Logarithmic Transformation Research Articles

Insights From the ISASS Webinar Series on Current and Emerging Techniques in Endoscopic Spine Surgery | Part 1: Polytomous Rasch Analysis of Surgeon Endorsement of Endoscopic Discectomy/Foraminotomy, Interbody Fusion, and Importance of Patient Feedback During Surgery.

The International Society for the Advancement of Spine Surgery hosted the first of a series of 4 webinars on endoscopic spine surgery techniques, focusing on end§oscopic discectomy, foraminotomy, instrumented endoscopic fusion, standalone lumbar interbody fusion with innovative materials, and the role of patient feedback in awake procedures. This series aims to share knowledge and discuss the complexities and clinical evidence of modern endoscopic spine surgery. To analyze the level of surgeon endorsement for the presented endoscopic spine surgery techniques before and after the webinar, utilizing polytomous Rasch analysis, and to evaluate the potential for these insights to inform clinical guideline recommendations. A survey was available to 1311 potential respondents during the Zoom webinar, collecting data on surgeon endorsements using a Likert scale. The polytomous Rasch model was employed to analyze responses, considering the complexity of decisions against surgeon expertise, developing a logarithmic measurement scale, allowing objective statistical analysis of categorical variables, highlighting incongruent or out-of-order items vs congruent and in-order items, and driving improvement in clinical guidelines. All 4 topics received higher confidence ratings demonstrated by descriptive statistics, highlighting the webinar's effective role in surgeon education and in identifying ongoing trends in spine surgery. The logarithmic transformation of these data during Rasch analysis showed noticeable shifts in surgeon confidence levels postwebinar, with increased endorsement for transforaminal full-endoscopic thoracolumbar interbody fusion for hard disc herniation and standalone endoscopic lumbar interbody fusion. The Wright plot and person-item map analyses demonstrated that the webinar effectively targeted areas of initial low confidence, significantly impacting surgeons' perceptions. Disordered endorsement thresholds remained in the topics of uniportal transforaminal discectomy/foraminotomy and patient feedback during endoscopic spine surgery, indicating issues in response category discrimination or confounding factors not captured by the survey. Ongoing controversies were highlighted by the influence of confounding factors, stemming from preconceived notions and limited familiarity with high-grade evidence. The first in the 4-part webinar series effectively shifted professional confidence and acceptance of innovative surgical approaches among spine surgeons. Observations indicated a high level of interest in applying the endoscopic surgery platform with other advanced technologies. The polytomous Rasch analysis provided nuanced insights into ongoing trends and areas in need of further clarification. Assessing surgeon confidence and acceptance of endoscopic spinal surgeries using polytomous Rasch analysis. Level 2 (inferential) and 3 (observational) evidence because Rasch analysis provides statistical validation of instruments rather than direct clinical outcomes.

Non-Destructive Prediction of Rosmarinic Acid Content in Basil Plants Using a Portable Hyperspectral Imaging System and Ensemble Learning Algorithms

Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for the RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. The best RA prediction model was the LightGBM model with feature selection by the AdaBoost algorithm and spectral pre-processing through logarithmic transformation and second derivative. This model performed satisfactorily for practical screening with R2P = 0.81 and RMSEP = 3.92. From in-field HSI data, the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage.

Predictive Value of Inflammatory Biomarkers in Assessing Major Depression in Adults

