Statistical Performance Research Articles

‘I can’t believe I just made history’: A temporal analysis of sports media reporting

Professional sport is a central element of our daily entertainment that contributes to shaping us individually and bonding us collectively: it provides us with shared ‘historic’ moments. This article is interested in these moments, and how the field of sports generates them, by asking the following questions: (1) has the frequency of ‘historic moments’ changed over time, and (2) is the way we make sports history consistent throughout the years? We conducted a temporal analysis of newspaper and magazine articles ( n = 1062) published in France (Le Monde, l’Équipe) and in the United States (USA Today, Sports Illustrated) during three time periods in the 21st century (2003, 2010, 2019). Our results show that: (1) as time passes, ‘historic’ moments occur more frequently; (2) sporting history is increasingly linked to social dimensions; and (3) statistical performances continue to mark history above all else. Although performance-based achievements are consistently celebrated, sporting history cannot be separated from our collective social existence, and the currents therein. We show that the act of making sports history is also bound to the normative, social, and cultural history of a society.

Algorithms to retrieve the spectral diffuse attenuation coefficient of light in the ocean from remote sensing.

We recently found a significant bias between spectral diffuse attenuation coefficient (Kd(λ)) retrievals by common ocean color algorithms and measurements from profiling floats [Remote. Sens.14, 4500 (2022)10.3390/rs14184500]. Here we show, using a multi-satellite match-up dataset, that the bias is markedly reduced by simple "tuning" of the algorithm's empirical coefficients. However, while the float dataset encompasses a larger proportion of the ocean's variability than previously used datasets, it does not cover the whole range of variability of observed remote sensing reflectance (Rrs). Thus, using algorithms tuned to this more comprehensive dataset may still result in a temporal and/or geographical bias in global application. To address this generalization issue, we evaluated a variety of analytical algorithms based on radiative transfer theory and settled on a specific one. This algorithm computes Kd(λ) from inherent optical properties (IOPs) obtained from an Rrs inversion and information about the angular distribution of the radiance transmitted through the air/ocean interface. The resulting Kd(λ) estimates at 412 and 490 nm were not appreciably biased against the float measurements. Evaluation using other in-situ datasets and radiative transfer simulations was also satisfactory. Statistical performance was good in both clear and turbid waters. Further work should be conducted to examine whether the tuned algorithms and/or the new analytical algorithm demonstrate adequate hyperspectral performance.

Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein

B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf.

Bioinformatic analysis of related immune cell infiltration and key genes in the progression of osteonecrosis of the femoral head.

Osteonecrosis of the femoral head (ONFH) is a common orthopedic condition that will prompt joint dysfunction, significantly impacting patients' quality of life. However, the specific pathogenic mechanisms underlying this disease remain elusive. The objective of this study is to examine the differentially expressed messenger RNAs (DE mRNAs) and key genes linked to ONFH, concurrently investigating the immune cell infiltration features in ONFH patients through the application of the CIBERSORT algorithm. Microarray was applied to scrutinize mRNA expression profiles in both ONFH patients and healthy controls, with data integration sourced from the GEO database. DE mRNAs were screened using the Limma method. The biological functions of DE mRNAs were explored through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) functional analysis, and Gene Set Enrichment Analysis (GSEA). Additionally, support vector machine-recursive feature elimination (SVM-RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to discern diagnostic biomarkers associated with the disease. Receiver operating characteristic (ROC) analysis was utilized to assess the statistical performance of the feature genes. The validation of key genes was performed using qRT-PCR in bone tissues obtained from ONFH patients and healthy controls. Osteogenic differentiation of BMSC was then performed and detected by alkaline phosphatase staining (ALP) and qRT-PCR to verify the correlation between key genes and osteogenic differentiation. Finally, immune cell infiltration analysis was executed to evaluate immune cell dysregulation in ONFH, concurrently exploring the correlation between the infiltration of immune cells and key genes. After consolidating the datasets, the Limma method revealed 107 DEGs, comprising 76 downregulated and 31 upregulated genes. Enrichment analysis revealed close associations of these DE mRNAs with functions such as cell migration, osteoblast differentiation, cartilage development and extracellular region. Machine learning algorithms further identified APOD, FBXO43 and LRP12 as key genes. ROC curves demonstrated the high diagnostic efficacy of these genes. The results of qRT-PCR showed that the expression levels of key genes were consistent with those of microarray analysis. In addition, the results of in vitro experiments showed that APOD was closely related to osteogenic differentiation of BMSC. Immune infiltration analysis suggested a close correlation between ONFH and imbalances in levels of Neutrophils, Monocytes, Macrophages M2, Dendritic cells activated and Dendritic cells resting. APOD is closely related to osteogenic differentiation of BMSCs and can be used as a diagnostic marker of ONFH. Immune cell infiltration significantly differs between controls and ONFH patients.

