Category: Nevin Manimala

Sci Rep. 2023 Jan 31;13(1):1727. doi: 10.1038/s41598-023-28874-9.

ABSTRACT

The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individual’s chances of finding a job in a timely manner and rests upon data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual’s actual skills and can augment this with knowledge of the individual’s group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model’s dynamics-especially fairness-related issues and trade-offs between different fairness goals- over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.

PMID:36721013 | DOI:10.1038/s41598-023-28874-9

Sci Rep. 2023 Jan 31;13(1):1801. doi: 10.1038/s41598-023-28750-6.

ABSTRACT

Although computer vision syndromes are becoming a major public health concern, less emphasis is given to them, particularly in developing countries. There are primary studies on different continents; however, there are inconsistent findings in prevalence among the primary studies. Therefore, this systematic review and meta-analysis aimed to estimate the pooled prevalence of computer vision syndrome. In this study, the review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Online electronic databases, including PubMed/Medline, CINAHL, and Google Scholar, were used to retrieve published and unpublished studies. The study was conducted from December 1 to April 9/2022. Study selection, quality assessment, and data extraction were performed independently by two authors. Quality assessment of the studies was performed using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument tool. Heterogeneity was assessed using the statistical test I². STATA 14 software was used for statistical analysis. A total of 7,35 studies were retrieved, and 45 studies were included in the final meta-analysis. The pooled prevalence of computer vision syndrome was 66% (95% CI: 59, 74). Subgroup analysis based on country was highest in Pakistan (97%, 95% CI: 96, 98) and lowest in Japan (12%, 95% CI: 9, 15). Subgroup analysis based on country showed that studies in Saudi Arabia (I² = 99.41%, p value < 0.001), Ethiopia (I² = 72.6%, p value < 0.001), and India (I² = 98.04%, p value < 0.001) had significant heterogeneity. In the sensitivity analysis, no single study unduly influenced the overall effect estimate. Nearly two in three participants had computer vision syndrome. Thus, preventive practice strategic activities for computer vision syndrome are important interventions.

PMID:36720986 | DOI:10.1038/s41598-023-28750-6

Sci Rep. 2023 Jan 31;13(1):1805. doi: 10.1038/s41598-023-28970-w.

ABSTRACT

The key to most subsurface processes is to determine how structural and topological features at small length scales, i.e., the microstructure, control the effective and macroscopic properties of earth materials. Recent progress in imaging technology has enabled us to visualise and characterise microstructures at different length scales and dimensions. However, one limitation of these technologies is the trade-off between resolution and sample size (or representativeness). A promising approach to this problem is image reconstruction which aims to generate statistically equivalent microstructures but at a larger scale and/or additional dimension. In this work, a stochastic method and three generative adversarial networks (GANs), namely deep convolutional GAN (DCGAN), Wasserstein GAN with gradient penalty (WGAN-GP), and StyleGAN2 with adaptive discriminator augmentation (ADA), are used to reconstruct two-dimensional images of two hydrothermally rocks with varying degrees of complexity. For the first time, we evaluate and compare the performance of these methods using multi-point spatial correlation functions-known as statistical microstructural descriptors (SMDs)-ultimately used as external tools to the loss functions. Our findings suggest that a well-trained GAN can reconstruct higher-order, spatially-correlated patterns of complex earth materials, capturing underlying structural and morphological properties. Comparing our results with a stochastic reconstruction method based on a two-point correlation function, we show the importance of coupling training/assessment of GANs with higher-order SMDs, especially in the case of complex microstructures. More importantly, by quantifying original and reconstructed microstructures via different GANs, we highlight the interpretability of these SMDs and show how they can provide valuable insights into the spatial patterns in the synthetic images, allowing us to detect common artefacts and failure cases in training GANs.

PMID:36720975 | DOI:10.1038/s41598-023-28970-w

Sci Rep. 2023 Jan 31;13(1):1767. doi: 10.1038/s41598-022-24745-x.

