics using pseudo-first-order (PFO) and pseudo-second-order (PSO) models. These models have been used to characterize the performance of adsorbents for environmental remediation and environmental modeling. Data were simulated using the PFO and PSO models with 1, 2, and 5% noise levels and fitted by nonlinear and linearized PFO and PSO equations. Nonlinear regression analysis provided rate constants and adsorption capacities with better accuracy than linearization. Besides the correlation coefficient, Chi-square and residual plot analysis helped choose the proper model to describe the adsorbent efficiency and validate the results. Both models and the NLR fitting were employed to reevaluate data obtained in our research group, including the adsorption of Hg(II) on thiol-modified vermiculite, glyphosate on soils rich in aluminum and iron oxides, phosphate on Fe(III) polyhydroxy cations modified montmorillonite, and paraquat on soil and vermiculite. While fitting the simulated data indicates an unequivocal and correct kinetic model, fitting the experimental data is not straightforward, suggesting mixed models rule the adsorption and that a large number of data points, especially at the initial steps of adsorption, provided by high throughput analysis, help to improve the kinetic modeling. [ABSTRACT FROM AUTHOR]

Flow-assisted corrosion occurs via increased dissolution and/or mechanical degradation of protective oxide formed on the surface of construction materials in direct contact with coolant liquids. In the present paper, this phenomenon is studied on carbon steel in an ammonia-ethanolamine-hydrazine electrolyte by in situ electrochemical impedance spectroscopy in conditions that closely simulate those that prevail in nuclear plant steam generators. Based on the obtained results, a quantitative kinetic model of the process is proposed and parameterized by nonlinear regression of experimental data to the respective transfer function. On the basis of the experimental and calculational results, it is concluded that flow-assisted corrosion of carbon steel is limited by oxide dissolution and cation ejection processes and the protective layer–coolant interface. Expressions for the film growth and corrosion release processes are proposed and successfully compared to operational data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a nonlinear regression model with exponentiated skew-elliptical errors distributed, which can be fitted to datasets with high levels of asymmetry and kurtosis. Maximum likelihood estimation procedures in finite samples are discussed and the information matrix is deduced. We carried out a diagnosis of the influence for the nonlinear model. To analyze the sensitivity of the maximum likelihood estimators of the model's parameters to small perturbations in distribution assumptions and parameter estimation, we studied the perturbation schemes, the case weight, and the explanatory and response variables of perturbations; we also carried out a residual analysis of the deviance components. Simulation studies were performed to assess some properties of the estimators, showing the good performance of the proposed estimation procedure in finite samples. Finally, an application to a real dataset is presented. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) are used to provide accurate position, navigation, and time (PNT) information to users in various sectors of our society including transportation. Augmentation systems such as differential GNSS (DGNSS), real-time kinematics (RTK), and Precise Point Positioning (PPP) improve the GNSS performance, and providing reliable measurements from its reference station is very crucial. To ensure safe and accurate PNT solutions, code and carrier measurements must be monitored for potential faults or a performance degrade. Although there exist numerous methods to model and monitor the measurements, research on the carrier phase measurements is not as extensive as the code measurements. This paper introduces a split of residuals into receiver noise and multipath components to customize their estimation according to their respective statistical properties. This study also proposes a method to use machine learning-based non-linear regression to effectively model and monitor potential faults in the GNSS measurements including the carrier phase. A training dataset is used to model the nominal quantities of GNSS measurement residuals, and inflation factors are applied to over-bound the fault-free residuals. These inflated residuals are coupled with uncertainty factors to compute thresholds for monitoring carrier phase residuals, and the effectiveness of the thresholds is validated with a test dataset by achieving the false alarm rate of 6.61 × 10 − 6 , slightly lower than the desired level of 10 − 5 . [ABSTRACT FROM AUTHOR]