A hybrid machine-learning model for solar irradiance forecasting

Abstract Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power output. As the generation capacity within the electric grid increases, accurately predicting this output becomes increasingly essential, especially given the random and non-linear characteristics of solar irradiance under variable weather conditions. This study presents a novel prediction method for solar irradiance, which is directly in correlation with PV power output, targeting both short-term and medium-term forecast horizons. Our proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression model. The proposed method excels in forecasting solar irradiance, especially during highly intermittent weather periods. A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable framework. We evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford, USA and compared it against three forecasting models: persistence, modified 24-hour persistence and least squares. Based on three widely accepted statistical performance metrics (root mean squared error, mean absolute error and coefficient of determination), our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

Population-Based Validation of the MIA and MSKCC Tools for Predicting Sentinel Lymph Node Status

Patients with melanoma are selected for sentinel lymph node biopsy (SLNB) based on their risk of a positive SLN. To improve selection, the Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute Australia (MIA) developed predictive models, but the utility of these models remains to be tested. To determine the clinical utility of the MIA and MSKCC models. This was a population-based comparative effectiveness research study including 10 089 consecutive patients with cutaneous melanoma undergoing SLNB from the Swedish Melanoma Registry from January 2007 to December 2021. Data were analyzed from May to August 2023. The predicted probability of SLN positivity was calculated using the MSKCC model and a limited MIA model (using mitotic rate as absent/present instead of count/mm2 and excluding the optional variable lymphovascular invasion) for each patient. The operating characteristics of the models were assessed and compared. The clinical utility of each model was assessed using decision curve analysis and compared with a strategy of performing SLNB on all patients. Among 10 089 included patients, the median (IQR) age was 64.0 (52.0-73.0) years, and 5340 (52.9%) were male. The median Breslow thickness was 1.8 mm, and 1802 patients (17.9%) had a positive SLN. Both models were well calibrated across the full range of predicted probabilities and had similar external area under the receiver operating characteristic curves (AUC; MSKCC: 70.8%; 95% CI, 69.5-72.1 and limited MIA: 69.7%; 95% CI, 68.4-71.1). At a risk threshold of 5%, decision curve analysis indicated no added net benefit for either model compared to performing SLNB for all patients. At risk thresholds of 10% or higher, both models added net benefit compared to SLNB for all patients. The greatest benefit was observed in patients with T2 melanomas using a threshold of 10%; in that setting, the use of the nomograms led to a net reduction of 8 avoidable SLNBs per 100 patients for the MSKCC nomogram and 7 per 100 patients for the limited MIA nomogram compared to a strategy of SLNB for all. This study confirmed the statistical performance of both the MSKCC and limited MIA models in a large, nationally representative data set. However, decision curve analysis demonstrated that using the models only improved selection for SLNB compared to biopsy in all patients when a risk threshold of at least 7% was used, with the greatest benefit seen for T2 melanomas at a threshold of 10%. Care should be taken when using these nomograms to guide selection for SLNB at the lowest thresholds.

Quantitative Structure-Permittivity Relationship Study of a Series of Polymers.

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.

Nearly Zero-Energy Building Load Forecasts through the Competition of Four Machine Learning Techniques

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.