ABSTRACT

Dust is an inherent byproduct of mining activities that raises notable health and safety concerns. Cumulative inhalation of respirable coal mine dust (RCMD) and respirable crystalline silica (RCS) can lead to obstructive lung diseases. Despite considerable efforts to reduce dust exposure by decreasing the permissible exposure limits (PEL) and improving the monitoring techniques, the rate of mine workers with respiratory diseases is still high. The root causes of the high prevalence of respiratory diseases remain unknown. This study aimed to investigate contributing factors in RCMD and RCS dust concentrations in both surface and underground mines. To this end, a data management approach is performed on MSHA’s database between 1989 and 2018 using SQL data management. In this process, all data were grouped by mine ID, and then, categories of interests were defined to conduct statistical analysis using the generalized estimating equation (GEE) model. The total number of 12,537 and 9050 observations for respirable dust concentration are included, respectively, in the U.S. underground and surface mines. Several variables were defined in four categories of interest including mine type, geographic location, mine size, and coal seam height. Hypotheses were developed for each category based on the research model and were tested using multiple linear regression analysis. The results of the analysis indicate higher RCMD concentration in underground compared to RCS concentration which is found to be relatively higher in surface coal mines. In addition, RCMD concentration is seen to be higher in the Interior region while RCS is higher in the Appalachia region. Moreover, mines of small sizes show lower RCMD and higher RCS concentrations. Finally, thin-seam coal has greater RCMD and RCS concentrations compared to thicker seams in both underground and surface mines. In the end, it is demonstrated that RCMD and RCS concentrations in both surface and underground mines have decreased. Therefore, further research is needed to investigate the efficacy of the current mass-concentration-based monitoring system.

PMID:36720966 | DOI:10.1038/s41598-022-24745-x

Sci Rep. 2023 Jan 31;13(1):1740. doi: 10.1038/s41598-023-28991-5.

ABSTRACT

The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mahalanobis distances (MD). We demonstrate this method on landslide susceptibility mapping of three subdistricts (Upazilas) of the Rangamati district, Bangladesh, and compare the results with the landslide susceptibility map produced based on the slope-based absence data sampling method. Using the 15 landslide causal factors, including slope, aspect, and plan curvature, we first determine the critical value of 23.69 based on the Chi-square distribution with 14 degrees of freedom. This critical value was then used to determine the sampling space for 261 random absence data. In comparison, we chose another set of the absence data based on a slope threshold of < 3°. The landslide susceptibility maps were then generated using the random forest model. The Receiver Operating Characteristic (ROC) curves and the Kappa index were used for accuracy assessment, while the Seed Cell Area Index (SCAI) was used for consistency assessment. The landslide susceptibility map produced using our proposed method has relatively high model fitting (0.87), prediction (0.85), and Kappa values (0.77). Even though the landslide susceptibility map produced by the slope-based sampling also has relatively high accuracy, the SCAI values suggest lower consistency. Furthermore, slope-based sampling is highly subjective; therefore, we recommend using MD -based absence data sampling for landslide susceptibility mapping.

PMID:36720965 | DOI:10.1038/s41598-023-28991-5

Sci Rep. 2023 Jan 31;13(1):1723. doi: 10.1038/s41598-023-27613-4.

ABSTRACT

Fiber reinforced polymer (FPR) bars have been widely used as a substitutional material of steel reinforcement in reinforced concrete elements in corrosion areas. Shear resistance of FRP reinforced concrete element can be affected by concrete properties and transverse FRP stirrups. Hence, studying the shear strength (V_s) mechanism is one of the highly essential for pre-design procedure for reinforced concrete elements. This research examines the ability of three machine learning (ML) models called M5-Tree (M5), extreme learning machine (ELM), and random forest (RF) in predicting V_s of 112 shear tests of FRP reinforced concrete beam with transverse reinforcement. For generating the prediction matrix of the developed ML models, statistical correlation analysis was conducted to generate the suitable inputs models for V_s prediction. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models. The results revealed that all the proposed models performed in general well for all the input combinations. However, ELM-M1 and M5-Tree-M5 models exhibited less accuracy performance in comparison with the other developed models. The study showed that the best prediction performance was revealed by M5 tree model using nine input parameters, with coefficient of determination (R²) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively. The comparison results also indicated that ELM and RF were performed significant results with a less slight performance than M5 model. The study outcome contributes to basic knowledge of investigating the impact of stirrups on V_s of FRP reinforced concrete beam with the potential of applying different computer aid models.

PMID:36720939 | DOI:10.1038/s41598-023-27613-4

Nat Hum Behav. 2023 Jan 31. doi: 10.1038/s41562-023-01523-x. Online ahead of print.

