Statistical Measures Research Articles

Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation.

Targeted low-throughput studies have previously identified subcellular RNA localization as necessary for cellular functions including polarization, and translocation. Furthermore, these studies link localization to RNA isoform expression, especially 3' Untranslated Region (UTR) regulation. The recent introduction of genome-wide spatial transcriptomics techniques enables the potential to test if subcellular localization is regulated in situ pervasively. In order to do this, robust statistical measures of subcellular localization and alternative poly-adenylation (APA) at single-cell resolution are needed. Developing a new statistical framework called SPRAWL, we detect extensive cell-type specific subcellular RNA localization regulation in the mouse brain and to a lesser extent mouse liver. We integrated SPRAWL with a new approach to measure cell-type specific regulation of alternative 3' UTR processing and detected examples of significant correlations between 3' UTR length and subcellular localization. Included examples, Timp3, Slc32a1, Cxcl14, and Nxph1 have subcellular localization in the mouse brain highly correlated with regulated 3' UTR processing that includes the use of unannotated, but highly conserved, 3' ends. Together, SPRAWL provides a statistical framework to integrate multi-omic single-cell resolved measurements of gene-isoform pairs to prioritize an otherwise impossibly large list of candidate functional 3' UTRs for functional prediction and study. In these studies of data from mice, SPRAWL predicts that 3' UTR regulation of subcellular localization may be more pervasive than currently known.

A novel trace-based sampling method for conformance checking

It is crucial for organizations to ensure that their business processes are executed accurately and comply with internal policies and requirements. Process mining is a discipline of data science that exploits business process execution data to analyze and improve business processes. It provides a data-driven approach to understanding how processes actually work in practice. Conformance checking is one of the three most relevant process mining tasks. It consists of determining the degree of correspondence or deviation between the expected (or modeled) behavior of a process vs the real one observed and revealed from the historical events recorded in an event log during the execution of each instance of the process. Under a big data scenario, traditional conformance checking methods struggle to analyzing the instances or traces in large event logs, increasing the associated computational cost. In this article, we study and address the conformance-checking task supported by a traces selection approach that uses representative sample data of the event log and thus reduces the processing time and computational cost without losing confidence in the obtained conformance value. As main contributions, we present a novel conformance checking method that (i) takes into account the data dispersion that exists in the event log data using a statistic measure, (ii) determines the size of the representative sample of the event log for the conformance checking task, and (iii) establishes selection criteria of traces based on the dispersion level. The method was validated and evaluated using fitness, precision, generalization, and processing time metrics by experiments on three actual event logs in the health domain and two synthetic event logs. The experimental evaluation and results revealed the effectiveness of our method in coping with the problem of conformance between a process model and its corresponding large event log.

Evaluating maternal death surveillance and response system in Sunyani Municipality of Bono region in Ghana from 2017-2021

IntroductionMaternal death rates in Ghana have decreased overall but remain high in rural areas. The Maternal Death Surveillance and Response System (MDSR) aims to eliminate preventable maternal deaths effectively. However, its effectiveness is less pronounced at district and subdistrict levels than at national and regional levels. Despite the requirement for periodic evaluation, there is a lack of evidence supporting these assessments. This study focuses on evaluating the MDSR in Sunyani Municipal.MethodsThe evaluation was conducted using a cross-sectional design, using both qualitative and quantitative approaches. Data from the District Health Information Management Systems (DHIMS) and the maternal death line list from January 2017 to December 2021 on maternal deaths were reviewed. Stakeholders, including health professionals, were interviewed. The means, proportions, and other statistical measures were calculated using Epi Info Version 7. Qualitative data underwent content analysis, and the results were visually presented in tables and flowcharts to illustrate the flow of surveillance information.ResultsMost respondents were female, constituting 51.4% (19/37), with midwives comprising the largest group at 32.4% (12/37). Approximately 75.7% (28/37) were able to explain how the surveillance data has been used for public health action. The notification and reporting process was clear to 85% (17/20) of respondents. Although 94.6% (35/37) expressed willingness to notify and participate in audits at facility and community levels, the audits remained facility-based. All facilities consistently reported maternal deaths, but data storage equipment was lacking. Approximately 80% (20/25) of forms were reported within 24 h, and 88% (22/25) were investigated within seven days. Only 68% (17/25) of forms were filled out correctly, with no municipal and facility-level line list. All reported deaths came from healthcare facilities, with no community-reported maternal deaths.ConclusionsThe MDSR system in Sunyani Municipal demonstrates high awareness and willingness to participate among healthcare providers but faces challenges in data accuracy and community engagement. Death audits remain facility-based, and the lack of community-reported maternal deaths and municipal-level line lists indicates gaps in comprehensive reporting and data management. To improve the system, it is recommended that community case searches be enhanced for better reporting and to address data management issues by ensuring proper data quality assessment.