Comparison of the new-Poisoning Mortality Score and the Modified Early Warning Score for predicting in-hospital mortality in patients with acute poisoning

Introduction The evaluation of acute poisoning is challenging due to varied toxic substances and clinical presentations. The new-Poisoning Mortality Score was recently developed to assess patients with acute poisoning and showed good performance in predicting in-hospital mortality. The objective of this study is to externally validate the performance of the new-Poisoning Mortality Score and to compare it with the Modified Early Warning Score. Methods This retrospective analysis used data from the 2019–2020 Injury Surveillance Cohort, established by the Korea Center for Disease Control and Prevention, to perform external validation of the new-Poisoning Mortality Score. The statistical performances of the new-Poisoning Mortality and Modified Early Warning Scores were assessed and compared in terms of discrimination and calibration. Discrimination analysis involved metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. For calibration analysis, the Hosmer-Lemeshow goodness-of-fit test was utilized and calibration curves for each score were generated to elucidate the relationship between observed and predicted mortalities. Results This study analysed 16,570 patients with acute poisoning. Significant differences were observed between survivors and those who died in-hospital, including age, sex, and vital signs. The new-Poisoning Mortality Score showed better performance over the Modified Early Warning Score in predicting in-hospital mortality, in terms of the area under the receiver operating characteristic curve (0.947 versus 0.800), sensitivity (0.863 versus 0.667), specificity (0.912 versus 0.817), and accuracy (0.911 versus 0.814). When evaluated through calibration curves, the new-Poisoning Mortality Score showed better concordance between predicted and observed mortalities. In subgroup analyses, the score system consistently showed strong performance, excelling particularly in substances with high mortality indices and remaining superior in all substances as a group. Conclusions Our study has helped to validate the new-Poisoning Mortality Score as an effective tool for predicting in-hospital mortality in patients with acute poisoning in the emergency department. The score system demonstrated superior performance over the Modified Early Warning Score in various metrics. Our findings suggest that the new-Poisoning Mortality Score can contribute to the enhancement of clinical decision-making and patient management.

Examining the Impact of Statistical Features on Grade 11 Students' Performance in Statistics Using Calculators

This research investigates how feature characteristics affect the academic achievement of Grade 11 students in the field of statistics, with a particular emphasis on the role played by the use of calculators. The control group utilized various scientific calculators that are typically employed in statistics classes. The experimental group utilized the statistical functionalities of the advanced calculator. The objective of this study is to ascertain the impact of statistical characteristics of calculators on the academic achievement of grade 11 students in the field of statistics. The study aimed to determine whether there was a notable disparity between the pretest scores of the two groups. Is there a substantial disparity between the pretest and posttest scores of the groups? Is there a substantial disparity in the gain scores between the two groups? Furthermore, provides a comprehensive assessment of the study. The study's findings indicate a notable disparity in the utilization of various statistical functions on calculators. The experimental group demonstrated superior performance and adeptly utilized the statistical aspects of advanced calculators. The students in the Control group had challenges when attempting to solve the z-test and p-value. The statistical functionality of advanced calculators becomes valuable in advanced statistical topics.

Comparative Evaluation of Machine Learning Models for Subtyping Triple-Negative Breast Cancer: A Deep Learning-Based Multi-Omics Data Integration Approach.

Objective: Triple-negative breast cancer (TNBC) poses significant diagnostic challenges due to its aggressive nature. This research develops an innovative deep learning (DL) model based on the latest multi-omics data to enhance the accuracy of TNBC subtype and prognosis prediction. The study focuses on addressing the constraints of prior studies by showcasing a model with substantial advancements in data integration, statistical performance, and algorithmic optimization. Methods: Breast cancer-related molecular characteristic data, including mRNA, miRNA, gene mutations, DNA methylation, and magnetic resonance imaging (MRI) images, were retrieved from the TCGA and TCIA databases. This study not only compared single-omics with multi-omics machine learning models but also applied Bayesian optimization to innovatively optimize the neural network structure of a DL model for multi-omics data. Results: The DL model for multi-omics data significantly outperformed single-omics models in subtype prediction, achieving a 98.0% accuracy in cross-validation, 97.0% in the validation set, and 91.0% in an external test set. Additionally, the MRI radiomics model showed promising performance, especially with the training set; however, a decrease in performance during transfer testing underscored the advantages of the DL model for multi-omics data in data consistency and digital processing. Conclusion: Our multi-omics DL model presents notable innovations in statistical performance and transfer learning capability, bearing significant clinical relevance for TNBC classification and prognosis prediction. While the MRI radiomics model proved effective, it requires further optimization for cross-dataset application to enhance accuracy and consistency. Our findings offer new insights into improving TNBC classification and prognosis through multi-omics data and DL algorithms.