Background: There are studies that have investigated the association of pro-inflammatory cytokines with depressive disorders, but they often present certain limitations. In this study, two substantial groups of patients were analyzed: 92 patients with major depressive disorder and 76 without depressive disorders. The strict inclusion and exclusion criteria for the analyzed groups significantly increased the value of the obtained results. The research question of this study was whether levels of inflammation, measured by the inflammatory markers IL-6, IL-1α, and TNF-α, could predict the severity of depressive symptoms. This could provide additional evidence supporting the hypothesis that inflammation plays a notable role in the pathogenesis of depression. The data analysis supports the hypothesis that the biological mechanisms of inflammation contribute to the clinical manifestations of depression. Elevated levels of inflammatory markers, especially interleukins (IL-6, IL-1α) and tumor necrosis factor-alpha (TNF α), have been identified in patients with major depressive disorder compared to the findings in healthy controls. Materials and Methods: Inflammatory markers (IL-6, IL-1α, and TNF-α) were measured in a sample of 92 patients hospitalized at the Socola Institute of Psychiatry in Iasi, Romania, and compared to a control group with no depression or inflammatory conditions (n = 76). Severity of depressive symptoms was assessed using HAM-D scores. Results: The study results indicated that values of plasma inflammatory markers were significantly higher in patients with major depressive disorder (MDD) compared to the control group (IL-1α: 1.16 ± 0.44 pg/mL vs. 0.89 ± 0.25 pg/mL, p = 0.0004; IL-6: 9.21 ± 4.82 pg/mL vs. 7.16 ± 4.32 pg/mL, p = 0.0149; and TNF-α: 2.02 ± 0.96 pg/mL vs. 1.67 ± 0.8 pg/mL, p = 0.0286). The differences remained significant after applying logarithmic transformation, which was necessary to adjust for outlier values. An analysis of demographic characteristics showed that the frequency of women (67.4% vs. 36.84%, p < 0.001), cohabiting individuals (28.26% vs. 10.53%, p = 0.0001), and alcohol consumers (67.39% vs. 47.37%, p = 0.0087) was significantly higher in patients with MDD. The level of education was significantly lower in patients with MDD (median (IQR): 12 (2.5) years vs. 14 (8) years, p = 0.0016). The evaluation of confounding variables, including patients’ gender, marital status, education level, and alcohol consumption, was performed using multiple linear regression models. The results indicated that these demographic variables did not significantly influence the correlation between the HAM-D score and the values of IL-6, IL-1α, and TNF-α. A significant correlation between the HAM-D score and the logarithmic values of inflammatory markers was observed for log IL-1α in men (r = 0.355, p = 0.0014), log IL-6 in women (r = 0.0313, p = 0.0027), and log TNF-α in women (r = 0.3922, p = 0.0001). The results of the multiple linear regression and predictive analysis indicated that IL-1α (AUC = 0.677, p = 0.0004), IL-6 (AUC = 0.724, p < 0.001), and TNF-α (AUC = 0.861, p < 0.001) demonstrate high accuracy in discriminating patients with MDD. Conclusions: The results highlighted that IL-6 (AUC = 0.724; 95% CI: 0.648–0.801) and TNF-α (AUC = 0.861; 95% CI: 0.797–0.925) are significant predictors for major depressive disorder. The study highlights the potential of cytokines (IL-1α, IL-6 and TNF-α) as diagnostic markers. These findings support the hypothesis that inflammation may play an important role in the development or exacerbation of depressive symptoms.

Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics

<i>Background</i>: Price changes in economics present significant geometric challenges due to sharp discontinuities, which cannot be efficiently described by continuous processes like Brownian motion. Traditional models often rely on linear assumptions, yet financial data frequently exhibit irregular, complex patterns. Fractal theory, a mathematical framework, offers a more accurate way to describe these fluctuations by revealing the underlying self-similar structures in price changes and scaling phenomena. This study explores the use of fractal geometry to gain deeper insights into market behavior. <i>Objective</i>: The objective is to demonstrate that an alternative model, constructed based on geometric scaling assumptions, offers a more accurate description of price changes in competitive markets. <i>Method</i>: The study combined the scaling principle from fractal geometry with a stable Levy model to formulate an integrated model. The logarithmic transformation of the model was applied over successive price changes to observe the behavior of market prices. <i>Result</i>: The scaling principle asserts that no specific time interval (such as a day or a week) holds inherent significance in competitive markets. Instead, these time features are compensated or arbitrated away, supporting the idea that market behavior is self-similar across different time scales. <i>Conclusion</i>: The scaling principle provides a more reliable framework for modeling price changes and is recommended for consideration in economic analyses.