Test-Retest Reliability of Two Computationally-Characterised Affective Bias Tasks

Affective biases are commonly seen in disorders such as depression and anxiety, where individuals may show attention towards and preferential processing of negative or threatening stimuli. Affective biases have been shown to change with effective intervention: randomized controlled trials into these biases and the mechanisms that underpin them may allow greater understanding of how interventions can be improved and their success be maximized. For such trials to be informative, we must have reliable ways of measuring affective bias over time, so we can detect how and whether they are altered by interventions: the test-retest reliability of our measures puts an upper bound on our ability to detect any changes. In this online study we therefore examined the test-retest reliability of two behavioural affective bias tasks (an ‘Ambiguous Midpoint’ and a ‘Go-Nogo’ task). 58 individuals recruited from the general population completed the tasks twice, with at least 14 days in between sessions. We analysed the reliability of both summary statistics and parameters from computational models using Pearson’s correlations and intra-class correlations. Standard summary statistic measures from these affective bias tasks had reliabilities ranging from 0.18 (poor) to 0.49 (moderate). Parameters from computational modelling of these tasks were in many cases less reliable than summary statistics. However, embedding the covariance between sessions within the generative modelling framework resulted in higher estimates of stability. We conclude that measures from these affective bias tasks are moderately reliable, but further work to improve the reliability of these tasks would improve still further our ability to draw inferences in randomized trials.

Spatio-temporal rainfall variability and trends using a Kriging-interpolation and Innovative trend analysis approach: the case of Wolaita zone, south Ethiopia

Climate change is one of the worst environmental issues, with a negative impact on most developing countries across the globe and in some regions, including Ethiopia. This study seeks to establish the temporal and spatial changes in rainfall for the period 1987–2021. In this study, ordinary statistical measures, such as the mean and coefficient of variation, precipitation concentration index (PCI), and standardized anomaly index (SAI), were applied to investigate the rainfall variation. Concerning the estimation of the spatiotemporal distribution and magnitude of changes in, non-parametric Mann-Kendall (MK) tests, Sen’s slope estimator, and innovative trend analysis (ITA) were also conducted in ArcGIS 10.8 environment and XLSTAT/R. The study showed significant fluctuations in rainfall in the Wolaita zone, with minimum mean annual rainfall in 1997 and maximum rainfall in 2003. All but the Belg season had more negative seasonal anomalies than positive ones. The annual rainfall for each of the AEZs in different parts of the country ranged from one another; it was 969.03 mm in a southeast lowland AEZ and 1648.75 mm in the northwest highland AEZ. Rainfall was not uniformly distributed throughout the year and study area; the highland AEZs received more rainfall in the Belg and Kermit seasons than in the lowland seasons. In the ordinary kriging results, the extent of variability in the CV of the mean annual rainfall for each zone was identifiable. The southwest lowland AEZ produced a CV of 24.22% with a decrease in rainfall amounts, and the northeast highland AEZ produced a CV of 31.63% of the rainfall distribution amounts. This area includes lowland and highland AEZs of the northeastern part of the study area’s rainfall, which is moderately distributed by PCI. This information is helpful when attempting to associate development and cropping systems with temporal and spatial climatic patterns with respect to rainfall for agro-climatological activities and projects or flood regulatory measures.

