Environmental benefits and impacts forecasting for three-phase induction motors operations in marine applications: A multiple linear regression approach

A series of 30 neonicotinoid insecticides, bearing nitroconjugated double bond and five-membered heterocycles and nitromethylene compounds containing a tetrahydropyridine ring with exo-ring ether modifications, active against the cowpea aphids (Aphis craccivora), was analyzed using multiple linear regression (MLR) method. The semiempirical quantum chemical PM7 approach was employed for structure optimization. Structural descriptors were calculated for the minimum energy conformers and were related to the insecticidal activity (expressed as pLC50 values) through genetic algorithm, using the multiple linear regression (MLR) approach. Several parameters were applied to check the internal and external model validation. The final MLR models demonstrated good statistical results and predictive power. The presence of more than 6-membered rings, a large number of rings containing secondary C(sp3) atoms, and/or higher values of strongest basic pKa in the core structure of neonicotinoids are considered to decrease the insecticide activity.

Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication

Present investigation was undertaken to predict first lactation 305-day milk yield (FL305DMY) using monthly test day milk records. Under this study, multiple linear regression (MLR) and artificial neural network (ANN) approach were used. Effectiveness of both methods was also compared for prediction of FL305DMY in Murrah buffalo. The data on 3336 monthly test day milk yields records of first lactation pertaining to 556 Murrah buffaloes maintained at National Dairy Research Institute, Karnal; Central Institute for research on buffalo; Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana and Choudhary Charan Singh Haryana Agricultural University (CCSHAU), Hisar were used in this study. In MLR study, it was observed that model 14 having four independent variable, i.e. FSP, TD2, TD4 and TD6 fulfilled most criteria such as highest R2, lowest MSE, lowest RMSE, lowest CP, lowest MAE, lowest MAPE, and lowest U value. In the present investigation, the accuracy of prediction obtained from ANN was almost similar to MLR for prediction of FL305DMY using monthly test day milk records in Murrah buffalo. The best ANN algorithm achieved 76.8% accuracy of prediction for optimum model, whereas the MLR explained 76.9% of accuracy of prediction of FL305DMY in Murrah buffalo. MLR method is simple as compared to ANN, hence MLR method could be preferred.

شور شدن خاک‌ها در جهان به گونه‌ای روزافزون روبه گسترش است و درنتیجه تولید محصولات کشاورزی در مواجهه با این تنش کاهش می‌یابد. سیاست‌گذاران و تصمیم‌سازان در راستای برنامه‌ریزی برای تطبیق با تغییرات اقلیمی و افزایش نیاز به غذا نیازمند پایش کمی مستمر شوری خاک می-باشند. شاخص‌های طیفی حاصل از سنجنده‌های ماهواره‌ای و یا سنجنده‌های نزدیک به سطح زمین به‌طور روزافزونی برای پایش شوری خاک مورداستفاده قرار می‌گیرند به‌نحوی‌که تا کنون تعداد زیادی شاخص برای پایش شوری خاک معرفی شده‌اند. برای مدل‌سازی و سنجش اعتبار مدل حاصله روش‌های رگرسیونی مختلفی مورداستفاده قرار گرفته که مهم‌ترین آن‌ها رگرسیون خطی چندگانه (شامل رگرسیون گام‌به‌گام، انتخاب رو به جلو و حذف رو به عقب) و رگرسیون حداقل مربعات جزئی است. در این پژوهش به‌منظور ارزیابی این دو روش در مدل‌سازی تغییرات شوری خاک از اندازه-گیری‌های آزمایشگاهی و الکترومغناطیسی شوری خاک مربوط به 97 نقطه در سال 1392 و 225 نقطه در سال 1393 در بخشی از دشت سبزوار- داورزن به مساحت حدود 50 هزار هکتار استفاده شد. تعداد 23 شاخص طیفی از تصاویر ماهواره لندست 8 مربوط به تاریخ‌های نمونه‌برداری استخراج و به همراه مدل رقومی ارتفاع به‌عنوان متغیر مستقل مورداستفاده قرار گرفت. روش‌های مختلف رگرسیون خطی چندمتغیره با استفاده از داده‌های سال اول به‌عنوان آموزش و سال دوم به‌عنوان آزمون و بالعکس هرچند ضریب تبیین بین حدود 22 تا 88 درصد ایجاد کرد، ولی این همبستگی در دسته اعتبار سنجی از 29 درصد تجاوز نکرد. به علت وجود هم‌راستایی خطی چندگانه در بین متغیرهای مستقل روش رگرسیون خطی چندگانه برای تمام متغیر‌ها قابل کاربرد نبود. حذف متغیرهای دارای هم‌راستایی خطی، تبدیل لگاریتمی و تصادفی کردن کل داده‌ها در دو دسته آموزش و آزمون، ضریب رگرسیون مدل و اعتبار آن را به‌طور قابل قبولی افزایش داد. استفاده از رگرسیون حداقل مربعات جزئی با استفاده از داده‌های اصلی و تبدیل لگاریتمی شده سال اول و دوم به‌عنوان آموزش و آزمون و بالعکس نیز در دسته آموزش ضریب تبیین بین 39 تا 85 درصد ایجاد کرد، ولی از برآورد در دسته آزمون ناتوان بود. تصادفی کردن داده‌ها و تقسیم مجدد آن‌ها به دو دسته آموزش و آزمون موجب ارتقای چشمگیر ضریب تعیین در دسته اعتبارسنجی شد. تکرار عملیات تصادفی کردن نشان داد که روش از ثبات لازم برای برآورد ضرایب متغیرها برخوردار است.