On the study of solitary wave dynamics and interaction phenomena in the ultrasound imaging modelled by the fractional nonlinear system

This research work focuses on investigating the propagation of ultrasonic waves, which propagate mechanical vibrations of molecules or particles inside materials. Ultrasound imaging is extensively used and deeply rooted in the medical field. The key technologies that form the basis for many different uses in the area include transducers, contrast agents, pulse compression, beam shaping, tissue harmonic imaging, techniques for measuring blood flow and tissue motion, and three-dimensional imaging. The third-order non-linear β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-fractional Westervelt model has been used as a governing model in the imaging process for securing the different wave structures. The exact solutions of different types, including mixed, dark, singular, bright-dark, bright, complex and combined solitons are extracted. These solutions are obtained by using two newly introduced techniques, namely modified generalized Riccati equation mapping method and modified generalized exponential rational function method. Moreover, breather, lump and other waves are extracted by the assistance of logarithmic transformation and different test functions. The used methodologies are extremely effective and possess substantial computing capacity to effectively address the different solutions with a high level of accuracy in these systems. The techniques used are well-known for being effective, simple, and flexible enough to integrate multiple soliton systems into a unified framework. In addition, we provide 2D and 3D graphs that explain the behavior of the solution at various parameter values, under the influence of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-fractional derivatives. The results offered in this study may improve the comprehension of the nonlinear dynamic behavior of the specific system and confirm the efficacy of the approaches used. We expect that our approaches will be beneficial for a wide range of nonlinear models and other problems in the related fields.

Association of soluble ST2 with heart failure and other comorbidities in patients with severe coronary heart disease

Abstract Background Circulating levels of soluble suppression of tumorigenicity-2 (sST2) reflect vascular congestion, inflammation, and fibrosis. sST2 has been proposed as a useful biomarker to predict the mortality and hospitalization due to heart failure. However, there is limited evidence regarding the relationship between sST2 and heart failure, as well as other comorbidities, in severe coronary heart disease (CAD). Purpose This study aims to measure the circulating levels of soluble ST2 (sST2) in a large population of patients with severe CAD to determine its association with heart failure, cardiac function, inflammation, and other important comorbidities. Methods This subanalysis is part of the ongoing perspective observational study. Available serum samples from 520 patients undergoing elective coronary artery bypass grafting (CABG) were analysed for sST2 levels. The inclusion criteria were written informed consent and age between 18-85 years. Exclusion criteria for this analysis were severe valvular disease, severe chronic obstructive pulmonary disease, oxygen therapy, nocturnal positive airway pressure support or mechanical ventilation, and the need for catecholamines or circulatory assist devices. The circulating levels of human sST2 were measured from frozen serum (-80°C) using commercially available ELISA kits. The plasma was diluted 100 times to detect sST2 levels (assay range of 31.2 - 2,000 pg/mL). Two measurements were taken to account as means for intra-assay variabilities. Logarithmic transformation was used to convert non-normal distributed data. Student t' test and linear regression were applied to categorical or continuous variables as appropriate. Results sST2 levels (median 20.4 ng/ml, [IQR 15.5 – 28.8]) significantly predict worse left ventricular ejection fraction (β = -0.164, p = 0.006), and significantly correlate with levels of NT-proBNP (β = 3.263, p < 0.001), hs-Troponin T (β = 3.391, p < 0.001), high-sensitivity C-reactive protein (β = 2.472, p = 0.014) and heart failure (p= 0.002). Interestingly, common comorbidities such as chronic kidney disease (estimated glomerular filtration rate < 60 mL/min/1.73 m2, p = 0.004), diabetes mellitus (p = 0.001) and higher levels of glycated haemoglobin (HbA1c, β = 3.615, p < 0.001) showed also a significant association with sST2 levels. No significant association of sST2 was observed with arterial hypertension (p = 0.190), atrial fibrillation (p = 0.289), obesity (BMI ≥ 30 kg/m2, p = 0.302), sleep apnoea (apnoea-hypopnoea index ≥ 15/h, p = 0.068), peripheral artery disease (p = 0.823), or previous stroke (p = 0.861). Conclusion(s) Circulating levels of sST2 significantly correlate with heart failure, NT-proBNP, hs-TnT, and hs-CRP in patients with severe CAD. Additionally, sST2 levels were found to be elevated in patients with diabetes mellitus and chronic kidney disease.