Study on Analysis of Variance in Statistics

Variance is a key statistical measure that quantifies the spread of values within a dataset, offering insights into data variability. It evaluates the average squared deviation of data points from the mean and serves as the foundation for statistical methods such as ANOVA and regression analysis. Introduced by Ronald Fisher in 1918, variance builds upon earlier concepts by Gauss and is calculated differently for populations and samples, using Bessel’s correction for unbiased estimation in samples. Unlike range, variance incorporates all data points, ensuring comparability across datasets of varying sizes. Its properties, including invariance to additive constants and scalability by squared factors, make it a robust metric for analyzing data spread. Variance is especially critical in experimental designs to distinguish between systematic (between-groups) and unsystematic (within-groups) variance, enabling researchers to assess and interpret variability in diverse contexts

Entropy as a Tool for the Analysis of Stock Market Efficiency During Periods of Crisis

In the article, we analyse the problem of the efficiency market hypothesis using entropy in moments of transition from a normal economic situation to crises or slowdowns in European, Asian and US stock markets and the economy in the years 2007–2023 (2008–2009, U.S. financial sector crises; 2020–2021, Pandemic period; and the 2022–2023 period of Russia’s attack on Ukraine). The following hypothesis is put forward in the article: In periods of economic slowdown and economic crises, the entropy of prices and return rates decreases. According to the principles of physics, in an isolated system, entropy increases and decreases at the moment of external intervention, similar to finance, where during crises and economic slowdowns, there is interference from governments introducing new regulations and intervening in financial markets. The article uses the Shannon entropy method. This measure, as a statistical measure, does not require the assumption of stationarity of time series or a known probability distribution, unlike classical statistical methods. Our results confirm decreased entropy in stock markets during crisis.

Be aware of overfitting by hyperparameter optimization!

Hyperparameter optimization is very frequently employed in machine learning. However, an optimization of a large space of parameters could result in overfitting of models. In recent studies on solubility prediction the authors collected seven thermodynamic and kinetic solubility datasets from different data sources. They used state-of-the-art graph-based methods and compared models developed for each dataset using different data cleaning protocols and hyperparameter optimization. In our study we showed that hyperparameter optimization did not always result in better models, possibly due to overfitting when using the same statistical measures. Similar results could be calculated using pre-set hyperparameters, reducing the computational effort by around 10,000 times. We also extended the previous analysis by adding a representation learning method based on Natural Language Processing of smiles called Transformer CNN. We show that across all analyzed sets using exactly the same protocol, Transformer CNN provided better results than graph-based methods for 26 out of 28 pairwise comparisons by using only a tiny fraction of time as compared to other methods. Last but not least we stressed the importance of comparing calculation results using exactly the same statistical measures.Scientific Contribution We showed that models with pre-optimized hyperparameters can suffer from overfitting and that using pre-set hyperparameters yields similar performances but four orders faster. Transformer CNN provided significantly higher accuracy compared to other investigated methods.

Modified Half-Cauchy Chen (MHCC) Distribution with Applications to Lifetime Dataset

In this article, we have recommended an innovative versatile distribution called Modified Half-Cauchy Chen distribution by modifying half-Cauchy Chen distribution. The recommended distribution's various properties are derived and analyzed. The recommended distribution's parameters are ascertained by applying the maximum likelihood estimation (MLE) approach. Furthermore, the performance of the Modified Half-Cauchy Chen distribution is compared against other distributions using various statistical measures. These measures consist of the Corrected Akaike Information Criterion (CAIC), the Kolmogorov-Smirnov (K-S) test, the Bayesian Information Criterion (BIC), and the Akaike Information Criterion (AIC). The results consistently demonstrate the superior fit of the Modified Half-Cauchy Chen distribution to the data. The goodness-of-fit analysis is carried out on a real data set in order to evaluate the innovative distribution's applicability. The Modified half-Cauchy Chen distribution is shown to perform better than a few other known distributions. R programming software is used to help with every computation.