The body of life cycle assessment (LCA) literature is vast and has grown over the last decade at a dauntingly rapid rate. Many LCAs have been published on the same or very similar technologies or products, in some cases leading to hundreds of publications. One result is the impression among decision makers that LCAs are inconclusive, owing to perceived and real variability in published estimates of life cycle impacts. Despite the extensive available literature and policy need formore conclusive assessments, only modest attempts have been made to synthesize previous research. A significant challenge to doing so are differences in characteristics of the considered technologies and inconsistencies in methodological choices (e.g., system boundaries, coproduct allocation, and impact assessment methods) among the studies that hamper easy comparisons and related decision support. An emerging trend is meta-analysis of a set of results from LCAs, which has the potential to clarify the impacts of a particular technology, process, product, or material and produce more robust and policy-relevant results. Meta-analysis in this context is defined here as an analysis of a set of published LCA results to estimate a single or multiple impacts for a single technology or a technology category, either in a statisticalmore » sense (e.g., following the practice in the biomedical sciences) or by quantitative adjustment of the underlying studies to make them more methodologically consistent. One example of the latter approach was published in Science by Farrell and colleagues (2006) clarifying the net energy and greenhouse gas (GHG) emissions of ethanol, in which adjustments included the addition of coproduct credit, the addition and subtraction of processes within the system boundary, and a reconciliation of differences in the definition of net energy metrics. Such adjustments therefore provide an even playing field on which all studies can be considered and at the same time specify the conditions of the playing field itself. Understanding the conditions under which a meta-analysis was conducted is important for proper interpretation of both the magnitude and variability in results. This special supplemental issue of the Journal of Industrial Ecology includes 12 high-quality metaanalyses and critical reviews of LCAs that advance understanding of the life cycle environmental impacts of different technologies, processes, products, and materials. Also published are three contributions on methodology and related discussions of the role of meta-analysis in LCA. The goal of this special supplemental issue is to contribute to the state of the science in LCA beyond the core practice of producing independent studies on specific products or technologies by highlighting the ability of meta-analysis of LCAs to advance understanding in areas of extensive existing literature. The inspiration for the issue came from a series of meta-analyses of life cycle GHG emissions from electricity generation technologies based on research from the LCA Harmonization Project of the National Renewable Energy Laboratory (NREL), a laboratory of the U.S. Department of Energy, which also provided financial support for this special supplemental issue. (See the editorial from this special supplemental issue [Lifset 2012], which introduces this supplemental issue and discusses the origins, funding, peer review, and other aspects.) The first article on reporting considerations for meta-analyses/critical reviews for LCA is from Heath and Mann (2012), who describe the methods used and experience gained in NREL's LCA Harmonization Project, which produced six of the studies in this special supplemental issue. Their harmonization approach adapts key features of systematic review to identify and screen published LCAs followed by a meta-analytical procedure to adjust published estimates to ones based on a consistent set of methods and assumptions to allow interstudy comparisons and conclusions to be made. In a second study on methods, Zumsteg and colleagues (2012) propose a checklist for a standardized technique to assist in conducting and reporting systematic reviews of LCAs, including meta-analysis, that is based on a framework used in evidence-based medicine. Widespread use of such a checklist would facilitate planning successful reviews, improve the ability to identify systematic reviews in literature searches, ease the ability to update content in future reviews, and allow more transparency of methods to ease peer review and more appropriately generalize findings. Finally, Zamagni and colleagues (2012) propose an approach, inspired by a meta-analysis, for categorizing main methodological topics, reconciling diverging methodological developments, and identifying future research directions in LCA. Their procedure involves the carrying out of a literature review on articles selected according to predefined criteria.« less

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m<sup>2</sup> and 84.04 W/m<sup>2</sup>, while using the MLR method they are 141.18 W/m<sup>2</sup> and 104.87 W/m<sup>2</sup> respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.

The application of neural networks in the implementation of ECG classifiers has become widespread. Unfortunately due to the lack of scientific evidence many of the choices made in the design of these classifiers are based on trial and error. This paper details an investigation into a statistical approach aimed at reducing the computational requirements when training an ECG classifier. The multiple linear regression method was used to develop a predictor that would indicate at which point training of a neural network should stop. When tested it was found that this genre of predictor exhibited reasonable accuracy and out performed other predictors based on neural network and genetic programming techniques.