Image shadow removal based on pseudo-illuminated region and log domain transformation

ABSTRACT The presence of shadows in images can significantly degrade image quality. To address the challenges, this study utilizes pseudo-illumination regions and logarithmic domain transformations. Firstly, the input shadow image and its corresponding shadow region are segmented and clustered into multiple image blocks. Then, a comprehensive consideration of distance, color, and texture features is employed to construct pseudo illumination regions. Subsequently, the designed logarithmic domain transformation function is utilized for shadow removal. Finally, color correction and optimization of shadow edges complete the overall process. The feasibility of this approach has been preliminarily verified through experimental analysis.

On the study of analytical soliton solutions and interaction aspects to the Estevez-Mansfield-Clarkson equation arising in diversity of fields

Abstract The beta fractional form of the Estevez-Mansfield-Clarkson equation is under consideration and this study is done with the assistance of methods such as modified F-expansion method and the logarithmic transformation. A variety of analytical solutions like bright, dark, mixed, singular, bright-dark, and combined solitons are extracted. Moreover, multi waves structures, interaction with double exponential form, breather waves, mixed type solutions as well as periodic cross kink solutions have been analyzed. The governing equation is converted into an ordinary differential equation by employing an appropriate wave transformation with the β-derivative in order to achieve the desired solutions. The applied approaches have substantial computational capability, enabling them to efficiently address exact solutions with high accuracy in these systems. The results indicate that the equation under investigation theoretically contains a substantial number of soliton solution structures. Additionally, in order to examine the behaviors of the solutions at various parameter values, we plot a variety of graphs that incorporate pertinent parameters. The results of this study have the potential to improve understanding of the nonlinear dynamic characteristics displayed by the specified system and to confirm the effectiveness of the techniques that have been implemented.

Dynamic monitoring of the endothelium function in pregnant women at high risk of preeclampsia during pathogenetic prevention of its development

BACKGROUND: Today, preclinical diagnosis of preeclampsia presents significant difficulties. In widespread practice, it is diagnosed based on existing clinical signs and laboratory and functional research methods. Most of them are invasive and expensive, which makes it difficult to use them in widespread clinical practice for diagnosis and, especially, for monitoring the effectiveness of therapy over the dynamics of the disease. It is known that the pathogenesis of preeclampsia can have two independent development paths, converging in a common resulting link — the formation of endothelial dysfunction. Methods for studying endothelial function include determining markers of its imbalance in blood samples and non-invasive functional tests. Non-invasive diagnosis of endothelial dysfunction using the EndoPAT test allows us to quantify endothelium-mediated changes in vascular tone during 5-minute occlusion of the brachial artery. AIM: The aim of this study was to evaluate the method for determining the endothelium function in the first, second and third trimesters of pregnancy during ongoing pathogenetic prevention of preeclampsia. MATERIALS AND METHODS: This interventional uncontrolled study of the effectiveness of preventing preeclampsia using non-invasive assessment of vascular endothelial dysfunction during pregnancy was conducted at the Research Institute of Obstetrics, Gynecology and Reproductology named after D.O. Ott, St. Petersburg, Russia. The study involved 108 pregnant women at high risk of developing preeclampsia. All pregnant women underwent a cuff test to determine endothelial dysfunction using the peripheral arterial tonometry technique on the Endo-PAT 2000 device. The dynamic study was carried out in the first, second and third trimesters of pregnancy. As a result of the study, when endothelial dysfunction was detected [logarithmic transformation of reactive hyperemia peripheral arterial tonometry index (LnRHI) less 0.51], a complex glycosaminoglycan was additionally added to the basic prophylaxis with acetylsalicylic acid at a dosage of 250 MU (one capsule three times a day for eight weeks), then a cuff test was monitored after 6–12 weeks. Statistical analysis was performed using the IBM SPSS Statistics 20 software. All tests for significance were two-tailed, and differences were considered significant at p 0.05. RESULTS: Endothelial dysfunction was detected in the first trimester in 59 (55%) patients, then during ongoing complex therapy in the second trimester (n = 72) in 46 (64%) patients and in the third trimester (n = 46) in 4 (1%) patients. Moderate preeclampsia in the third trimester (35–39 weeks of gestation) developed in 28 (25.9%) patients out of 108. At the same time, at the start of the study, 15 patients with endothelial dysfunction received complex therapy, and 13 individuals only took acetylsalicylic acid. Among 46 patients who observed the dynamics of the entire pregnancy, only 4 (8.7%) women developed preeclampsia. After complex treatment prescribed based on the first trimester parameters, out of 36 patients who discontinued complex therapy and did not undergo a functional test subsequently, preeclampsia developed in 16 (44.4%) women. In the second trimester, out of 26 patients who stopped complex therapy, preeclampsia developed in 8 (30.8%) people. Thus, constant monitoring and complex therapy reduced the frequency of preeclampsia. In the group with a history of preeclampsia, the disease developed in 13 (39.4%) women. In the group with a high risk of preeclampsia according to the results of combined prenatal screening in the first trimester, with the exception of a history of preeclampsia (blood pressure test, placental growth factor level in the blood serum, lowest uterine artery pulsatility index value calculated to assess the individual risk of preeclampsia with a titer less than 1 : 100), preeclampsia occurred in 4 (13.3%) women. In the group with extragenital pathology associated with the risk of preeclampsia, including obesity, pregestational diabetes mellitus, chronic arterial hypertension, and chronic kidney disease, with the exception of a history of preeclampsia and a high risk of preeclampsia according to the results of perinatal screening, preeclampsia occurred in 11 (24.4%) women. In the high–risk groups for preeclampsia, we identified the highest-risk group, namely, one with the presence of preeclampsia in the anamnesis. CONCLUSIONS: The effectiveness of the functional method for determining endothelial dysfunction in the first, second and third trimesters of pregnancy during pathogenetic prevention of preeclampsia has been proven.

