Statistical Model Research Articles

Landscape connectivity for African elephants in the world's largest transfrontier conservation area: A collaborative, multi‐scalar assessment

Abstract Landscape connectivity operates at a variety of scales, depending on the geography of the area in question and the focal species or ecological process under consideration. Most connectivity studies, however, are typically focused on a single scale, which in the case of resistance‐based connectivity modelling, is often the entire landscape or protected area (PA) network. This large, single‐scale focus may miss areas that are important for connectivity at smaller scales and that can be documented via observed animal movements without resorting to landscape‐wide statistical modelling and extrapolation approaches. Here, we characterize landscape connectivity at three different scales (local/micro, inter‐PA, and landscape‐wide/macro), using observed animal movements rather than conventional resistance surface models, to produce a connectivity conservation blueprint for African elephants (Loxodonta africana) in the Kavango‐Zambezi transfrontier conservation area (KAZA) in southern Africa. This analysis is based on an extensive, high‐resolution GPS tracking database comprising approximately 4 million GPS locations from nearly 300 tagged elephants and their associated herds. Our results show that high‐fidelity elephant use of micro‐corridors is typically—though not exclusively—related to directed movements towards water, often amidst heavy anthropogenic presence. Movement pathways that connected KAZA's core protected areas were longer and variable, with some channelled into narrow areas of use and others more dispersed across larger sub‐landscapes. At the largest scale, a network analysis incorporating all used landscape grid cells revealed several clusters of large‐scale movement corridors that connected distant parts of KAZA. Synthesis and applications: Our three scales of analyses reveal disparate geographical priorities for connectivity conservation that collectively could help ensure the functional connectivity of KAZA for its largest inhabitants. Each scale will require its own set of inter‐related conservation interventions, while further research into areas with sparse data collection, and other species of conservation concern, could reveal additional connectivity priorities at each scale.

Experimental evaluation of expansive soil stabilised with sustainable binder incorporating blast furnace slag and bagasse ash

ABSTRACT Civil engineering structures made upon expansive soils known in India as Black Cotton (BC) soils are susceptible to structural damages due to their seasonal swell-and-shrink behaviour. This study focuses on assessing the mechanical performance of BC soil stabilised using unconventional binders, specifically Sugarcane Bagasse Ash (SCBA) and Ground Granulated Blast Furnace Slag (GGBS) with different proportions. The experimental evaluation included Compaction tests, Unconfined Compressive Strength (UCS) tests, Triaxial tests, and Atterberg’s limits tests. Additionally, mineralogical and morphological studies were carried out using x-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive x-ray spectroscopy (EDS), and chemical analysis using x-ray fluorescence spectroscopy analysis (XRF). The results showed that the mixture containing 21% SCBA and 9% GGBS produced cementitious-siliceous-hydrate (C-S-H) molecule, which improved the strength. Based on the soil-binder percentage ratio obtained from UCS tests, a regression equation was developed to estimate consolidated soil strength. The regression model, exhibiting an impressive R2 value of 93.69%, was analysed within the framework of existing empirical correlations by other researchers. This statistical model, with its good fit, is a useful tool for evaluating the compressive strength of stabilised expansive soil. The findings provide insights into successful stabilisation solutions for expansive soils found locally and globally.

Using 7E Method Learning Teaching About Derivative and indefinite Integration In Faculty of Education Souphanouvong University Academic Year 2019 - 2020

The purposes of this study were to: [1] investigate the E.I.(Effectiveness Index) of teaching and learning Derivative and indefinite Integration through 7E (7-E Learning Cycle) technique. [2] investigate students’ satisfaction toward teaching and learning Derivative and indefinite Integration, through 7E technique. The sample group of this study were the second year students majoring in Mathematics and physics in Faculty of Education Shouphanouvong University academic year 2019-2020. The research instrument were pre-test, post –test and questionnaires. The statistics model used for this study was SPSS program. The results of this study were regarded to the E.I. of teaching and learning Derivative and indefinite Integration through 7E technique consequence that E.I.= 0.5484, this meant that it is significantly higher than criterial and students’ satisfaction toward teaching and learning Derivative and indefinite Integration through 7E technique on high level.

Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability.