This work presents a novel approach for the growth of potassium sulfate (K2SO4) from supersaturated solutions by reaction-extractive crystallization in a fluidized-bed crystallizer (FBC), in which K2SO4 is transformed into potassium chloride and sulfuric acid based on a reactive extraction-crystallization process using triisooctylamine (TOA) in n-octanol as the extraction system. This paper uses the moment transformation method to solve the population balance equations of K2SO4 in the extractive crystallization process, and the crystallization kinetic parameters are obtained by nonlinear regression. The kinetic equations of crystal nucleation, growth, and agglomeration rate in the crystallization process of potassium sulfate are determined, and experiments verify the reliability of the kinetic parameters. The results show that in a specific range, higher supersaturation and fluidization rate are conducive to the nucleation, growth, and aggregation of K2SO4. Suspension density promotes crystal nucleation and growth but has the opposite effect on accumulation. At the same time, based on the constant calculation equation of particle number and the constant calculation equation of mass, the extraction crystallization coupling mathematical model of the crystal in the fluidization state is established. The effects of suspension density, Reynolds number, extraction rate, feeding rate, and extraction concentration on potassium sulfate's nucleation, growth, and aggregation rate were studied by combining model calculation with experimental research. A process coupling model of the potassium sulfate extraction crystallization process in a fluidized bed was proposed. The model agrees with the experimental results, which have specific guiding significance for industrial production. [ABSTRACT FROM AUTHOR]

Rainwater harvesting (RWH) systems are one of the most promising technologies for water supply, but the economic viability often barricades their implementation. To assess the economic viability of decentralized RWH systems at a regional level, this paper develops a generic method to investigate the effect of variations in building characteristics on the economic performance of rainwater harvesting systems in regions with fixed water tariffs and rainfall distribution characteristics. We simulated the financial efficiency (expressed as a benefit-cost ratio) of a large number of decentralized RWH systems in Guangzhou, China. We found that the financial efficiency of the RWH systems is closely related to the catchment fraction (i.e., the ratio of rainfall catchment surface to total floor area). Based on this dimensionless parameter, we derived explicit equations expressing the financial efficiency of RWH systems using a nonlinear regression method. In addition, we validated the analytical equations by the root mean square error test, normality test, and error distribution test. This analytical solution provides a simple and generic method for forecasting the investment potential of rainwater harvesting systems. This methodology can also be adapted to other regions where the variations in local water price and rainfall data need to be considered. [ABSTRACT FROM AUTHOR]

The discrete element method (DEM) is widely used for its flexibility in handling a large number of geotechnical problems. To study the relationship between macro–micro parameters during the shear process, a series of rock direct shear tests were carried out by PFC2D in this paper. Firstly, the qualitative influence of main microparameters (particle contact elastic modulus, particle ratio of normal to shear stiffness, particle friction coefficient, parallel bond tensile strength, parallel bond cohesion, and parallel bond friction angle) and normal stress on macroscopic shear parameters were analyzed by simulation results. Subsequently, one-way analysis and multivariate nonlinear regression of the relationship between micro- and macro- parameters were performed, and the quantitative prediction models of the main shear macro-parameters (shear strength, shear stiffness, residual shear strength) were established. Finally, comparing the simulation results with the predicted values of the proposed model, it was found that the predicted values were very close to the simulation results, thus verifying the effectiveness of the model. [ABSTRACT FROM AUTHOR]

As an important public facility, the number, area, and scale of hospital buildings are growing rapidly. The efficiency of nurses' rounds to beds is an important indicator of the efficiency of nursing units in ward buildings. Ward buildings occupy a very important position in the overall energy consumption of hospital building complexes. The type and scale of nursing unit floorplans are some of the key factors affecting the energy consumption of ward buildings. In this paper, three typical floorplan layout types of hospital ward buildings in cold regions of China are selected. The relationships between rounding efficiency, building energy consumption, floorplan layout, and building size were quantified using Origin based on linear regression and non-linear regression. The study showed that at 60 beds, the efficiency of nurse rounds was 35.68% higher in the double-corridor layout compared to the single-corridor. At 44 beds, the difference in average bed energy consumption between the double-corridor type and the single-corridor type is the greatest, with a 9.02% saving in energy consumption. This result confirms that the layout and scale of the ward building has a significant impact on the efficiency of nursing unit rounds and building energy efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates whether the impact of income on CO2 emissions is invariant to endogenously estimated threshold levels for the economic structure (ES) represented by value added in manufacturing, industry, and service sector shares in GDP for a panel of 54 economies over the 1971–2017 period. Our panel smooth transition regression estimation results strongly suggest that the sensitivity of CO2 emissions to income is substantially much higher in countries with higher manufacturing and industry sector shares, whilst it is much lower in servicified economies. Given the argument that manufacturing is the engine of growth, this finding may not necessarily downgrade the crucial importance of an industrial policy which places the manufacturing at the core. The empirical findings in this paper suggest that countries may better to design and implement a strategic and systematic industrial policy which promote the use of emission reduction technologies and encourage manufacturing and industrial sectors with lower carbon emissions. [ABSTRACT FROM AUTHOR]