The effects of land use on soil carbon stocks in the UK

Abstract. Greenhouse gas stabilisation in the atmosphere is one of the most pressing challenges of this century. Sequestering carbon in the soil by changing land use and management is increasingly proposed as part of climate mitigation strategies, but our understanding of this is limited in quantitative terms. Here we collate a substantial national and regional data set (15 790 soil cores) and analyse it in an advanced statistical modelling framework. This produced new estimates of the effects of land use on soil carbon stock (Sc) in the UK, different in magnitude and ranking order from the previous best estimates. Soil carbon stocks were highest in woodlands, followed by rough grazing, semi-natural grasslands, and improved grasslands, and they were lowest in croplands. Estimates were smaller than the previous estimates, partly because of new data, but mainly because the effect is more reliably characterised using a logarithmic transformation of the data. With the very large data set analysed here, the uncertainty in the differences among land uses was small enough to identify consistent mean effects. However, the variability in these effects was large, and this variability was similar across all surveys. This has important implications for agri-environment schemes seeking to sequester carbon in the soil by altering land use, because the effect of a given intervention is very hard to verify. We examined the validity of the “space-for-time” substitution, and, although the results were not unequivocal, we estimated that the effects are likely to be overestimated by 5 %–33 %, depending upon land use.

Reliable Prediction of Solar Photovoltaic Power and Module Efficiency using Bayesian Surrogate Assisted Explainable Data-Driven Model

This study proposes a Bayesian surrogate-driven explainable deep neural network model to predict and interpret the module efficiency and maximum output power of three commercially available photovoltaic modules: monocrystalline silicon, polycrystalline silicon, and amorphous silicon during the winter season. In addition, the influence of the photovoltaic material and ambient conditions on the predicted power and module efficiency is investigated. The model inputs include solar irradiance, module material (monocrystalline, polycrystalline, and amorphous silicon), season of the year (months), and time of day (morning to evening). Experiments were conducted on these three modules during the winter using data from Rawalpindi, Pakistan. These data were then fed into the deep neural network model to train and yield predictions. Several preprocessing techniques such as logarithmic transformation, square root, cube root, reciprocal, and exponential transformations are employed to improve the linearity of distributed data. For hyper-parameters optimization, Gaussian process, gradient boost regression trees, and random forest are used. The results show that the optimal deep neural network using random forest surrogate model along with square root and exponential transformation is able to predict the maximum power output and module efficiency of the considered photovoltaic modules for the entire investigated period with a correlation coefficient, R2 = 0.998. The least accurate model (R2=0.991) is a Gaussian process using simple data distribution. These results hold valuable insights for predicting and optimizing the performance of other solar photovoltaic modules in various climates and different seasons of the year.