Generalised Poisson-new linear-exponential distribution

It is a compound discrete probability distribution which has two parameters and whose particular case is Poisson-New Linear-Exponential Distribution (PNLED) [1]. The statistical characteristics required for this distribution such as probability mass function (pmf), statistical moments, estimation of parameters and goodness of fit have been derived and explained nicely. To test theoretical reliability of this distribution, goodness of fit has been applied to some over-dispersed data which were used by other researchers and further, we found that this distribution looks more appropriate for statistical modelling than PNLED, generalised Poisson-Lindley distribution (GPLD) [1, 2] and Poisson-Lindley distribution (PLD) [3].

The Impact of Exchange Rates on Macroeconomic Factors in Lao PDR from 2012-2021

The purpose of the analysis of the impact of exchange rates on macroeconomic factors of Lao PDR from 2012-2022 aims to (1) analyze the fluctuation of macroeconomic factors and exchange rate of Kip/Dollar and Kip/Baht in the Lao PDR from 2012-2021 and (2) the impact of the exchange rate on the macroeconomic factors of the Lao PDR from 2012-2021. By bringing the collected data to summarize, interpret, consider, select and analyze by using descriptive statistical model and inferential statistics to find a value in percentage, parameter value of regression equation with OLS through E-view 12. The analysis results found that: the exchange rate of Kip/Dollar has effect on the Gross National Income (GNI) of the Lao PDR in the same direction relationship, if the exchange rate changes and increases, it means that the kip of Lao PDR is weak. The country will have the ability to compete in terms of price or make the income from exports that change the value of the kip increase and have a continuous effect on the wages of labor in businesses related to exports, investment and the purchasing power of consumers through income and the economic growth increases, lead to export and investment increase. That will result in the increase of the Gross National Income (GNI) of the Lao PDR. The Consumer Price Index of Lao PDR is continuously increasing, if the Dollar /Kip exchange rate increase by 1 US dollar (USD), it will cause the consumer price index (CPI) of Lao PDR to increase by 1.67% in US dollar (USD). For the exchange rate and export relationship of Lao PDR, it shows that if the exchange rate of Dollar/Kip increase by 1 US dollar (USD), it will result in the export value (EX) of Lao PDR increase by 11.15% US dollar (USD). In other side, the Thai/Kip Baht exchange rate is unable to explain the Gross National Income (GNI), Consumer Price Index (CPI) and Lao PDR's exports clearly, which means that there is no relationship among them, or in other words, the Thai Baht exchange rate does not affect the macroeconomic factors in the model of this research.

Birdwatching preferences reveal synergies and tradeoffs among recreation, carbon, and fisheries ecosystem services in Pacific Northwest estuaries, USA

Coastal ecosystems provide multiple ecosystem services that are valued in diverse ways. The Nisqually River Delta (the Delta), an estuary in Puget Sound, Washington, U.S.A., is co-managed by the Nisqually Indian Tribe and the Billy Frank Jr. Nisqually National Wildlife Refuge. In an ecosystem services assessment, we used different service-appropriate methods including citizen science, statistical and geospatial models, and scenario analysis to evaluate three ecosystem services – recreational birdwatching, soil carbon accumulation and fishery production – indicated as priorities for the Refuge, Nisqually Indian Tribe, and surrounding communities. We developed a generalized additive mixed model set based on eBird mobile application birdwatching observations to understand the biological and landscape features that influence birdwatching and to project birdwatching visitation based on scenarios of Delta habitat change. We evaluated ecosystem service synergies and tradeoffs associated with habitat change for three coastal habitat types using scenario outputs from the birdwatching model and published results on Delta soil carbon accumulation and fishery production. The highest-ranked birdwatching models explained 88 % of the deviance and showed that visitation was greatest in winter months when distance to major cities was approximately 20 km. Recreational birdwatching increased with increasing area of forested wetland, emergent wetland, aquatic vegetation bed, open access, and total estuary. With increasing forested and emergent wetland area, recreational birdwatching, out-migrating juvenile Chinook salmon weight and soil carbon accumulation all increased. With increasing aquatic vegetation bed (resulting from sea level rise), recreational birdwatching increased, but salmon weight and soil carbon accumulation decreased. We identified practical ways in which ecosystem services may be incorporated into adaptive management frameworks that support climate adaptation decision making. This study illustrated how use of ecosystem services can help managers make decisions that have greater benefit for wildlife and people, communicate the societal value of decisions and increase local support and participation.