The global economy's transition toward more sustainable development models is undoubtedly grounded on small and medium‐sized enterprises (SMEs). However, SMEs, individual entrepreneurs, and microenterprises have always encountered barriers to implementing social responsibility and sustainability concepts. The paper investigates the enabling role of formalized corporate networks to drive SMEs toward sustainable behaviors. A quantitative nonlinear regression approach is applied to a content analysis of a sample of network contracts coded. The content analysis is applied to analyze the declared objectives, the purpose of the contract, and sustainability areas. An ordered logistic regression is applied on variables related to the behavior of SMEs before entering in the contract and post‐adhesion phases. Data demonstrates how networks of SMEs can be used as enabling factors to boost sustainability among them. Specifically, the study is based on a sample of 96 formalized network contracts (FNCs), including 1486 Italian SMEs in that sustainability‐oriented networks. It offers an evidence‐based perspective on how networks of companies can play a fundamental role in the development of policies aimed at bringing small companies closer to the concept of sustainability (such as eco‐innovations, eco‐efficiency, environmental performance, and social innovations, among others) and its practical implementation. This paper has two significant strengths. The first is that it uses as a sample a set of 1486 companies, including individual entrepreneurs and microenterprises, whose data are usually difficult to collect. The second is that it demonstrates the efficacy of a contractual form that could be scalable to different countries. [ABSTRACT FROM AUTHOR]

The thermal inertia of buildings brings considerable flexibility to the building heating and cooling loads, which is believed to be a promising demand response resource in energy systems. However, it is challenging to utilize the thermal inertia of buildings in the operation of energy systems because of the complicated thermal dynamics and high computational cost. This paper proposes a data-driven aggregate thermal dynamic model (ATDM) for the multi-zone building and building cluster, respectively, which offers an equivalent and low-complexity building model for the operation and control of energy systems. The ATDM consists of the aggregation equation and the state equation. The former projects the detailed real states of buildings into the characteristic state (i.e., aggregate state) using an affine function, and the latter describes the thermal dynamics of buildings using the aggregate state. The ATDM is formulated for two practical load control strategies for the building cluster, including direct load control and indirect load control. Then, the constrained nonlinear regression model is proposed to estimate the model parameters and occupant behavior, for which an efficient algorithm based on the block coordinate descent method is developed by exploiting the decomposable structure of the regression model. Simulation results based on real-world data show that the root mean square error and mean absolute percentage error for the multi-zone building (or building cluster) are below 0.72 °C and 1.44% (or 0.32°C and 1.39%), respectively, verifying the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. A new test for linear cointegration against nonlinear departures is developed based on a simple linearized pseudo-model that is very convenient for practical implementation and has standard normal limit theory in the strictly exogenous regressor case. Accompanying the asymptotic theory of nonlinear regression, the paper establishes some new results on weak convergence to stochastic integrals that go beyond the usual semimartingale structure and considerably extend existing limit theory, complementing other recent findings on stochastic integral asymptotics. The paper also provides a general framework for extremum estimation limit theory that encompasses stochastically nonstationary time series and should be of wide applicability. [ABSTRACT FROM AUTHOR]

This paper develops an asymptotic theory of nonlinear least squares estimation by establishing a new framework that can be easily applied to various nonlinear regression models with heteroscedasticity. As an illustration, we explore an application of the framework to nonlinear regression models with nonstationarity and heteroscedasticity. In addition to these main results, this paper provides a maximum inequality for a class of martingales, which is of interest in its own right. [ABSTRACT FROM AUTHOR]

This paper presents an instrument for finding the location of an object in 3-D space. The location is determined using estimates of the object’s distance from static anchor points with known coordinates. This process involves three main challenges: 1) a way of measuring the Euclidean distance between two points; 2) estimating system parameters such as the position of the anchor points, the speed of sound, and data acquisition delay; and 3) computing the position of the object from its distance to the anchor points. This paper addresses each of these challenges. A wideband acoustic signal is used for distance estimation. Nonlinear regression techniques are used to estimate the positions of the anchors and jointly optimize other system parameters. After calibration, a similar method is also used, this time using the optimized parameter set, to estimate the position of the target object. The design and performance of a prototype system, based on the proposed method, are discussed. The prototype, using five anchors and 13 reference locations, achieved a mean positioning accuracy of 0.86 mm over a test volume spanning 0.5 m $\times0.5$ m $\times0.25$ m. [ABSTRACT FROM AUTHOR]

The article introduces several papers published within the issue, including one on inversing rig functions through nonlinear regression, another on capturing the motion of viscous thin film flows on a curved surface and a paper on using Smoothed Particle Hydrodynamics in computer animation.