Association Between Nickel Exposure and Metabolic Syndrome: Data from NHANES 2017-2018.

Previous studies have found a possible association between nickel and metabolic syndrome (MetS), but with conflicting results. No studies have determined whether nickel exposure increases the prevalence of MetS in the general U.S. population. Therefore, we used data from the National Health and Nutrition Examination Survey (NHANES) to assess the association between urinary nickel and MetS. Since urinary nickel levels were presented as a skewed distribution, they were normalized using a logarithmic transformation. Weighted multivariate logistic models, restricted cubic spline, threshold effect analysis, and subgroup analyses were used to examine the association between urinary nickel concentration and the risk of MetS and its components. Based on data from 1577 participants, individuals in the second, third, and fourth quartiles of urinary nickel had an adjusted OR for MetS of 1.42 (95% CI: 0.88, 2.28), 2.00 (95% CI: 1.22, 3.28), and 1.68 (95% CI: 1.05, 2.70), respectively, representing an inverted "L"-shaped nonlinear dose-response relationship with an inflection point at 0.2141ng/L. Patients over the age of 40, males, less educated, and smokers are more susceptible to nickel exposure. In addition, there were significant associations between nickel and most components of the MetS, with the strongest to weakest correlations being high fasting glucose, reduced high-density lipoprotein, abdominal obesity, and elevated blood pressure; however, there was no significant correlation between nickel and hyperlipidemia. In conclusion, environmental nickel exposure increases the prevalence of MetS in U.S. adults, particularly in males over 40years of age, those with less education, and smokers.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.

Direct evidence for logarithmic magnitude representation in the central nervous system

Fechner’s law proposes a logarithmic relationship between the physical intensity and perceived magnitude of a stimulus. The principle of logarithmic magnitude representation has been extensively utilized in various theoretical frameworks. Although the neural correlates of Weber’s law have been considered as possible evidence for Fechner’s law, there is still a lack of direct evidence for a logarithmic representation in the central nervous system. In our study, participants were asked to reproduce the time intervals between two circles and ignore their spatial distances while electroencephalogram (EEG) signals were recorded synchronously. Behavioral results showed that a Bayesian model, which assumes a logarithmic representation of spatiotemporal information, was better at predicting production times than a model relying on a linear representation. The EEG results revealed that P2 and P3b amplitudes increased linearly with the logarithmic transformation of spatiotemporal information, and these event-related potentials were localized in the parietal cortex. Our study provides direct evidence supporting logarithmic magnitude representation in the central nervous system.

Global COVID-19 under-reporting: A Tobit model

During the COVID-19 pandemic, the precision in reporting infectious cases and fatalities presents significant challenges, exacerbated by rapid transmission rates and overburdened healthcare infrastructures. Officially reported cases occasionally exhibit zero increments, which is likely to be under-reported. Some models exclude zero values from the sample, creating a sample selectivity problem. In contrast, alternative models substitute zero values with a constant to enable logarithmic transformations. Since both modelling approaches are wrong, in this study, we address this issue by extending the Tobit model to account for both under-reporting and random noise. The standard Tobit model accommodates zero values but not under-reporting. Analyzing data from 61 countries between January 1, 2020, and November 3, 2020, we explore external factors that explain country-specific under-reporting. Our findings confirm the existence of under-reporting across countries and reveal that cases reported with zero increments actually involve non-zero infectious instances. This novel methodology enriches future under-reporting analyses.

