Statistical Measures Research Articles

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.

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.

In Silico Molecular Dynamics and 3D‐QSAR Study on Thiazolidinedione Derivatives as Dual Inhibitors of DPP IV and PTP1B (DDPI's) for the Management of T2DM

AbstractSimultaneous inhibition of dipeptidyl peptidase IV and protein tyrosine phosphatase 1B as DDPI's emerged as a therapeutic intervention for the management of T2DM. In the present study, we have employed molecular dynamics simulation in conjugation with the field‐based 3D QSAR studies on a dataset having DDPI activities to identify the spatial fingerprints of target‐specific thiazolidinedione analogs. Separate contours were generated for both DPP IV and PTP1B showing respective pharmacophoric structural requirements for optimal inhibitory activity. These developed 3D QSAR models also showed good statistical measures (DPP IV: r2 = 0.9468; q2 = 0.7173, and PTP1B: r2 = 0.9718; q2 = 0.819) with the excellent predictive ability with PLS‐generated validation constraints. Comparative steric and electrostatic features were elucidated using respective contour maps for selective target‐specific favorable activity. Furthermore, molecular docking was used for elucidating the mode of binding as DDPI's to DPP IV and PTP1B, along with MD simulation (200 ns) analysis such as RMSD, RMSF, Rg, SASA, PCA, and FEL. These studies revealed that all the protein‐ligand docked complexes elicited an overall better stability as compared to reference ligand (vildagliptin or ertiprotafib) complexes. Molecular docking studies revealed that compound 24 (DPP IV: −150.667 kcal/mol; PTP1B: −142.792 kcal/mol) showed maximum affinity toward both the proteins DPP IV and PTP1B, as compared to their respective standard inhibitor, i.e., vildagliptin −99.9843 kcal/mol and ertiprotafib −125.399 kcal/mol, respectively. A comparative study of these developed multitargeted QSAR models along with molecular docking and dynamics study were employed for the optimization of drug candidates as DDPI's. Compound 24 showed most stable behavior in the binding pockets of both the enzymes, as compared to their respective standard inhibitors. This study will be helpful in designing novel DDPIs with appropriately substitute thiazolidinedione for the management of T2DM. The designed compound YSR‐14 exhibited the predicted IC50 values of 0.143 µM in DPP IV and 0.158 µM in PTP1B along with their excellent binding affinity against both targets.

Groundwater flow system of the Abijata-Langano-Ziway lakes basin, Ethiopia

The Abijata-Langano-Ziway Lakes Basin (ALZLB) is situated in the Central part of the Main Ethiopian Rift. The availability and dynamics of groundwater in the Abijata-Langano-Ziway Lakes Basin (ALZLB) are primarily controlled by its geological and hydrogeological characteristics, shaped by volcanic-tectonic and sedimentary processes. The basin faces significant challenges, including drastic change in land use pattern, rapid population growth sustained by subsistence farming, over-extraction of water resources, and vulnerability to climate change and fragile ecosystems. These issues emphasize the urgent need for effective water resource management.To understand this complex system, a numerical groundwater flow model was employed to characterize the groundwater flow system within the ALZLB and examine its interaction with surface water bodies. The MODFLOW model translates the conceptual understanding of the basin's hydrogeology into a mathematical representation, allowing for numerical analysis. The model incorporates input parameters such as hydraulic conductivity and boundary conditions representing groundwater inflow and outflow. Steady-state numerical calculation was used to characterize the qualitative hydrogeological conceptual model into numerical representation and thereby describe the groundwater system.The calibrated model exhibited excellent agreement between simulated and observed groundwater levels. Statistical measures indicated a strong correlation (R2 = 0.98) and high efficiency (NSE = 0.97) in replicating the observed data. Additionally, the Mean Error (ME) of −8.3 m suggests minimal bias in the simulations. Further analysis of the histogram residuals revealed that a significant portion of the simulated values (65 % and 82 %) fell within ±20 m and ±30 m of the observed groundwater levels, respectively. This revealed the model's accuracy in capturing the groundwater system's behavior.The model identified groundwater recharge and constant head boundaries as the primary sources of groundwater inflow, contributing 445 million cubic meters per year (MCM/year) and 90 MCM/year, respectively. Conversely, constant head boundaries represented the most significant outflow pathway, with a simulated discharge of 519 MCM/year. The calibrated balance between inflow and outflow (discrepancy of −0.75 %) confirms that the model effectively simulates steady-state groundwater flow conditions. The calibrated model demonstrates the model's capability to accurately represent the basin's groundwater system.