In this paper, we give a necessary and sufficient criterion for the existence of the ℓ p -norm estimate for the nonlinear ℓ p -norm fitting problem. Our criterion is based on the existence level that describes the behavior of the objective function as its argument approaches the extended boundary of the parameter space. [ABSTRACT FROM AUTHOR]

Intelligent compaction (IC) technology have been used for quality control and quality assurance (QC/QA) in subgrade construction. The effective regression correlations between Intelligent Compaction Measurement Values (ICMV) and In-situ Measurement Values (ISMV, including compaction degree and subgrade modulus ELWD) are the essential prerequisite of using IC technology for QC/QA. This paper presents the results from an experimental research study that was conducted from a practical subgrade project of China to explore the regression relationships between ICMV and ISMV. Three types of ICMV, including CMV, CCV and Evib, were collected along with the corresponding positions of the rollers. Two types of ISMV, containing compaction degree and ELWD, were measured by ring sampler method and light weight deflectometer (LWD) at specified test points, respectively. Based on these data, the influences of roller parameters and subgrade properties on the regression relationships of ICMV and ISMV were investigated. In addition, linear regression and 5 nonlinear regression algorithms were compared. The results suggest that ICMV reflect the stiffness of subgrade more than reflecting the density. In the regression of ICMV and ISMV, subgrade properties are more important than roller parameters while both of them should not be neglected. The influences of underlying stiffness and roller amplitude are linear while that of roller speed and moisture content are nonlinear. Nonlinear algorithms, especially the random forest, have the better performance compared to linear algorithm. Moreover, the combination of random forest and linear algorithm can further improve the accuracy of ISMV prediction. [ABSTRACT FROM AUTHOR]

Conventional convergence analysis on mini-batch learning is usually based on the stochastic gradient concept, in which we assume that the training data are presented in a random order. Also, some convergence results require that the learning rate should decrease with the number of training cycles, and that the objective function is a smooth function. Practically speaking, a deterministic presentation scheme with a fixed learning rate is more preferable. Hence, there is a gap between theoretical results and actual implementation. This paper aims at filling the gap. We use the radial basis function (RBF) model for nonlinear regression problems as an example to analyze the convergence properties of mini-batch learning. This paper considers a nonsmooth objective function, which consists of three terms. The coexistence of these three terms is able to handle a number of situations. The first term is a conventional training set error. The second term is a quadratic term which is used to suppress the effect of imperfections in the implementation. The last term is an ℓ 1 -norm term which is used to select important RBF nodes for the resultant network. Note that the ℓ 1 -norm term is a nonsmooth function. Although a nonsmooth ℓ 1 -norm is included and the mini-batch algorithm is deterministic, we are still able to derive the convergence properties, including the sufficient conditions for convergence and range of learning rate. With our results, we have a better theoretical understanding on the behaviour of mini-batch learning and obtain some guidelines on choosing the learning rate. The analysis results can be extended to other flat structural neural network models and other objective functions, which are with quadratic terms and ℓ 1 -norm. [ABSTRACT FROM AUTHOR]

Dynamic multi-objective problems (DMOPs) permeate all aspects of daily life and practical applications. As the variables of the search space or target space alter in pace with time, savants are also deepening the research on DMOPs, among which methods based on prediction mechanisms have been extensively developed. The historical optimal solutions can effectively predict the trend and location of the optimal solutions in the future. In this paper, a new hybrid prediction model (HPM) integrating the fuzzy linear prediction model with entropy-like kernel function and the one-step prediction model is developed to sort out DMOPs. In the method, the predicted center by the HPM prediction model is combined with the approximate manifold of PS to generate a trail population, and the linear one-step prediction model is utilized to generate another trail population. When the environment changes, the initial PS at the next moment is obtained by randomly crossing these two trail populations. To assess the proposed HPM model, it is compared with the reinitialization strategy, feedforward prediction strategy, population prediction strategy, T-S nonlinear regression strategy with multistep prediction and individual-based transfer learning under different MOEA optimizers for 22 benchmark problems. The results indicate that HPM has great advantages in solving these dynamic optimization problems. [ABSTRACT FROM AUTHOR]