Psychometric properties of the pain anxiety symptom scale among postoperative patients in Amol, Iran

IntroductionSo far, the psychometric properties of the Persian version of the Pain Anxiety Symptom Scale have not been assessed in Iran. Therefore, this study was conducted to determine the validity and reliability of the Persian version of the Pain Anxiety Symptom Scale among a group of Iranian patients in Amol.MethodsThis methodological study was conducted in 2023 with a sample of 400 postoperative patients from Amol, Iran, selected through convenience sampling. The dataset was divided into two groups of 200 for exploratory and confirmatory factor analyses. Construct validity was assessed using maximum likelihood exploratory factor analysis with Promax rotation, supported by Horn’s parallel analysis and network analysis to visualize item relationships. Confirmatory factor analysis, convergent and discriminant validity was performed on the second dataset. Reliability was evaluated through various statistical measures, including Cronbach’s alpha, McDonald’s omega, average inter-item correlation coefficient, composite reliability, and maximal reliability (MaxR).ResultsAmong the 400 participants, the mean age was 44.38 years (SD = 13.49), with 152 (46.1%) being women and 178 (53.9%) men. Most participants (n = 268, 81.2%) had an education level lower than a diploma, and 93 individuals (28.2%) reported a history of surgery. The results of exploratory factor analysis with Promax rotation developed two factors accounting for 66.29% of the variance comprising 15 items. Also, after necessary modifications during confirmatory factor analysis, the final model was approved. As for reliability, the Cronbach’s alpha, composite reliability, and MaxR for all constructs were greater than 0.7, demonstrating good internal consistency and construct reliability.ConclusionAccording to the results, the Persian version of the Pain Anxiety Symptom Scale has a valid structure and acceptable reliability. This scale can be used by health professionals in many ways.

PyPCG: a Python toolbox specialized for phonocardiography analysis.

Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. The development of methods which analyse phonocardiographic data tries to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. In these approaches there is little to no standardization and multiple parts of these models have to be reimplemented on a case-by-case basis. Datasets containing heart sound recordings also lack standardization in both data storage and labeling, especially in fetal phonocardiography. We are presenting a toolbox that can serve as a basis for a future standard framework for heart sound analysis. This toolbox contains some of the most widely used processing steps and with these, complex analysis pipelines can be created. These functions can be tested individually. Due to the interdependence of the steps, we validated the current segmentation stage using two phonocardiogram datasets, a fetal dataset comprising 50 one-minute abdominal PCG recordings, which include 6758 S1 and 6729 S2 labels and a filtered version of the dataset used in the 2022 PhysioNet Challenge, containing 413 records with 9795 S1 and 9761 S2 labels. Our results were compared to other common and publicly available segmentation methods, such as peak detection with the Neurokit2 library, and the Hidden Semi-Markov Model by Springer et al. Our best model achieved a 96.1% F1 score and 11.7 ms mean absolute error for fetal S1 detection, and 81.3% F1 score and 50.5 ms mean absolute error for PhysioNet S1 detection. Our detection method outperformed all other tested methods on the fetal dataset and
achieved results comparable to the state of the art on the PhysioNet dataset. Accurate segmentation of signals is critical for the calculation of accurate statistical measures and the creation of classification models. Our toolbox contains functions for both feature extraction and calculation of statistics which are compatible with the previous steps. All of our methods can be fine tuned for specific datasets. pyPCG is available on https://pypcg-toolbox.readthedocs.io/en/latest/.

Gestational Age-Specific Stillbirth Rates: Are We Using theRight Denominator?