Models for the Inactivation of Foodborne Pathogens in Salad Dressing from Challenge Studies

The Association for Dressings and Sauces’ (ADS) members have conducted challenge studies on salad dressing products to assess pathogen survival. Data from 79 different challenge studies provided by ADS were used in this analysis. The acid-moisture ratio, pH, incubation temperature and ingredient details were provided for each study. Linear regression models were used to predict the time to 3-log, 4-log, and 5-log reduction as a function of study parameters. A statistically based approach also was used to estimate the concentration of pathogens in ingredients based on testing history. This was combined with decline modeling to estimate pathogen concentration over time. The time to five log reduction for each of the target pathogens were highly skewed. A logarithmic transformation of time to 5 log reduction resulted in approximately normally distributions. Incubation temperature and formulation pH were highly significant (p < 1E-6), in predicting the number of days to a five-log reduction of Escherichia coli O157:H7, while the percentage of spices in the formulation is also quite significant (p = 0.01). Salmonella modeling showed that the most highly significant parameter was the percentage of water (p < 1E-8). Other parameters in order of descending significance include the percent fruit (p=0.00032), incubation temperature (p=0.00268), followed by percent sugar (p=0.02161) and percent vegetables (p=0.03149). The most significant parameter in predicting Listeria monocytogenes reduction was incubation temperature (p=0.000687), followed by acid moisture ratio (p=0.012423). The next two significant parameters in the Listeria model were percent lipid (p=0.023772) and percent water (p=0.025701). The least significant parameter that meets the minimum criteria for inclusion in the Listeria model (p<0.05) was percent fruit (p=0.047074). Our analysis will be useful in developing risk-based approaches to continue to assure the safety of commercially prepared salad dressings.

Inflow generation using Thomas- Fiering model for Thaphanseik Reservoir in Myanmar

Thomas-Fiering (T-F) model was applied for generating synthetic streamflow for the Thaphanseik reservoir. The Thomas-Fiering model accommodated the non-stationarity of seasonal data. Time series of streamflow are crucial for the planning, design, and management of various water resource systems. In this study, the model was tested using historical data spanning 39 years (from 1985 to 2023). The model’s performance was evaluated by using statistical measurements such as Coefficient of Determination (R2) and Nash-Sutcliffe Efficiency (NSE). Additionally, this model was utilized to generate synthetic streamflow data for the years 2024 to 2100. The logarithmic transformation method was used in order to avoid the negative flows in the synthetic data. In this study, synthesis flow data were generated using different random sequences. The mean, standard deviation and correlation coefficient of different synthetic series were calculated. The calibration process was performed for the periods 1985 to 2016 and validation process was performed for the years 2017 to 2023. Based on R2 value, most suitable synthetic series were chosen. The generated data showed a high goodness of fit, with R² and NSE values. An analysis of the historical and synthetic discharge statistics revealed that the model successfully captured the features of the historical data and integrated them into the generated sequences.

Optimizing hierarchical classifiers with parameter tuning and confidence scoring

Hierarchical classifiers play a crucial role in addressing complex classification tasks by breaking them down into smaller, more manageable sub-tasks. This paper continues a series of works, focused on the technical Ukrainian texts hierarchical classification, specifically the classification of repair works and spare parts used in automobile maintenance and servicing. We tackle the challenges posed by multilingual data inputs – specifically Ukrainian, Russian, and their hybrid – and the lack of standard data cleaning models for the Ukrainian language. We developed a novel classification algorithm, which employs TF-IDF victimization with unigrams and bigrams, keyword selection, and cosine similarity for classification. This paper describes a method for training and evaluating a hierarchical classification model using parameter tuning for each node in a tree structure. The training process involves initializing weights for tokens in the class tree nodes and input strings, followed by iterative parameter tuning to optimize classification accuracy. Initial weights are assigned based on predefined rules, and the iterative process adjusts these weights to achieve optimal performance. The paper also addresses the challenge of interpreting multiple confidence scores from the classification process, proposing a machine learning approach using Scikit-learn's GradientBoostingClassifier to calculate a unified confidence score. This score helps assess the classification reliability, particularly for unlabeled data, by transforming input values, generating polynomial parameters, and using logarithmic transformations and scaling. The classifier is fine-tuned using hyper parameter optimization techniques, and the final model provides a robust confidence score for classification tasks, enabling the verification and classification results optimization across large datasets. Our experimental results demonstrate significant improvements in classification performance. Overall classification accuracy nearly doubled after training, reaching 92.38 %. This research not only advances the theoretical framework of hierarchical classifiers but also provides practical solutions for processing large-scale, unlabeled datasets in the automotive industry. The developed methodology can enhance various applications, including automated customer support systems, predictive maintenance, and decision-making processes for stakeholders like insurance companies and service centers. Future work will extend this approach to more complex tasks, such as extracting and classifying information from extensive text sources like telephone call transcriptions.