This paper investigates the online learning problem of Lagrangian ε -twin support vector regression (L- ε -TSVR), with the goal of presenting incremental implementations. First, to solve the problem that the existing L- ε -TSVR cannot efficiently update the model under incremental scenarios, an incremental Lagrangian ε -twin support vector regression (IL- ε -TSVR) based on the semi-smooth Newton method is proposed. By utilizing the matrix inverse theorems to update the Hessian matrices incrementally, IL- ε -TSVR lowers the time complexity and expedites the training process. However, when solving the problem of nonlinear case, the training speed of IL- ε -TSVR rapidly decreases with the increasing size of the kernel matrix. Therefore, an incremental reduced Lagrangian ε -twin support vector regression (IRL- ε -TSVR) is presented. IRL- ε -TSVR employs the reduced technique to restrict the size of the inverse matrix at the cost of slightly lower the prediction accuracy. Next, to lighten the prediction accuracy loss caused by parameters reduction, a novel regularization term is introduced to replace the original one, and an improved incremental reduced Lagrangian ε -twin support vector regression (IIRL- ε -TSVR) is designed. The results on UCI benchmark datasets show that IL- ε -TSVR can effectively address the linear regression problem under incremental scenarios and obtain almost the same generalization capability as offline learning. Moreover, IRL- ε -TSVR and IIRL- ε -TSVR can reduce training time of nonlinear regression model and obtain sparse solution, and their generalization capabilities are close to those of offline ones. Particularly, the proposed algorithms can implement fast incremental learning of large-scale data. [ABSTRACT FROM AUTHOR]

Throughout the lifespan of a bridge, morphological changes in the riverbed affect the variable action-imposed loads on the structure. This emphasizes the need for accurate and reliable data that can be used in model-based projections targeted for the identification of risk associated with bridge failure induced by scour. The aim of this paper is to provide an analysis of scour depth estimation on large sand-bed rivers under the clear water regime, detect the most influential (i.e., explanatory) variables, and examine the relationship between them and scour depth as a response variable. A dataset used for the analysis was obtained from the United States Geological Survey's extensive field database of local scour at bridge piers, i.e., the Pier-Scour Database (PSDB-2014). The original database was filtered to exclude the data that did not reflect large sand-bed rivers, and several influential variables were omitted by using the principal component analysis. This reduction process resulted in 10 influential variables that were used in multiple non-linear regression scour modeling (MNLR). Two MNLR models (i.e., non-dimensional and dimensional models) were prepared for scour estimation; however, the dimensional model slightly overperformed the other one. According to the Pearson correlation coefficients (r), the most influential variables for estimating scour depth were as follows: Effective pier width (r = 0.625), flow depth (r = 0.492), and critical and local velocity (r = 0.474 and r = 0.436), respectively. In the compounded hydraulic-sediment category, critical velocity had the greatest impact (i.e., the highest correlation coefficient) on scour depth in comparison to densimetric Froude and critical Froude numbers that were characterized by correlation coefficients of r = 0.427 and r = 0.323, respectively. The remaining four variables (local and critical bed shear stress, Froude number, and particle Reynolds number) exhibited a very weak correlation with scour depth, with r < 0.3. [ABSTRACT FROM AUTHOR]

Physical simulation is one of the effective methods to study mining problems, but the selection and proportion of simulation materials are greatly affected by the regional environment. This paper is based on a multilevel orthogonal design test scheme using sand, lime, and gypsum as the materials in the Shangwan coal mine in the Shendong coalfield, with the sand to cement ratio, paste to ash ratio, and maintenance days as variables. The effect of the polar difference method on the strength and density of gypsum was used as a reference for physical simulation in the Shendong coalfield. The sensitivity analysis of each factor was carried out by the polar difference method, and the influencing factors on density were, in descending order, sand to mortar ratio, mortar to ash ratio, and the number of maintenance days; the influencing factors on strength were, in descending order, mortar to ash ratio, maintenance days, and sand to mortar ratio. The sand cement ratio was negatively correlated with strength and density, the paste to ash ratio was positively correlated with strength and density, and the number of maintenance days was positively correlated with strength and negatively correlated with density. The multivariate non-linear regression analysis of sand to cement ratio and paste to ash ratio identified similar material proportioning test equations for the Shendong coalfield, which can improve the accuracy of physical simulation and be used to guide physical simulation experiments in the Shendong coalfield. [ABSTRACT FROM AUTHOR]