Globally, a stillbirth occurs every 17 seconds totalling almost 2 million every year. The global standard for reporting stillbirths is the stillbirth rate. While a critical and accessible summary statistic, it masks gestational age-specific trends. This study aimed to summarise and critique stillbirth trends and document gestational age-specific rates and risk calculations according to three published methodologies: gestational age-specific stillbirth rate (GS-SBR), foetus-at-risk (FAR) stillbirth rate, and continuity corrected foetus-at-risk (ccFAR) stillbirth rate. We conducted a cross-sectional analysis of all births from the National Perinatal Data Collection in Australia (1998-2018). Stillbirth was defined as the birth of a baby with no sign of life from 20 weeks of gestation or weighing 400 grams at birth. Three statistical measures were applied reflecting alternate denominators: the GS-SBR measure used the number of births in a given week, the FAR used the number of foetuses in utero at the start of a week, and the ccFAR used the number of foetuses at the midpoint of the week. In Australia from 1998 to 2018, there were 39,576 stillbirths among 5.9 million births, an overall stillbirth rate of 6.7 per 1000 births. For each week of gestation (20-41+), the average GS-SBR varied from 1.0 to 790.0 stillbirths per 1000 births; FAR varied from 0.1 to 1.2 stillbirths per 1000 FAR; and ccFAR varied from 0.1 to 2.4 stillbirths per 1000 ongoing pregnancies. The three stillbirth rate calculations showed distinct trends during pregnancy, and only FAR and ccFAR reflected increased stillbirth risk as gestation advances. When calculating gestational-age-specific stillbirth rates, the FAR or ccFAR approaches provide the most accurate representation of the probability of stillbirth throughout pregnancy and better enable clinicians to act on risk.

Reporting and interpreting statistical results in veterinary medicine: Calling for change

AbstractUnderstanding and correctly interpreting statistical results presented in scientific articles is a required skill for practicing evidence‐based veterinary medicine. A prerequisite for doing so is the adequate reporting of the results in scientific journals. However, most authors of veterinary publications determine the importance of their findings based on statistical significance (ie, P < .05), indicating that neither the limitations of using P values for inference nor the existence of more appropriate alternatives are widely appreciated in veterinary medicine. This deficiency in knowledge indicates a need to increase awareness in veterinary medicine regarding reporting statistical measures that quantify the magnitude of an effect along with its level of uncertainty, and then interpreting these results for clinical decision making. We utilize a hypothetical randomized controlled trial of dietary management in cats with chronic kidney disease to discuss some common misconceptions about P values and provide practical suggestions for alternatives. Reporting appropriate effect estimates along with their confidence intervals will allow veterinarians to easily and correctly determine whether the magnitude of the effect of interest meets clinical needs while acknowledging uncertainty in the results. We also describe confidence interval functions and show their utility as visual tools in aiding interpretation of confidence intervals. By providing practical guidance, we show that a change in reporting and interpreting statistical results is feasible and necessary. We hope this crucial step will promote clinical decision making based on effect estimates and confidence intervals.

Parameter Estimation of Proton Exchange Membrane Fuel Cells Using Chaotic Newton-Raphson-Based Optimizer

This paper presents a novel improved metaheuristic algorithm, Chaotic Newton-Raphson-Based Optimizer (CNRBO), for the Proton Exchange Membrane Fuel Cells (PEMFCs) parameter extraction problem. In this study, ten distinct chaotic maps are integrated within the Newton-Raphson-based optimizer (NRBO) to improve its performance. The chaotic maps are employed to define the probability of the trap avoidance operator (TAO). The proposed CNRBO algorithm is validated using standard benchmark optimization problems. Results proved its advantages over the standard NRBO algorithm in terms of accuracy, robustness, and convergence speed. For PEMFC parameters extraction using the proposed CNRBO algorithm, the objective function to be minimized is the sum of squared errors (SSE) between estimated and measured stack voltages across various data points. To validate the effectiveness and reliability of the proposed CNRBO, its performance is compared with recent algorithms on eight different PEMFC stack models including NedStackPS6, BCS 500W, Horizon 500W, 250W stack, Avista SR-12 500W, Temasek 1KW, Ballard Mark V 5KW and Horizon H-1000XP stack. Statistical measures are employed to assess the superiority and robustness of the proposed CNRBO. Statistical analyses demonstrate that the proposed CNRBO algorithm outperforms existing algorithms in terms of accuracy, search capability, and convergence speed, solidifying its position as a powerful tool for PEMFC parameter estimation.