Handling missing data and measurement error for early-onset myopia risk prediction models

BackgroundEarly identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction.MethodsWe explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).ResultsOur findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models.ConclusionMI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction.

Integrating the Energy Performance Gap into Life Cycle Assessments of Building Renovations

The environmental impact of building energy renovation is commonly evaluated through life cycle assessment (LCA). However, existing LCA studies often overlook the energy performance gap—a substantial disparity between calculated and actual energy use—when estimating operational energy use before and after renovation. This paper examines the influence of the energy performance gap on the comparative LCA between unrenovated and renovated buildings. First, a statistical correction model, based on a recent large-scale Flemish study, is developed to correct regulatory calculated energy use for space heating and domestic hot water in a pragmatic way. Subsequently, the model is applied to four single-family dwellings with different energy characteristics that underwent renovation in accordance with Flemish energy regulations. The results show that the anticipated environmental savings over a 60-year study period decrease significantly when the correction model is applied, reducing the estimated savings of 49–80% to 21–49%. Moreover, environmental payback times increase from 2.9–9.1 years to 10.4–22.5 years. Notably, neglecting the energy performance gap in LCAs leads to systematic underestimations of the material use significance. This research underscores the importance of integrating the energy performance gap into LCAs to obtain more accurate estimations of the environmental benefits of energy renovations.

The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset

With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound. Deviating from the conventional approach, in this investigation, we have employed the classification Read-Across Structure-Activity Relationship (c-RASAR), which involves the amalgamation of the concepts of classification-based quantitative structure-activity relationship (QSAR) and Read-Across to incorporate Read-Across-derived similarity and error-based descriptors into a statistical and machine learning modeling framework. ML models developed from these RASAR descriptors use similarity-based information from the close source neighbors of a particular query compound. We have employed different classification modeling algorithms on the selected QSAR and RASAR descriptors to develop predictive models for efficient prediction of query compounds’ hepatotoxicity. The predictivity of each of these models was evaluated on a large number of test set compounds. The best-performing model was also used to screen a true external data set. The concepts of explainable AI (XAI) coupled with Read-Across were used to interpret the contributions of the RASAR descriptors in the best c-RASAR model and to explain the chemical diversity in the dataset. The application of various unsupervised dimensionality reduction techniques like t-SNE and UMAP and the supervised ARKA framework showed the usefulness of the RASAR descriptors over the selected QSAR descriptors in their ability to group similar compounds, enhancing the modelability of the dataset and efficiently identifying activity cliffs. Furthermore, the activity cliffs were also identified from Read-Across by observing the nature of compounds constituting the nearest neighbors for a particular query compound. On comparing our simple linear c-RASAR model with the previously reported models developed using the same dataset derived from the US FDA Orange Book (https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm), it was observed that our model is simple, reproducible, transferable, and highly predictive. The performance of the LDA c-RASAR model on the true external set supersedes that of the previously reported work. Therefore, the present simple LDA c-RASAR model can efficiently be used to predict the hepatotoxicity of query chemicals.

A theoretical study of a radiofrequency wave propagation through a realistic turbulent marine atmospheric boundary layer based on large eddy simulation

This study aims at modeling and investigating the impact of realistic turbulence inhomogeneity on radiowave propagation by utilizing large eddy simulations (LES). An up-to-date version of the X-LES method for generating realistic turbulent phase screens is first introduced. It combines atmospheric simulations with the classical Tatarski statistical modeling. This method naturally incorporates vertical turbulence inhomogeneity into phase screens at scales resolved by LES. It is then extended to sub-grid scales by weighting statistically generated phase variations with the vertical profile of the turbulent structure constant extracted from atmospheric data. This method is applied to replicate turbulence of a classical tropical marine atmospheric boundary layer. The impact of the generated medium on the propagation of a 10 GHz spherical wave emanating from a Gaussian aperture is analyzed through a statistical study of log-amplitude profiles performed for three different source altitudes. Results first show that contrary to a classical homogeneous turbulence modeling, log-amplitude profiles resulting from the propagation into inhomogeneous turbulence exhibit a statistical heterogeneity strongly dependent of source altitude. Furthermore, classical stochastic phase screen generation from a homogeneous Von-Kàrmàn Kolmogorov spectrum seems to give a statistically significant underestimation of the actual impact of turbulence compared to the X-LES method.