As more genetic information becomes available, such as DNA methylation levels, it becomes increasingly important to have techniques to analyze such data in the context of cancers such as anal and cervical carcinomas. In this paper, we present an algorithm that differentiates between healthy control patients and individuals with anal and cervical carcinoma, using as an input DNA methylation data. The algorithm used a combination of ridge regression and neural networks for the classification task, achieving high accuracy, sensitivity and specificity. The relationship between methylation levels and carcinoma could in principle be rather complex, particularly given that a large number of CpGs could be involved. Therefore, nonlinear techniques (machine learning) were used. Machine learning techniques (nonlinear) can be used to model linear processes, but the opposite (linear techniques simulating nonlinear models) would not likely generate accurate forecasts. The feature selection process is carried out using a combination of prefiltering, ridge regression and nonlinear modeling (artificial neural networks). The model selected 13 CpGs from a total of 450,000 CpGs available per patient with 171 patients in total. The model was also tested for robustness and compared to other more complex models that generated less precise classifications. The model obtained (testing dataset) an accuracy, sensitivity and specificity of 97.69%, 95.02% and 98.26%, respectively. The reduction of the dimensionality of the data, from 450,000 to 13 CpGs per patient, likely also reduced the likelihood of overfitting, which is a very substantial risk in this type of modelling. All 13 CpGs individually generated classification forecasts less accurate than the proposed model. [ABSTRACT FROM AUTHOR]

Foreshock detection before mainshock occurrence is an important challenge limiting the short-term forecasts of large earthquakes. Various models for predicting mainshocks based on discrimination of foreshocks activity have been proposed, but many of them work in restricted scenarios and neglect foreshocks and mainshocks out of their scope. In disaster prevention, it is often necessary to change the forecast period and the magnitude of target mainshocks. This paper presents a cluster-based statistical discrimination of foreshocks which is applicable all over Japan and adjustable with respect to forecasting time span and mainshock magnitudes. Using the single-link clustering method, the model updates the expanding seismic clusters and determines in real time the probabilities that larger subsequent events will occur. The foreshock clusters and the others show different trends of certain feature statistics with respect to their magnitudes and spatiotemporal distances. Based on those features and the epicentral location, a nonlinear logistic regression model is used to evaluate the probabilities that growing seismic clusters are foreshocks that will trigger mainshocks within 30 days. The log of odds is estimated between the foreshock clusters and other clusters for respective feature values as nonlinear spline functions from a Japanese hypocenter catalog for the period 1926–1999. Based on the estimated odds functions, foreshock clusters tend to have smaller differences in their two largest magnitudes, shorter time durations, and slightly longer epicentral distances within the individual clusters. Given a potential foreshock cluster, its mainshock magnitude can be predicted by the Gutenberg–Richter law over the largest foreshock magnitude. The timing of mainshock occurrences from foreshocks is also predicted by multiplying the portion of mainshocks within a shorter span from those within 30 days by the evaluated foreshock probabilities. The predictive performance of our model is validated by the holdout method using a Japanese hypocenter catalog before and after 2000. The evaluated foreshock probabilities are roughly consistent with the actual portion of foreshocks in the validation catalog and could serve as an alert for large mainshocks. [ABSTRACT FROM AUTHOR]

In this paper, the influence of ground motion duration on the inelastic displacement ratio, C1, of highly damped SDOF systems is studied. For this purpose, two sets of spectrally equivalent long and short duration ground motion records were used in an analysis to isolate the effects of ground motion duration on. The effect of duration was evaluated for observed values of C1 by considering six ductility levels, and different damping and post-yield stiffness ratios. A new predictive equation of C1 also was developed for long and short duration records. Results of non-linear regression analysis of the current study provide an expression with which to quantify the duration effect. Based on the average values of estimated C1 ratios for long duration records divided by C1 for a short duration set, it is concluded that the maximum difference between long and short duration records occurs when the damping ratio is 0.3 and the post-yield stiffness ratio is equal to zero. [ABSTRACT FROM AUTHOR]