Groundwater quality assessment using revised classical diagrams and compositional data analysis (CoDa): Case study of Wadi Ranyah, Saudi Arabia

This research aims to assess the status and chemical properties of groundwater in the shallow aquifer of the Wadi Ranyah region, Saudi Arabia, using robust statistical methods (CoDA) that take into account the unique characteristics of chemical analysis data from forty-five (45) groundwater samples collected in the Wadi Ranyah valley. A centered logarithmic transformation (clr) approach and isometric logarithmic transformation (ilr) plots for compositional data were utilized to determine the different water types and to understand the hydrogeochemical processes influencing groundwater chemistry in the region. The results of principal component analysis and K-means clustering reveal the existence of three groundwater groupings: (i) The first group occupies the recharge region with a relationship between trace metal elements (F, Pb, Mn, Zn), pH, and TDS, indicating the effect of alteration of igneous and metamorphic rock minerals. (ii) The second group characterizes the downstream part of the Al-Hujrah area with a relationship between calcium and nitrates, indicating the influence of agricultural activities. (iii) The third group, located in the Ranyah area, shows a relationship between major ions (Na+, SO42−, Mg2+, HCO3–, Cl−, K+), which explains water mineralization influenced by silicate weathering and evaporation phenomena. According to the modified Piper diagram (ilr Piper), two hydrochemical facies are identified. The upstream region is categorized as Ca-HCO3– while the downstream region is classified as Na-K-HCO3–. Calculated water quality index (WQI) values indicate that all Wadi Ranyah groundwater samples are of very poor quality (WQI > 300). This is due to higher concentrations of polluting elements (Pb, Mn and F), in particular the high lead concentrations which vary between 0.24 and 0.4 mg/L, and are well above the WHO limit (0.01 mg/L) for consumption of drinking water. The application of CoDA for the first time in the study region has provided a more detailed and reliable assessment of the mechanisms governing groundwater quality, permitting suggestions for the management of groundwater resources and defining future research needs.

Type 2 diabetes mellitus modifies and mediates the association between the visceral adiposity index and depression: A cross-sectional study using NHANES 2005–2018 data

BackgroundThe objective of this investigation was to explore the correlation between the visceral adiposity index (VAI) and depression, and to analyze how type 2 diabetes mellitus (T2DM) may influence this relationship. MethodsThis study included data of 12,378 participants sourced from the National Health and Nutrition Examination Survey (NHANES) 2005–2018. Utilizing multivariate logistic regression and restricted cubic spline (RCS) regression, we examined the correlation between VAI and depression. Additionally, we investigated the interactive and mediating effects of T2DM on the association between VAI and depression. ResultsControlling for all potential confounders, the Ln logarithmic transformation of VAI showed a significant positive correlation with depression [odds ratio (OR) = 1.16, 95 % confidence interval (CI): 1.01–1.35, P = 0.041]. T2DM exhibited a notable interaction effect on the relationship connecting lnVAI and depression (P for interaction = 0.013). Specifically, the T2DM group exhibited a notable positive correlation between lnVAI and depression (OR = 1.46, 95 % CI: 1.17–1.82, P < 0.001), whereas such correlation didn't reach statistical significance within the non-T2DM group. The RCS model revealed a J-shaped nonlinear link between lnVAI and depression, with an inflection point value of 0.052. Mediation analysis indicated that diabetes accounted for 8.0 % of the correlation between lnVAI and depression. Furthermore, sensitivity analysis confirmed the consistency of these findings. ConclusionsA J-shaped nonlinear dose-response relationship was observed between lnVAI and depression among American adults, with a threshold of 0.052. T2DM not only served as a mediator between the two variables but also modified their association.