Enhanced Tobacco Yield Prediction Using Spatial Information and Exogenous Variable-driven Machine Learning Models

Remote sensing technology has been essential in studying the relationship between tobacco canopy spectral characteristics and biomass yield. This study has been conducted in Garnepudi, Andhra Pradesh, employed satellite imagery obtained between 2015 and 2023 to extract vegetation indices (VI’s). Accurately predicting yield is crucial for India's economy. This study investigates the efficacy of various predictive models for tobacco yield forecasting using multiple vegetation indices: Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI) and Leaf Surface Water Index (LSWI). The models assessed include traditional parametric approaches (ARIMAX, MLR), machine learning techniques (ANN, SVR, RFR), and advanced ensemble methods like XGBoost. The results highlight XGBoost as the most accurate model, consistently delivering the lowest error metrics, including RMSE and MAE, across all vegetation indices. Specifically, XGBoost achieved the best performance with LAI showing RMSE of 86.657, MAE of 58.324, sMAPE of 14.354, MASE of 1.001, and QL of 29.162 respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in tobacco yield prediction. This study highlights the significant role of remote sensing technology in capturing crop development patterns and accurately forecasting tobacco yield, thereby offering valuable insights for agricultural planning and decision-making. The study also addresses challenges such as data quality and model generalization, providing a comprehensive view of the research impact and future directions.

An Ultramicroporous Zinc‐Based Zeolitic Imidazolate Framework‐8 for the Adsorption of CO2, CH4, CO, N2 and H2: A Combined Experimental and Theoretical Study

AbstractAn alternative synthesis route to obtain ultramicroporous zeolitic imidazolate framework‐8 (ZIF‐8) is reported that is rapid and does not require organic solvent or heating. The polyethylene glycol‐templated ZIF‐8 nanoparticles (<50 nm size) exhibited a high BET (Brunauer, Emmett and Teller) surface area of 1853 m2/g, a total pore volume of 0.73 cm3/g, and 0.54 nm ultramicropores. A new approach is introduced here to better understand gas adsorption in porous metal organic frameworks (MOFs) that combines theoretical and experimental isotherm data obtained for five gases with statistical physics modelling. The multiscale analytical model reveals that the gas molecules, irrespective of their structure, adsorb in a mixed orientation at low temperature, and a parallel multi‐molecular orientation at higher temperature. The number of gas molecules adsorbed per site ranged from 0.83–2.2 while the number of adsorbent layers ranged between 1.4–5.6, depending on the gas and the temperature. The multilayer adsorption processes involved adsorption energies from 2.85 kJ/mol–9.77 kJ/mol for all gases, consistent with physisorption via van der Waals and London dispersion forces. The initial adsorption energies were higher and therefore stronger. A particularly high capacity for CO2 of 1088 mg(CO2)/g at 298 K was observed while H2 adsorption was only 9 mg(H2)/g. The other gases adsorbed at 145 mg/g–245 mg/g at 298 K. Thermodynamic functions that governed the adsorption process such as the internal energy, the enthalpy, and Gibbs free energy, are also reported, as well as the adsorption entropies, for the 5 gases.

Robust safety monitoring and signal detection using alternatives to the standard poisson distribution

ABSTRACT Proper and timely characterization of the safety profile of a pharmaceutical product under development is imperative for assessing the overall benefit-risk relationship of the product and for making key development decisions. For ongoing clinical development, a comprehensive and robust safety monitoring and safety signal detection program which is based upon quantitative statistical reasoning is critical. Methods presented here can be applied to safety signal detection and periodic safety monitoring. Various statistical properties, distributions, and models, all utilizing a Bayesian framework are considered and further examined in order to identify robust methods applicable to a broad set of scenarios and situations. Methods developed for incidence counts (including those with under-dispersed distributions) with variable time-at-risk and with underlying constant or non-constant hazard rates, are proposed and compared to traditional methods designed to assess adverse event incidence rates or binomial incidence proportions (which assume an underlying constant hazard rate and subsequent Poisson distribution for modeling event counts).