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Benefiting from low cost, high tensile strength, chemical stability, and great resistance to temperature, alkaline, and acids, it is a reasonable and valuable technology to use basalt fiber (BF) as an admixture to optimize building materials. So far, the challenge is still to master the engineering performance of BF-reinforced materials, especially poor subgrade soils. To this end, this paper carried out a series of unconfined compressive strength (UCS) tests, splitting tensile strength (STS) tests, and scanning environmental microscope (SEM) tests to study the mechanical properties and microstructure mechanism of BF-reinforced subgrade cemented silty sand with different fiber contents and curing times. The aims of this research were: (i) the UCS and STS of basalt fiber reinforced uncemented silty sand (BFUSM) and basalt fiber reinforced cemented silty sand (BFCSM) both increased with the increase of curing time and the strength reached the maximum value after curing for 28 days; (ii) the optimal fiber content was 0.2%, and a good linear correlation existed between UCS and STS; (iii) from the microscopic point of view, the combination of BF and cement could combine the physical action of fiber reinforcement and the chemical action of cement hydration reaction to form a fiber-cement-soil skeleton structure to improve the strength of silty sand and the improvement effect after working together was better than separately incorporated BF or cement; and (iv) the corresponding developed multiple nonlinear regression (MNLR) models which can well predict UCS and STS of BFUSM and BFCSM were established. [ABSTRACT FROM AUTHOR]

Evaluating the seismic vulnerability of regional group structures is essential for effectively quantifying the seismic resilience and risk analysis of regional cities. To study the degree of damage caused by a typical earthquake to the overall group of buildings in a specific area, this paper takes the field observation data of the Wenchuan earthquake on May 12, 2008, in China as the research background and makes empirical vulnerability statistics and classification on all the inspection data of structural groups in Dujiangyan city. The seismic risk experience database (8621 building samples) based on the overall regional group buildings is developed. Using nonlinear regression, probabilistic risk prediction, and a cumulative damage conditional probability model, the empirical vulnerability matrices of six typical groups of buildings in different seismic intensity zones are established. Considering the effects of seismic design, age, purpose, number of floors, and plane shape features on the seismic resilience and vulnerability of regional group structures, a multifactor vulnerability innovation comparison model based on updating the quantitative scale of vulnerability level is developed. Ultimately, an innovative seismic vulnerability update index domain assessment model is proposed to quantify the damage modes of regional group structures. Zonal fragility prediction models of six typical regional structure groups are established. In the analysis results of the multidimensional empirical vulnerability model, it is found that with the increase in the macroseismic intensity grade, the sample number distribution shows a decreasing trend. Additionally, an essential finding is that the damage of the regional reinforced concrete structure is relatively light, indicating that it has a certain seismic resilience. The empirical vulnerability database is used within the proposed model to obtain a seismic vulnerability index. [ABSTRACT FROM AUTHOR]

Self-sensing actuation of shape memory alloy (SMA) means to sense both mechanical and thermal properties/variables through the measurement of any internally changing electrical property such as resistance/inductance/capacitance/phase/frequency of an actuating material under actuation. The main contribution of this paper is to obtain the stiffness from the measurement of electrical resistance of a shape memory coil during variable stiffness actuation thereby, simulating its self-sensing characteristics by developing a Support Vector Machine (SVM) regression and nonlinear regression model. Experimental evaluation of the stiffness of a passive biased shape memory coil (SMC) in antagonistic connection, for different electrical (like activation current, excitation frequency, and duty cycle) and mechanical input conditions (for example, the operating condition pre-stress) is done in terms of change in electrical resistance through the measurement of the instantaneous value. The stiffness is then calculated from force and displacement, while by this scheme it is sensed from the electrical resistance. To fulfill the deficiency of a dedicated physical stiffness sensor, self-sensing stiffness by a Soft Sensor (equivalently SVM) is a boon for variable stiffness actuation. A simple and well-proven voltage division method is used for indirect stiffness sensing; wherein, voltages across the shape memory coil and series resistance provide the electrical resistance. The predicted stiffness of SVM matches well with the experimental stiffness and this is validated by evaluating the performances such as root mean squared error (RMSE), the goodness of fit and correlation coefficient. This self-sensing variable stiffness actuation (SSVSA) provides several advantages in applications of SMA: sensor-less systems, miniaturized systems, simplified control systems and possible stiffness feedback control. [ABSTRACT FROM AUTHOR]