Using structural equation modeling and intima-media complex texture features to assess cardiovascular disease risk in the common carotid artery

The potential for stroke risk in humans due to clinical cardiovascular disease (CVD), which leads to the hardening of the artery walls (atherosclerosis), can be assessed by examining the common carotid artery (CCA) in ultrasound images. Specifically, this can be done by measuring the intima media thickness (IMT), which represents the thickness of the arteries wall, and by analyzing texture features (TFs) of the CCA's intima-media complex (IMC) of the artery wall. In this paper, a sample of 612 longitudinal-section ultrasound images of the left and the right CCA from 158 men and 148 women, out of which 42 demonstrated clinical CVD, is studied. All images are intensity normalized and despeckled, with the IMC segmented through an in-house system of semi-automated segmentation, where the IMT is measured, and 40 different TFs are extracted. Then, employing structural equation modeling (SEM), these TFs, which are put in 6 groups (constructs), are collectively tested on how they are related to CVD. The influence of the IMT is also studied through a moderation analysis, while gender and age of the sub-jects of the study are tested with regard to their control effect. The main conclusions of the study are as follows. The 6 TFs groups and the IMT fit the measurement model very well. Also, 6 hypothesized paths for the impact of each TFs group on CVD are tested in a structural model, with 5 of them, namely Statistical Features (SF), Spatial Gray Level Dependence Matrices (SGLDM), Gray Level Difference Statistics (GLDS), Statistical Feature Matrix (SFM) and Laws Texture Energy Measures (LTEM), impacting CVD in a moderate manner (p-values between .04 and .07). The IMT is shown to play a strong moderating role (p-values of Δχ2 < .01), enhancing the level of impact of the TFs on CVD. Gender and age have a strong controlling effect on CVD (p-values of < .01). The precision of the findings of this study is considerably improved compared to those previously reported, because of the very good model fit (e.g., normed fit index (NFI) = 0.94) for both measurement and structural models. However, due to the moderate nature of the impact, supplementary work is required for testing either additional combinations of TFs, as well as testing other samples through the proposed model, for further evaluation of the CVD risk on symptomatic subjects at risk of atherosclerosis.

Integrating geomechanical proxy models with data assimilation for energy transition applications

This study presents a method to address the significant uncertainties in subsurface modeling that impact the efficiency of energy transition applications such as geothermal energy extraction and CO2 geological storage. The approach combines a physics-based geomechanical proxy model with an ensemble smoother with multiple data assimilation (ES-MDA), aimed at enhancing uncertainty quantification through the integration of vertical displacement measurements from fluid production and injection. The data from wells is limited in spatial coverage, while these measurements offer extensive spatial information, improving the understanding of subsurface behavior by reflecting changes in reservoir pressure and temperature. The open-DARTS simulator for fluid flow and a geomechanical proxy are used to perform data assimilation with ES-MDA. By generating an ensemble of model realizations with varied permeability, calculating vertical displacements at the surface, and applying ES-MDA, we effectively identify the probability distribution of the vertical displacement of the model conditioned to observed subsidence data. Entropy is used as a statistical measure to quantify the reduction of uncertainty of subsurface models based on observations. Our approach was tested on a 2D conceptual and 3D realistic datasets, demonstrating its capability to provide data assimilation. This workflow represents an advancement in subsurface modeling, supporting informed decision-making in geothermal energy production and CO2 sequestration by offering an improved alternative for data assimilation and enhancing tools for uncertainty quantification.