A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system

ABSTRACT Road accidents are an inevitable aspect of daily life, and predicting crashes is crucial for minimizing disruptions and advancing intelligent transportation technologies. This study aims to design an ensemble fusion decision system using various base classifiers and a meta-classifier to improve crash prediction efficiency within the driver-vehicle-environment system. We adopted a data-driven strategy to analyze four categories of features—driver demographics, vehicle telemetry, driver inputs, and environmental conditions—collected from a driving simulator. Optimized modeling strategies using AdaBoost, XGBoost, GBM, LightGBM, and CatBoost were implemented. Moreover, statistical logit models were also used to assess the likelihood of crashes and the correlations among key variables. Furthermore, three resampling strategies, SMOTE-TL, SMOTE-ENN, and ADASYN, were employed to address class imbalance. The best performance was achieved with GBM, XGBoost, and AdaBoost as base classifiers, SMOTE-TL for balancing, and CatBoost as the meta-classifier, with 89.78% precision, 95.69% recall, and 92.64% F1-score.

Development of Econometric Models for Financial Performance Forecasting in Companies

This study develops an econometric model to predict corporate financial performance. The goal is to improve the accuracy of predictions by analysing relevant economic and financial variables. The model combines statistical and econometric analysis to identify significant input variables such as asset turnover, firm size, capital structure, and liquidity. The study also highlights the importance of external factors, such as environmental policies and knowledge management, that affect corporate financial performance. Using ARIMA and VAR models, the study shows that selecting the correct parameters, such as the number of lags, is critical to improving prediction accuracy. The developed model is evaluated based on RMSE, MAD, and MAPE metrics, which show that the econometric model offers more accurate predictions than the classical statistical model. These results contribute significantly to understanding corporate financial performance dynamics and can be a reliable tool in strategic decision-making across various industry sectors.

Photonic probabilistic machine learning using quantum vacuum noise

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element – a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~1 Gbps and energy consumption of ~5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.

Pre-Reconstruction Processing with the Cycle-Consist Generative Adversarial Network Combined with Attention Gate to Improve Image Quality in Digital Breast Tomosynthesis.

The current study proposed and evaluated "residual squeeze and excitation attention gate" (rSEAG), a novel network that can improve image quality by reducing distortion attributed to artifacts. This method was established by modifying the Cycle Generative Adversarial Network (cycleGAN)-based generator network using projection data for pre-reconstruction processing in digital breast tomosynthesis. Residual squeeze and excitation were installed in the bridge of the generator network, and the attention gate was installed in the skip connection between the encoder and decoder. Based on the radiation dose index (exposure index and division index) incident on the detector, the cases approved by the ethics committee and used for the study were classified as reference (675 projection images) and object (675 projection images). For the cases, unsupervised data containing a mixture of cases with and without masses were used. The cases were trained using cycleGAN with rSEAG and the conventional networks (ResUNet and U-Net). For testing, predictive processing was performed on cases (60 projection images) that were not used for learning. Images were generated using filtered backprojection reconstruction (kernel: Ramachandran and Lakshminarayanan) from projection data for testing data and without pre-reconstruction processing data (evaluation: in-focus plane). The distortion was evaluated using perception-based image quality evaluation (PIQE) analysis, texture analysis (feature: "Homogeneity" and "Contrast"), and a statistical model with a Gumbel distribution. PIQE has a low rSEAG value. Texture analysis showed that rSEAG and a network without cycleGAN were similar in terms of the "Contrast" feature. In dense breasts, ResUNet had the lowest "Contrast" feature and U-Net had differences between cases. The maximal variations in the Gumbel plot, rSEAG reduced the high-frequency ripple artifacts. In this study, rSEAG could improve distortion and reduce ripple artifacts.

On statistical evaluation of reverse degree based topological indices for iron telluride networks

In the context of graph theory and chemical graph theory, this research conducts a detailed mathematical investigation of reverse topological indices as they relate to iron telluride networks, clarifying their complex interactions. Graph theory is a branch of abstract mathematics that carefully studies the connections and structural features of graphs made up of edges and vertices. These theoretical ideas are expanded upon in chemical graph theory, which models molecular architectures with atoms acting as vertices and chemical bonds as edges. By extending these concepts, this work investigates the reverse topological indices in the context of Iron Telluride networks and outlines their significant effects on chemical reactivity, molecular topology and statistical modeling. By navigating intricate mathematical formalisms and algorithmic approaches, the analysis provides profound insights into the reactivity patterns and structural dynamics of Iron Telluride compounds, enhancing our knowledge of solid-state chemistry and materials science.

Time-variations of wave energy and forecasting power availability at a site in Fiji using time-series, regression and ANN techniques

ABSTRACT Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco-friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness-of-fit ( R 2 ) value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved.