Exponential Approaches Research Articles

ARTIFICIAL INTELLIGENCE ROLE IN OPTIMIZING ELECTRIC VEHICLE CHARGING PATTERNS, REDUCE COSTS, AND IMPROVE OVERALL EFFICIENCY: A REVIEW

The global popularity of electric cars (EVs) as a sustainable means of transportation, reliable and efficient charging infrastructure is essential. Traditional electric vehicle charging involves connecting the car to a power source and waiting for the battery to charge. However, AI has allowed us to improve charging patterns, reduce costs, and boost efficiency. This article examines how AI algorithms are changing electric car charging. If electric vehicle (EV) charging and discharging are not coordinated, the power supply infrastructure will be overrun. Demand response like dynamic pricing might encourage electric vehicle owners to participate in scheduling initiatives. Thus, EV charging and discharging scheduling and dynamic pricing model research are crucial. Artificial intelligence-based models for EV charging predictions and scheduling have been the focus of researchers. These models outperform linear, exponential, and multinomial logit optimization approaches. Due to the novelty and ongoing development of EVs returning electricity to the power grid, vehicle-to-grid (V2G) systems have received little attention. Thus, a complete analysis of EV charging and discharging research is needed to identify gaps and improve future studies. This study categorizes EV charging and discharging studies into forecasting, scheduling, and pricing techniques. The work links forecasting, scheduling, and pricing processes and identifies research gaps in EV discharge scheduling and dynamic pricing models.

Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches

Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity–permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.

Dynamic parameter identification of cold-formed storage rack systems using shaking table test

In this paper, 1:3 scale shaking table tests were performed on three cold-formed steel rack systems with three levels of different heights at the structural laboratory, Yildiz Technical University, to evaluate the factors that affect the dynamic characteristics of the rack systems that are exposed to different excitation frequencies representing the ground motion. The experiments were conducted using a small shaking table designed for this purpose. The dynamic characteristics of the racks were predicted with the help of the signal processing toolbox in MATLAB using the test output data. The results demonstrated that as the mass of the rack structure increases, the rack system vibration frequency caused by external excitation decreases. Also, the damping ratio of the rack systems increases with an increase in the rack height due to the increase in stiffness, and then it starts decreasing because the structure's stiffness begins to decrease with long-term vibration. The modal analysis results demonstrated that the peak-picking (PP) method outperformed the least-squares complex exponential (LSCE) and least-squares rational function estimation (LSRF) approaches in damping estimation. However, each approach achieved nearly identical results for the response frequency in the same mode.

Solving the two-dimensional time-dependent Schrödinger equation using the Sinc collocation method and double exponential transformations

Over the last four decades, Sinc methods have occupied an important place in numerical analysis due to their simplicity and great performance. An incorporation of the Sinc collocation method with double exponential transformation is used to solve the two-dimensional time dependent Schrödinger equation. Numerical comparison between the double exponential and single exponential approaches is made to illustrate the superiority of the double exponential Sinc method.

Fully-decoupled conservative exponential approaches for the coupled nonlinear Schrödinger-Boussinesq equations

Fully-decoupled conservative exponential approaches for the coupled nonlinear Schrödinger-Boussinesq equations

Application of Exponential and Logistic Models in Estimating the Population of Bulukumba Regency in 2020-2030

Population data is useful as planning material in making various policies, including avoiding imbalances between the number of health facilities and services and the population in an area as well as other facilities such as schools, markets, and other public facilities. Ordinary differential equations of exponential and logistic models are used in modeling population dynamics in Bulukumba Regency to obtain population estimates until 2030. The determination of the future population of Bulukumba Regency is based on the growth rate and capacity obtained using the exponential and logistic approaches. The results obtained show that the estimation using the exponential model and the logistic model estimation for 2015-2019 are close to the data from the Central Bureau of Statistics. However, the logistic model is more accurate than the exponential model which is more significantly close to the data from BPS. So that the results of the logistic model are better than the exponential. The logistic model assumes that Bulukumba Regency has a capacity of K = 450000, while the exponential model assumes that the population increases exponentially.