Eficácia da ferramenta de progressão de arquivos climáticos CCWorldWeatherGen

Abstract This paper aimed to analyze the effectiveness of the CCWorldWeatherGen tool, focusing on climate change in São Paulo, São Paulo State, Brazil. For this, dry-bulb temperature, relative humidity, global solar radiation, and wind speed data from the test reference year weather file (1954) and the CCWorldWeatherGen file for the 2020 period (representing the 2011-2040 period) were compared with observational data collected between 2011 and 2023 by the Meteorological Station of the Institute of Astronomy, Geophysics, and Atmospheric Sciences of the University of São Paulo. The accuracy of variables predicted using weather files was evaluated using five statistical measures of error. Annual relative root mean square error (RRMSE) values for dry-bulb temperature, relative humidity, global solar radiation, and wind speed in the morphed weather file were 17.04% (good), 17.95% (good), 31.57% (poor), and 224.44% (poor), respectively. It is concluded that CCWorldWeatherGen is suitable for generating future weather files with complete information, mainly for its practicality. However, this approach requires caution, as sequences depend on the consistency of the weather file used as a basis.

Tipping detection using climate networks

The development of robust Early Warning Signals (EWSs) is necessary to quantify the risk of crossing tipping points in the present-day climate change. Classically, EWSs are statistical measures based on time series of climate state variables, without exploiting their spatial distribution. However, spatial information is crucial to identify the starting location of a transition process and can be directly inferred by satellite observations. By using complex networks constructed from several climate variables on the numerical grid of climate simulations, we seek for network properties that can serve as EWSs when approaching a state transition. We show that network indicators such as the normalized degree, the average length distance, and the betweenness centrality are capable of detecting tipping points at the global scale, as obtained by the MIT general circulation model in a coupled-aquaplanet configuration for CO2 concentration-driven simulations. The applicability of such indicators as EWSs is assessed and compared to traditional methods. We also analyze the ability of climate networks to identify nonlinear dynamical patterns.

Optimal design of spatial structures by a novel meta-heuristic algorithm: Sound energy optimizer

Sound signals are used by various species of living creaters to commonicate with each other. Scientific experiments have demonstrated that sound energy can travel through media and cause the receiver objects to vibrate or move. The present work simulates such a phenomenon in development of a new meta-heuristic algorithm called: Sound Energy Optimizer (SEO). Concerning physical rules, SEO leverages natural or ambient sounds for optimization. SEO utilizes a tunable virtual machine to control the decaying pattern of the sound energy in a medium representing the search space. Performance of the proposed algorithm is first evaluated on unconstrained optimization of some mathematical test functions. Then, a variety of pin-jointed structural problems are optimized including space tower, double-layer grid, barrel vault, helipad structure and power transmission towers. In this set, performance evaluation is done not only against the previous works but also by running SEO versus eight recent meta-heuristics under fair comparison conditions. New quantitative indices are introduced and traced as well as the common statistical measures and convergence curves. The results demonstrate competitive performance of SEO versus recent meta-heuristics in the discrete and continuous design of spatial structures.

Classification of Defect Photovoltaic Panel Images Using Matrox Imaging Library for Machine Vision Application

The maintenance of large-scale photovoltaic (PV) power plants has long been a challenging task. Currently, monitoring is carried out using electrical performance measurements or image processing, which have limited ability to detect faults, are time-consuming and costly, and cannot pinpoint the defect's precise location quickly. To address these challenges, this research focused on using deep learning techniques to classify defect and non-defect PV panels. The application provided deep learning algorithms capable of image classification in various classifiers. The image dataset was carefully curated and split into training and development datasets during the training model to ensure the highest accuracy for the prediction of the presence or absence of defects on the PV panel. Statistical measures, which are the average accuracy for the training model and average prediction, were employed to evaluate the classification performance of the defect PV panel model. The results demonstrated a remarkable total accuracy of model 99.9% for each class, and prediction results showed that almost 70% of defect PV panels were detected from the testing dataset. Furthermore, a comparative analysis was conducted to benchmark the findings against other algorithms. The practical implications of this research are significant, showcasing the effectiveness of deep learning algorithms and their compatibility with machine vision applications for the classification of defect PV panel images. By leveraging these techniques, solar farm operators can significantly improve maintenance management, thereby enhancing the efficiency and reliability of solar power generation and potentially saving significant costs.