Analysis of the Alignment of K to 12 Most Essential Learning Competencies, Instruction, and Assessment Tools in Statistics and Probability

Curriculum alignment is vital in realizing educational goals. However, predicaments in the teaching and learning process challenge the alignment. Hence, this paper aims to determine students' academic performances in the years 2022-2023 and 2023-2024, analyze the alignment of the curriculum, instruction, and assessment in Statistics and Probability, and propose recommendations to ensure the alignment of curriculum, instruction, and assessment. A document analysis of the teaching and learning tools was conducted to gain in-depth results. Descriptive statistics, such as frequency, percent, mean, and standard deviation, were employed to describe further the results. Results showed that the students’ average academic performance in Statistics and Probability for 2022-2023 was 86.80 and 87.46 for 2023-2024, both indicating a very satisfactory level. The teacher’s instructions in the six (6) contents of Statistics and Probability were aligned with the curriculum or the K to 12 Most Essential Learning Competencies. Assessment tools were aligned with the teacher’s instructions or strategies. The 21st-century skills commonly integrated among learners were critical thinking, information, media, and technology. As the teacher’s instructions indicated, students gained an average mastery level (57%). The most common cognitive domain used in the students’ assessments was remembering. Findings revealed that the teacher attained good proof of transfer, lesson outlines, and assessments or graded activities aligning curriculum, instruction, and assessment. The teacher obtained an excellent level of objectives. Overall, the teacher aligned the curriculum, instruction, and assessment well. Based on the document analysis, the alignment of teaching and learning resources to the curriculum was evident. This study proposed the following recommendations - implement assessment of standards-based curriculum, effective pedagogy, and achievable assessments, provide teacher support, strengthen evaluation of teaching and assessment, and contextualize assessments catering to the 21st-century skills of the learners.

Foliar Uptake Models for Biocides: Testing Practical Identifiability of Diffusion-Based Models

With the expanding global population and diminishing resources, the imperative of ensuring sufficient food production becomes increasingly urgent. It is pivotal to develop safer biocides to enhance crop yields and address the escalating demands for food. Mathematical models are essential for understanding and characterizing the dynamic behaviour of complex biological systems. This work focuses on the development and statistical validation of a model for the description of biocide uptake through the leaves of plants. The systematic modelling strategy applied follows the steps: 1) formulation of candidate models; 2) conduction of identifiability tests to verify that model parameters can be estimated from observations; 3) selection of the best model based on its statistical performance in representing the experimental observations; 4) design of experiments for improving the precision of parameter estimates from data; 5) statistical validation of the final model. This paper presents a diffusion-based model for foliar uptake and a study on the practical identifiability of the model parameters (steps 1 and 2 of the procedure outlined). These results will guide further model-driven experimentation in the context of foliar uptake of pesticides.

Equivalent analysis of different estimations under a multivariate general linear model

<p>This article explores the mathematical and statistical performances and connections of the two well-known ordinary least-squares estimators (OLSEs) and best linear unbiased estimators (BLUEs) of unknown parameter matrices in the context of a multivariate general linear model (MGLM) for regression, both of which are defined under two different optimality criteria. Tian and Zhang <sup>[<xref ref-type="bibr" rid="b38">38</xref>]</sup> once collected a series of existing and novel identifying conditions for OLSEs to be BLUEs under general linear models: <italic>On connections among OLSEs and BLUEs of whole and partial parameters under a general linear model, Stat. Probabil. Lett., 112 (2016), 105–112</italic>. In this paper, we show how to extend this kind of results to multivariate general linear models. We shall give a direct algebraic procedure to derive explicit formulas for calculating the OLSEs and BLUEs of parameter spaces in a given MGLM, discuss the relationships between OLSEs and BLUEs of parameter matrices in the MGLM, establish many algebraic equalities related to the equivalence of OLSEs and BLUEs, and give various intrinsic statistical interpretations about the equivalence of OLSEs and BLUEs of parameter matrices in a given MGLM using some matrix analysis tools concerning ranks, ranges, and generalized inverses of matrices.</p>