Multi-wave, breather and interaction solutions to (3+1) dimensional sine-Gordon equation arising in nonlinear physical sciences

Based on the Hirota bilinear forms, the multi-wave solutions of the (3 + 1) dimensional sine-Gordon equation with various physical properties were constructed with using symbolic computation. This equation is characterized by constant coefficients that can be integrated in the Painleve sense. Double exponential and homoclinic breather approaches are used to obtain solutions. In addition, the interaction phenomenon between the exponential function, sine function and the hyperbolic sine function are constructed. For a better understanding the dynamics of the underlying equation three dimensional, density and contour graphs are demonstrated for the appropriately selected parameters. Thus, it has become more possible to observe the physical behaviors of the solutions. The results obtained in this study will contribute to the expansion and enrichment of the solution forms available in the literature of the model under consideration. The results obtained could have important applications in nonlinear optics, solid state physics, optical solitons and quantum field theory.

Analyses of Spatial Correlation and Coherence in ABL Flow with a Fleet of UAS

The spatial structures of turbulent flow in the atmospheric boundary layer (ABL) are complex and diverse. Multi-point spatial correlation measurements can help improve our understanding of these structures and their statistics. In this context, we investigate Taylor’s hypothesis and the statistics of spatial structures on the microscale. For the first time, simultaneous horizontally distributed wind measurements with a fleet of 20 quadrotor UAS (unmanned aerial systems) are realized. The measurements were taken at different heights and under different atmospheric conditions at the boundary layer field site in Falkenberg of the German Meteorological Service (DWD). A horizontal flight pattern has been specifically developed, consisting of measurements distributed along and lateral to the mean flow direction with separation distances of 5ldots 205 m. The validity of Taylor’s hypothesis is studied by examining the cross-correlations of longitudinally distributed UAS and comparing them with the autocorrelations of single UAS. To assess the similarity of flow structures on different scales, the lateral and longitudinal coherence of the streamwise velocity component is examined. Two modeling approaches for the decay of coherence are compared. The experimental results are in good agreement with the model approaches for neutral atmospheric conditions, whereas in stable and convective ABL, the exponential approaches are not unconditionally valid. The validation results and the agreement with the literature on coherence in the ABL underline the potential of the UAS fleet for the purpose of spatial turbulence measurements.

Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine

BackgroundThe research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint.ResultsTo favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect.ConclusionsAdaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.

The Bioinformatics of Aptamers: HT-SELEX Analysis with AptaSUITE.

Next-generation systematic evolution of ligands by exponential enrichment approaches consistently combine the experimental protocol with high-throughput sequencing after each round of selection. This extension, known as HT-SELEX, results in a vast amount of raw sequencing data that requires tailored bioinformatics approaches to efficiently process and analyze these reads. Here, we present a step-by-step walkthrough of AptaSUITE, a previously published, open-source, and platform-independent software collection of peer-reviewed bioinformatics tools for HT-SELEX data, integrated into a convenient and efficient graphical user interface. We highlight AptaSUITE's main components and illustrate their utility for a variety of usage scenarios. These include, but are not limited to, computational solutions for data preprocessing such as demultiplexing and quality control, aptamer candidate identification, as well as binding motif elucidation. Taken together, AptaSUITE comprises a complete bioinformatics solution for HT-SELEX data analysis while providing real-time, intuitive, and graphical access to the aptamer information.

An exponential stabilization of random impulsive control systems and its application to chaotic systems

This paper addresses the problem of pth moment exponential stability and stabilization for random impulsive control systems. Some novel pth moment exponential stability and synchronization approaches are established based on the method of maximum and minimum eigenvalues by using the Lyapunov functions and Razumukhin technique. Finally, we show that the stability and synchronization behavior of random impulses are faster than the fixed time impulses. Some physical examples are given to verify the validity and usefulness of the results obtained.

Forecasting of Indonesian Annual Protein Demand for Nile Tilapia (Oreochromis niloticus) Using Exponential Smoothing

The need for protein or the amount of protein needed by the fish sector in Indonesia has never been recorded and known. Although the demand for protein in aquatic feeds is very high, data related to protein requirements for fish feed in Indonesia need to be known for determining the urgency of providing protein substitutes in feed. So, it is necessary to predict feed and protein demand based on a series of fish production data. This study predicted the protein demand for Nile tilapia (Oreochromis niloticus). Three exponential smoothing approaches, namely, single, double, and triple exponential smoothing, were applied to obtain the best model for production estimation. The protein demand was analyzed based on feed conversion ratio and standard protein content in the Nile feed by knowing the production. Production decreased dramatically in 2020. The best approach for predicting is triple exponential smoothing, which predicted a gradual increase of Nile production in 2021. The protein demand for the next five years (2025) is about 345,331.50 to 690,663.00 tons in the scenario of FCR 1.2 and 2.0. It is the first report on predicting protein demand for the production of Nile tilapia. Thus, the government needs to prepare the fulfillment of protein needs for the tilapia fish.

Compact Exponential Conservative Approaches for the Schrödinger Equation in the Semiclassical Regimes

Compact Exponential Conservative Approaches for the Schrödinger Equation in the Semiclassical Regimes

EFFICACY OF THE RICE CROP GROWTH USING DIFFERENT SMOOTHING METHODS

Rice is one of India's most important food crops. The government of India has a major duty in agriculture:perfect mapping and continual monitoring of paddy rice fields. Rice growth has a major impact on SCATSAT-1 backscatter images, and rice fields have been successfully mapped using a timeseries analysis employing satellite data. SCATSAT-1 time-series data was used to detect single-cropped, double-cropped, and triple-cropped rice fields (1 to 3 harvests per year) and identify different phonological stages using a crop phenology-based categorization. The usefulness of rice crop growth utilizing exponential smoothing approaches to estimate and forecast yield growth is demonstrated in this paper. The Holt linear trend, Holt-Winters methods (additive and multiplicative), and Mean Absolute Error (MAE), Sum Squared Error (SSE), Mean Squared Error (MSE), Mean Percentile Error (MPE), and Mean Absolute Percentage Error (MAPE) are used as error factors to choose the best forecasting methods among the exponential smoothing techniques.

Metathesis Cascade‐Triggered Depolymerization of Enyne Self‐Immolative Polymers**

AbstractA novel class of enyne self‐immolative polymers (SIPs) capable of metathesis cascade‐triggered depolymerization is reported. Studies on model compounds established 1,6‐enyne structures for efficient metathesis cascade reactions. SIPs incorporating the optimized 1,6‐enyne motif were prepared via both polycondensation and iterative exponential growth approaches. These SIPs demonstrated excellent stability in strong acid, base, nucleophiles, or at elevated temperatures, and can undergo efficient and complete depolymerization once triggered by a metathesis catalyst. Further studies revealed that introducing a terminal alkene to the chain end of the enyne SIPs improved the depolymerization efficiency, and established their potential as stimuli‐responsive materials.

Metathesis Cascade‐Triggered Depolymerization of Enyne Self‐Immolative Polymers**

A novel class of enyne self-immolative polymers (SIPs) capable of metathesis cascade-triggered depolymerization is reported. Studies on model compounds established 1,6-enyne structures for efficient metathesis cascade reactions. SIPs incorporating the optimized 1,6-enyne motif were prepared via both polycondensation and iterative exponential growth approaches. These SIPs demonstrated excellent stability in strong acid, base, nucleophiles, or at elevated temperatures, and can undergo efficient and complete depolymerization once triggered by a metathesis catalyst. Further studies revealed that introducing a terminal alkene to the chain end of the enyne SIPs improved the depolymerization efficiency, and established their potential as stimuli-responsive materials.

A Curve Fitting Modelling Approach to Forecast Long-Term Electrical Energy Consumption: Case Study of Turkey

For Turkey to achieve the targets of Vision 2023 of being in the top ten economies of the world, the eleventh National Development Plan (NDP11) focuses on ensuring uninterrupted, high-quality, sustainable, reliable and affordable energy supply. In this regard medium- and long-term energy supply-demand planning is regarded as a key input to the planning process. Medium and long-term planning is possible only when reliable forecasts are available. Using Turkey’s electrical energy consumption data from 1970 to 2015, this study presents novel Gaussian, Fourier and Exponential curve fitting and extrapolation approaches to forecast Turkey’s electrical energy consumption up to the year 2025. Major interest is put on how the model forecasts electrical energy consumption for year 2023 because this year marks a century of the establishment of the Republic of Turkey and all strategic plans are focused on how to achieve the targets as outline in Vision 2023. We evaluate the performance of the models on how best they forecast electrical energy consumption for the year 2023. Our forecasts for the year 2023 are 352.7TWh, 377.4 TWh, and 460.1TWh for the Gaussian, Fourier and Exponential models respectively which compare well with NDP11’s estimated 375.8 TWh electrical energy consumption in 2023.