Building energy performance forecasting: A multiple linear regression approach

Estimating hourly average indoor PM2.5 using the random forest approach in two megacities, China

It is important to develop a suitable model to calculate electricity demand forecasting requested by decision makers. The present study deals with the electrical long-term peak load demand forecasting using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) methods. The MLR model is formulated as a function of population and Gross Domestic Product (GDP) for the Gulf Cooperation Council (GCC) region. The Neuro-Fuzzy is thereafter trained using previous sets of data. This training gives a future annual electricity load prediction. The results obtained from the developed models have an acceptable level of mean errors. In general, the GCC region has high-energy consumption influenced by a number of factors, such as population and GDP. The annual variation of both population and GDP growth scenarios is based on development in the country. The obtained results will encourage the GCC through the energy field development and setting the future plans for it. The novelty of the present study is to avoid an increase in generation capacity in mid-term and long-term plans, which will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. The developed models will help the economic development of the GCC countries. It also helps with finding the optimum time for electrical energy trading. The obtained results for the GCC illustrate the average percentage error calculated which was found to be close to 2% and 0.53% in multilinear regression and Neuro-Fuzzy, respectively. These results reduce capital investment, limiting the equipment installed and the expected load needed for better load distribution in the region. In conclusion, the Neuro-Fuzzy is the most accurate technique compared with MLR to estimate future electricity demand and, at the same time, it can be used in power system planning and development.

Histamine H3 receptor subtype has been the target of several recent drug development programs. Quantitative structure-activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to compare the predictive powers of three different QSAR techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and HASL as a 3D QSAR method, in predicting the receptor binding affinities of arylbenzofuran histamine H3 receptor antagonists. Genetic algorithm coupled partial least square as well as stepwise multiple regression methods were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. Using the leave-group-out cross-validation technique, the performances of the MLR and ANN methods were evaluated. The calculated values for the mean absolute percentage error (MAPE), ranging from 2.9 to 3.6, and standard deviation of error of prediction (SDEP), ranging from 0.31 to 0.36, for both MLR and ANN methods were statistically comparable, indicating that both methods perform equally well in predicting the binding affinities of the studied compounds toward the H3 receptors. On the other hand, the results from 3D-QSAR studies using HASL method were not as good as those obtained by 2D methods. It can be concluded that simple traditional approaches such as MLR method can be as reliable as those of more advanced and sophisticated methods like ANN and 3D-QSAR analyses.

A quantitative structure property relationship for prediction of solubilization of hazardous compounds using GA-based MLR in CTAB micellar media

The goal of this research is to model the level of carbon dioxide flowing from soil to sky using various methods. The methods of multiple linear regression (MLR) and artificial neural networks (ANN) beside two different hybrid models were exploited to achieve this objective. These hybrid models were arranged as the prior two methods with principal component analysis (PCA). For the ANN, 36 different structures were used with different transfer (logsig–logsig, tansig–tansig, pureline–pureline, logsig–tansig, logsig–pureline and tansig–pureline)—learning functions (Levenberg–Marquardt and Gradient Descent with Momentum) and neuron numbers (10, 20 and 30). The manure norm, soil type, soil temperature, soil moisture content, soil depth, and photosynthetically active radiation values were taken into account as input parameters while CO2 flux was output parameter. According to the research conducted, the best results were obtained from the ANN method. This method was followed by PCA + ANN, MLR and PCA + MLR methods. The R2 value of the network established in the ANN method was determined as 0.98. In this ANN model, Levenberg–Marquardt and tansig–pureline with 30 neurons were used as transfer and learning functions, respectively. Besides, when principal components were used as input parameters, the lower R2 values were obtained with both the MLR and ANN methods.

The increasing accuracy of the recently released Global Geopotential Models (GGMs) make them a reasonable geoid models, particularly in developing countries. Incorporating local geodetic datasets into a GGM could enhance its performance significantly. However, such integration requires appropriate mathematical modelling. The current research investigates the factors influencing the fitting of a GGM to heterogeneous geodetic data over local areas. The Multiple Linear Regression (MLR) approach is performed with variable independent factors to model the GGM discrepancies over two study areas in Egypt. Observed Global Navigation Satellite Systems (GNSS)/levelling and measured terrestrial gravity anomalies are investigated, among other independent variables, in the regression modelling. Based on the available data and attained findings, it has been demonstrated that MLR approach could produce a good fitting of a specific GGM’s geoid undulations, namely the XGM2019e_2159 model, locally with a coefficient of determination of more than 0.99. The regression equation has decreased the standard deviation of the investigated GGM-based undulations from ±0.130 m to ±0.046 m. Accordingly, the accuracy of a particular GGM has been enhanced considerably with improvements achieved 99 % and 64 % over the investigated two case study regions in Egypt.