Effects of K-W-L Instructional Strategy on Academic Performance of Pupils in Primary Mathematics and English in Owerri North Lga of Imo State, Nigeria

The purpose of this study was to find out if learners taught algebraic expression/elementary statistics in Mathematics and parts of speech in English Language using K-W-L instructional strategy will have better academic performance than those taught with the conventional Teacher method at 0.05 alpha levels. The researchers used quasi-experimental design which adopted the pretest, post-test Experimental group method. The area of study was Owerri North LGA Imo State, Nigeria. The population of the study was all Primary 5 learners in Owerri North Public schools. Purposive sampling technique was used to select three primary schools and simple random sampling technique was used to select classes in each of the schools. The sample of the study was 120 primary learners. The data collection instruments for the study were treatment 1 (K-W-L instructional strategies), treatment 2, (TCM [Teacher conventional method), Treatment 3 (combined TCM & K-W-L), multiple-choice questions for each subject chosen from past FSLCE and Common entrance questions. Two research questions and one null hypothesis guided the study. Mean, standard deviation and ANCOVA were the statistical tools used to analyze the data. The findings of the study revealed that the group taught with combined TCM & K-W-L achieved higher academically than the group taught with only TCM or K-W-L. The study also discovered that K-W-L instructional strategy is gender friendly. It was recommended that primary school teachers should be encouraged to use K-W-L instructional strategy to facilitate learners’ performance in algebraic expression, elementary statistics and parts of speech. Furthermore Basic Education Stake holders should organize regular workshops and seminars to train and retrain teachers on the use of 21st century teaching strategies.

Optimization of processing technology of bark of Ilicis Rotundae Cortex based on quality by design concept

Based on the concept of quality by design(QbD), this study optimized the processing technology of Ilicis Rotundae Cortex. According to the processing method and ingredient requirements of Ilicis Rotundae Cortex in the Chinese Pharmacopoeia, the content of syringin and pedunculoside, alcohol extract, fragmentation rate, and moisture content were taken as the critical quality attributes(CQAs). The soaking time, moistening time, and drying time were taken as critical process parameters(CPPs) by single factor tests. The weight coefficients of CQAs were determined by the analytic hierarchy process(AHP)-entropy weighting method, and the comprehensive score was calculated. With the comprehensive score as the response value, Box-Behnken design was employed to establish a mathematical model between CPPs and CQAs, and the design space for the processing of Ilicis Rotundae Cortex was built and verified. The results of ANOVA showed that the mathematical model had the P value below 0.05, the lack of fit greater than 0.05, adjusted R~2=0.910 5, and predicted R~2=0.831 0, which indicated that the proposed model had statistical significance and good prediction performance. Considering the factors in production, the best processing conditions of Ilicis Rotundae Cortex were decoction pieces of about 1 cm soaking for 1 h, moistening for 4 h, and drying at 60-70 ℃ in a blast drier for 2 h. The optimized processing technology of Ilicis Rotundae Cortex was stable and feasible, which can provide a reference for the standardized preparation and stable quality of Ilicis Rotundae Cortex.

Statistical comparison of Path-Complete Lyapunov Functions: a Discrete-Event Systems perspective

The goal of this paper is to advertise the tight links between the theory of Discrete-Event Systems and Path-Complete Lyapunov Functions. These are algebro-combinatorial stability criteria for hybrid systems, whose meta-parameters are an automaton and a template of candidate Lyapunov functions.To do this, we analyse a phenomenon recently observed in the literature, namely the statistical ordering of Path-Complete Lyapunov Functions, which is by far not well understood yet: Path-Complete Lyapunov functions can be endowed with a preorder structure with respect to their performance. It has been recently shown that the preorder corresponding to relative worst case performance can be characterized with tools from automata theory, but the ordering corresponding to statistical relative performance has remained elusive. We advocate for a Discrete-Event Systems approach to this problem and provide preliminary results in this direction.