KNOWLEDGE OF BLOOD TRANSFUSION AMONG NURSES AND MEDICAL TECHNICIANS

Introduction: In blood transfusion, nurses play a key role in ensuring patient safety by performing identification, compatibility and vital parameter monitoring procedures as well as providing support and education to patients. Their contribution is crucial for achieving high standards of medical care and improving medical treatment outcomes.Objective: To assess the level of knowledge amongst nurses with regards to safe and proper application of transfusion therapy. Participants and Methods: This research was conducted as a cross-section descriptive-analytical study with the aim of assessing knowledge of routine blood transfusion among three groups of nurses and analyzing differences in their results. The sample consisted of three target groups of nurses studying at the Faculty of Health Studies (of the University Clinical Hospital Mostar, the Cantonal Hospital Dr. "Safet Mujić" Mostar and the County Hospital "Dr. fra Mihovil Sučić" Livno). A modified version of the RBTKQ questionnaire was used for the research. The questionnaire consists of 6 sections and 49 items. Descriptive and inferential statistical measures were used, including frequencies and percentages, normality distribution tests, as well as the Mann Whitney U test and Kruskal-Wallis test, with results evaluated at both levels of significance.Results: The analysis of nurses' knowledge on blood transfusion indicates an average level of 77.4%, with notable variation among participants. These findings underscored the need for more intensive education particularly in recognizing symptoms and responding to serious complications. Additionally, statistically significant differences between male and female participants, as well as participants from different institutions and qualifications, highlight specific areas where overall improvement of nurses' knowledge in these domains require focus.Conclusion:Lack of knowledge in recognizing the causes of fatal transfusion reactions, especially patient identification errors, compromises patient safety during transfusions, emphasizing the urgent need to improve education of nurses in this area. Keywords: blood transfusion, nurses, knowledge, patient safety, education

Measuring network dynamics of opioid overdose deaths in the United States

The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook’s social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called “deaths in social proximity”, to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100, 000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties. Our analysis provides a pathway for public health interventions informed by social network structures. The statistical robustness of our primary variable of interest, deaths in social proximity, supports the hypothesis of a social network effect on OODs. Using agent-based modeling (ABM) to simulate social networks can offer an effective method to design interventions that incorporate the dynamics of social networks for maximum impact.

Estimation of Potential Impact of Land Use Change on Sediment Yield From a Small Tropical Watershed Using the TREX Erosion Model

ABSTRACTAnalysis of the influence of changes in land use on the sediment yield from watersheds provides crucial inputs that aid the development of appropriate strategies for the sustainable management of consequent land degradation. Event‐based hydrologic models can be used for performing such analysis, as long‐term hydrological datasets are not available for most of the rivers in developing countries like India. In this study, an event‐based Two‐Dimensional Runoff, Erosion, and Export (TREX) model was used to simulate the soil erosion process in Moozhy, an ungauged watershed in the Karamana River basin in Thiruvananthapuram District, Kerala State, India. Rainfall, streamflow, and sediment concentration data corresponding to three isolated storm events were used to calibrate and validate the model. The performance of the model was assessed using four statistical measures, namely, Percent bias (PBIAS), Nash‐Sutcliffe Efficiency Coefficient (NSEC), Coefficient of determination (R2) and RMSE‐observations standard deviation ratio (RSR). The values of PBIAS varied between 46% and 49%, indicating satisfactory performance of the model. Also, from the values of NSEC and RSR, it can be concluded that the TREX model yields acceptable results. For a rainfall event that occurred on the 17 September 2017, the simulated values showed that about 1054 t of suspended sediments are transported from the watershed to the stream channels, and about 1020 t is carried out of the Moozhy watershed. Only a very small amount of sediment is left in the channel at the end of the simulation. About 34 t of sediment is deposited in the channel bed, about 0.04 t remains in suspension, and the balance 0.04 t remains in suspension as a suspended load. About 3% of the total sediment entering the reach of the stream considered in this study is deposited on the riverbed. The major component of the settled sediments is silt; about 12% of the total silt size fraction entering the stream settles on the river bed. Future scenarios of land use in the watershed were modelled using a hybrid Artificial Neural Network (ANN) and Cellular Automata (CA) model. These were used for simulating sediment discharge using the TREX erosion model. The predicted land use scenarios were used to investigate both short‐term (2025 and 2029) and long‐term (2037, 2045, and 2053) variations in sediment discharge. The sediment yield for the storm event that occurred on 17 September 2017 was used to benchmark the variation in sediment yield under the predicted land use scenarios. Results indicate that the peak runoff and the runoff volume are expected to increase by about 29% and 22% respectively in the period from 2005 to 2053. The expected increase in the volume of sediments in this period is about 50%; the peak sediment concentration is likely to increase by about 56%. The study highlights the threats of likely increase in soil erosion and consequent land degradation posed by unscientific changes in land use caused by urbanisation and calls for proper management interventions to address the problem.

Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning

Background: Alzheimer&amp;rsquo;s disease (AD) is the most common form of dementia. The lack of effective prevention or cure makes AD a significant concern, as it is a progressive disease with symptoms that worsen over time. Objective: The aim of this study is to develop an algorithm capable of differentiating between patients with early-stage AD (mild cognitive impairment [MCI]), moderate AD, and healthy controls (C) using electroencephalogram (EEG) signals. Methods: A publicly available EEG database was utilized, with seven EEG recordings selected from each study group (MCI, AD, and C) to ensure a balanced dataset. For each 1-s segment of EEG data, 43 time-frequency features were computed. These features were then compressed over time using 10 statistical measures. Subsequently, 15 classifiers were employed to distinguish between paired groups using a 7-fold cross-validation. Results: The strategy yielded better results than state-of-the-art methods, achieving a 100% accuracy in both C versus MCI and C versus AD binary classifications. This improvement translated to a 2% increase in accuracy for C versus MCI and a 4% increase for C versus AD, despite a 1.2% decrease in performance for AD versus MCI. In addition, the proposed method outperformed prior work on the same database by 4.8% for the AD versus MCI comparison. Conclusion: The present study highlights the potential of EEG as a promising tool for early AD diagnosis. Nevertheless, a more extensive database should be used to enhance the generalizability of the results in future work.

The polygenic hazard score mediates the association between plasma neurofilament light chain and brain morphometry in dementia spectrum

IntroductionBlood-based biomarkers such as plasma neurofilament light chain (pNfL) are crucial biomarkers for Alzheimer's disease (AD). Additionally, neuroimaging techniques such as tensor-based morphometry (TBM), which identify structural changes in the brain, can provide valuable insights into AD pathophysiology. However, the role of genetics in linking the blood based biomarkers and imaging findings has not been well understood. Therefore, we aimed to investigate whether the polygenic hazard score (PHS), affects the association between neurofibrillary tangles and neuritis plaques and brain imaging findings. MethodsUsing the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we enrolled all participants for whom a complete dataset of pNfL, PHS, and TBM was available. Using Python, we analyzed the associations between pNfL levels and the TBM data of 567 participants incluidng 152 cognitively normal individuals, 309 participants with mild cognitive impairment (MCI), and 106 patients with AD. We used a mediation analysis to identify the effect of PHS in how pNfL is associated with TBM measures. ResultsWe found a negative correlation between the accelerated TBM measure and NfL levels in both the MCI and AD groups. The pNfL concentration predicted both accelerated statistical and anatomical TMB measures in patients with MCI. Furthermore, PHS mediatedthe association between statistical TBM measures and NfL levels in AD patients, to the extent that the significant association between NfL and TBM measures disappeared after accounting for PHS. ConclusionWe showed that although pNfL can predict the cognitiee decline and imaging findings in AD, this effect is mediated by the PHS. Therefore, PHS should be considered when investigating AD biomarkers and their corresponding imaging findings.

Effects of noise intensity on early warning indicators of thermoacoustic instability: An experimental investigation on a lean-premixed combustion system

In this work, we experimentally investigate the noise-induced dynamics of a lean premixed combustion system operating on natural gas–air mixtures that exhibit thermoacoustic instability via a subcritical Hopf bifurcation. The investigation is done before the bistable region with equivalence ratio (ϕ) as the control parameter. We analyze the acoustic pressure oscillations (p′) in the combustor and fluctuations in the heat release rate (q′) from the laminar quasi-flat flame at increasing levels of white noise. We show the effects of noise intensity on the reliability of various types of early warning indicators (EWIs) to predict the onset of the impending thermoacoustic oscillations. We investigate the indicators based on statistical measures (variance, skewness, and kurtosis), autocorrelation and spectral properties (coherence factor), system identification (growth/decay rates of p′), multi-fractality (Hurst exponent), and time series complexity (permutation entropy and Jensen–Shannon complexity). The coherence factor, variance, and decay rates of p′ always increases as the system approaches thermoacoustic instability, indicating their robustness as an EWI under most noise levels. An increase in kurtosis cannot be employed as an EWI. Implementing autocorrelation, skewness, Hurst exponent, permutation entropy and Jensen–Shannon complexity as effective EWIs has limitations: they can be estimated accurately only from pressure oscillations (p′) data and work only above a particular threshold value of noise intensity. Our results have direct implication on early prediction and control of thermoacoustic instability in practical gas turbine combustors.Novelty and significance statementDeveloping effective early warning indicators (EWIs) to anticipate the onset of thermoacoustic instability is crucial for preventing potential damage and ensuring the reliable operation of lean premixed gas turbine combustion systems. In such systems, inherent noise dynamics undergo variations with changing operating conditions and combustor designs. Specifically, noise intensity increases as the system becomes more turbulent. In this study, we demonstrate that the inherent noise dynamics in a lean premixed combustion system play a crucial role in influencing the trends observed in early warning indicators of thermoacoustic instability. We address several key questions, including (a) whether comparative reliability assessments of different classes of EWIs exist, (b) the effect of variations in noise properties on the efficacy of EWIs, and (c) which EWIs are most reliable considering that noise characteristics depend on the system and may even change with operating conditions within a specific system. This information is critical for engine designers/users, facilitating the development of robust and effective monitoring systems for gas turbine combustion systems.