Morphodynamics of an erodible channel under varying discharge

AbstractAlluvial channels arise through the interaction between morphology, hydraulics, and sediment transport, known as the ‘fluvial trinity’. Over relatively short timescales where climate and geology are fixed but discharge and sediment supply may vary, this process facilitates adjustments towards steady state, where the system oscillates around a mean condition. The relationship between changes in conditions and geomorphic response may be highly complex and nonlinear, especially in systems with multiple modes of adjustment. This study examines the adjustment of an erodible channel with fixed banks and a widely graded sediment mixture to successive increases in discharge. With each increase in discharge, components of the fluvial trinity adjusted towards a steady state. Particularly at relatively low discharges, adjustments were controlled by intrinsic thresholds and highlighted important morphodynamic processes. Notably, there was a strong interplay between channel morphology and sediment transport, and an effect whereby larger‐than‐average grains controlled channel deformation. These two processes occurred at the bar scale and were highly spatialised, which has two important implications: (1) reach‐averaged representations of process provide only partial insight into morphodynamics; and (2) models of rivers that suppress these process feedbacks and size‐dependent transport may not replicate morphodynamics that typically occur in field conditions. The experiments provide quantitative evidence for conceptual models describing exponential approaches towards steady state and the potential for transiency if disturbance frequency exceeds the recovery time. They also highlight how in natural rivers, particularly those with greater degrees of freedom for adjustment (notably, lateral adjustment and meandering), continuous changes in discharge may lead to nonlinear rather than steady‐state behaviour. In these settings, more holistic analytical frameworks that embrace different aspects of the system are critical in understanding the direction, magnitude and timing of channel adjustments.

Two linearly implicit energy preserving exponential scalar auxiliary variable approaches for multi-dimensional fractional nonlinear Schrödinger equations

In this paper, we propose two linearly implicit energy preserving schemes with constant coefficient matrix for multi-dimensional fractional nonlinear Schrödinger equations. By introducing an exponential auxiliary variable for the nonlinear energy, we first reformulate the original system into an equivalent system, which admits mass and energy conservation laws. Further, by virtue of the Lawson transformation, we rewrite the modified system into another equivalent system, which possesses both the mass and energy conservation laws. As for the two kinds of modified systems, we construct two classes of linearly implicit energy preserving schemes by combining the implicit midpoint method and the extrapolation strategy. Moreover, the proposed schemes enjoy the second-order accuracy in time and spectral accuracy in space, respectively. Numerical results are reported to demonstrate that the proposed numerical schemes have high efficiency for energy preservation in long-time computations.

CALCULATION OF SNOW HEIGHT (HS) AND SNOW WATER EQUIVALENT (SWE) AT KEY POINTS OF THE NORTHERN KAZAKHSTAN REGION FROM SENTINEL-2 SATELLITE IMAGES

The article presents a comparative assessment of the snow height (HS) in the Limited Liability Partnership "North Kazakhstan Agricultural Experimental Station" using three different approaches of calculation of Snow Cover Fraction (SCF) with further determination of Snow Water Equivalent (SWE) from the one hand and in-situ prospective from the other. It was clear that the quadratic formula of SCF calculation provide better and reliable outcomes with an RMSE 1.36 cm which is followed by linear (12.06 cm) and exponential approaches (12.86). The lowest water level was 9 mm, the average level was about 50 mm, and the highest level was up to 62 mm, according to the SWE map produced by the quadratic equation while the highest, average and the lowest snow height (HS) have reached up to 28 cm, 13 cm and 5 cm respectively. Based on the results and the accuracy obtained, we strongly recommend to use the given methodology in the whole northern and central regions of Kazakhstan to estimate the amount of snow for the further hydrological plans and decisions.