Noisy Tensor Completion via Low-Rank Tensor Ring.

Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restrictive in practice. To remedy such drawback, this article proposes a novel noisy tensor completion model, which complements the incompetence of existing works in handling the degeneration of high-order and noisy observations. Specifically, the tensor ring nuclear norm (TRNN) and least-squares estimator are adopted to regularize the underlying tensor and the observed entries, respectively. In addition, a nonasymptotic upper bound of estimation error is provided to depict the statistical performance of the proposed estimator. Two efficient algorithms are developed to solve the optimization problem with convergence guarantee, one of which is specially tailored to handle large-scale tensors by replacing the minimization of TRNN of the original tensor equivalently with that of a much smaller one in a heterogeneous tensor decomposition framework. Experimental results on both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed model in recovering noisy incomplete tensor data compared with state-of-the-art tensor completion models.

Reduced order infinite impulse response system identification using manta ray foraging optimization

This article presents a useful application of the Manta Ray Foraging Optimization (MRFO) algorithm for solving the adaptive infinite impulse response (IIR) system identification problem. The effectiveness of the proposed technique is validated on four benchmark IIR models for reduced order system identification. The stability of the proposed estimated IIR system is assured by incorporating a pole-finding and initialization routine in the search procedure of the MRFO algorithm and this algorithmic modification contributes to the MRFO algorithm when seeking stable IIR filter solutions. The absence of such a scheme, which is primarily the case with the majority of the recently published literature, may lead to the generation of an unstable IIR filter for unknown real-world instances (particularly when the estimation order increases). Experiments conducted in this study highlight that the proposed technique helps to achieve a stable filter even though large bounds for the design variables are considered. The convergence rate, robustness, and computational speed of MRFO for all the considered problems are investigated. The influence of the control parameters of MRFO on the design performances is evaluated to gain insight into the interaction between the three foraging strategies of the algorithm. Extensive statistical performance analyses employing various non-parametric hypothesis tests concerning the design consistency and convergence are conducted for comparison of the proposed MRFO-based approach with six other metaheuristic search procedures to investigate the efficiency. The results on the mean square error metric also highlight the improved solution quality of the proposed approach compared to the various techniques published in the literature.

Probabilistic models for harmful algae: application to the Norwegian coast

Abstract We have developed probabilistic models to estimate the likelihood of harmful algae presence and outbreaks along the Norwegian coast, which can help optimization of the national monitoring program and the planning of mitigation actions. We employ support vector machines to calibrate probabilistic models for estimating the presence and harmful abundance (HA) of eight toxic algae found along the Norwegian coast, including Alexandrium spp., Alexandrium tamarense, Dinophysis acuta, Dinophysis acuminata, Dinophysis norvegica, Pseudo-nitzschia spp., Protoceratium reticulatum, and Azadinium spinosum. The inputs are sea surface temperature, photosynthetically active radiation, mixed layer depth, and sea surface salinity. The probabilistic models are trained with data from 2006 to 2013 and tested with data from 2014 to 2019. The presence models demonstrate good statistical performance across all taxa, with R (observed presence frequency vs. predicted probability) ranging from 0.69 to 0.98 and root mean squared error ranging from 0.84% to 7.84%. Predicting the probability of HA is more challenging, and the HA models only reach skill with four taxa (Alexandrium spp., A. tamarense, D. acuta, and A. spinosum). There are large differences in seasonal and geographical variability and sensitivity to the model input of different taxa, which are presented and discussed. The models estimate geographical regions and periods with relatively higher risk of toxic species presence and HA, and might optimize the harmful algae monitoring. The method can be extended to other regions as it relies only on remote sensing and model data as input and running national programs of toxic algae monitoring.