NO ABSTRACT

PMID:36720938 | DOI:10.1038/s41562-023-01523-x

Sci Rep. 2023 Jan 31;13(1):1802. doi: 10.1038/s41598-023-28816-5.

ABSTRACT

Three years after the pandemic, we still have an imprecise comprehension of the pathogen landscape and we are left with an urgent need for early detection methods and effective therapy for severe COVID-19 patients. The implications of infection go beyond pulmonary damage since the virus hijacks the host’s cellular machinery and consumes its resources. Here, we profiled the plasma proteome and metabolome of a cohort of 57 control and severe COVID-19 cases using high-resolution mass spectrometry. We analyzed their proteome and metabolome profiles with multiple depths and methodologies as conventional single omics analysis and other multi-omics integrative methods to obtain the most comprehensive method that portrays an in-depth molecular landscape of the disease. Our findings revealed that integrating the knowledge-based and statistical-based techniques (knowledge-statistical network) outperformed other methods not only on the pathway detection level but even on the number of features detected within pathways. The versatile usage of this approach could provide us with a better understanding of the molecular mechanisms behind any biological system and provide multi-dimensional therapeutic solutions by simultaneously targeting more than one pathogenic factor.

PMID:36720931 | DOI:10.1038/s41598-023-28816-5

Sci Rep. 2023 Jan 31;13(1):1752. doi: 10.1038/s41598-023-28466-7.

ABSTRACT

Current study aimed to evaluate presence & concentration of salivary molecular pain biomarkers Calcitonin Gene Related Peptide (CGRP) and Brain-Derived Neurotrophic Factor (BDNF) during initial stages of orthodontic treatment and correlation with subjective pain scales, Numerical Rating Scale (NRS), Visual Analogue Scale (VAS), Verbal Rating Scale (VRS) and McGill Pain Questionnaire (MPQ). Consented, healthy-pain free patients (n = 40) undergoing orthodontic therapy, having moderate crowding with pre-molar extraction were recruited. Unstimulated whole saliva was collected and stored at -80 °C in cryotubes. Levels of CGRP & BDNF in salivary samples was assessed by enzyme-linked immunosorbent assay. Samples were collected under stipulated 5 time periods using saliva collection tube by passive drooling method: immediately after bonding but before wire placement (T0-baseline), after 24 h (T1), 48 h (T2), 72 h (T3) & 168 h (T4) after wire placement. Consolidated subjective pain scales were administered concurrently. Regression value (R² > 0.9) confirmed BDNF & CGRP in saliva. Significant change was observed from baseline to 168 h in all subjective parameters (p < 0.05). CGRP did not correlate with subjective pain scales statistically (p > 0.05). BDNF levels correlated with all the subjective pain scales, NRS (T3-p = 0.0092&T4-p = 0.0064), VRS (T3-p = 0.0112&T4-p = 0.0500), VAS (T3-p = 0.0092 &T4-p = 0.0064) &MPQ (T1-p = 0.0255). Mean BDNF & median subjective pain scale graphs were similar. BDNF correlated with all the subjective pain scales warranting further investigation.Trial registration; Clinical Trial Registry-India (CTRI) Reg No: CTRI/2018/12/016571; Registered 10th December, 2018 (10/12/2018) prospectively; http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=29640&EncHid=&userName=Dr%20Sagar%20S%20Bhat .

PMID:36720924 | DOI:10.1038/s41598-023-28466-7

Nat Commun. 2023 Jan 31;14(1):514. doi: 10.1038/s41467-023-36080-4.

ABSTRACT

The scattering of exotic quasiparticles may follow different rules than electrons. In the fractional quantum Hall regime, a quantum point contact (QPC) provides a source of quasiparticles with field effect selectable charges and statistics, which can be scattered on an ‘analyzer’ QPC to investigate these rules. Remarkably, for incident quasiparticles dissimilar to those naturally transmitted across the analyzer, electrical conduction conserves neither the nature nor the number of the quasiparticles. In contrast with standard elastic scattering, theory predicts the emergence of a mechanism akin to the Andreev reflection at a normal-superconductor interface. Here, we observe the predicted Andreev-like reflection of an e/3 quasiparticle into a – 2e/3 hole accompanied by the transmission of an e quasielectron. Combining shot noise and cross-correlation measurements, we independently determine the charge of the different particles and ascertain the coincidence of quasielectron and fractional hole. The present work advances our understanding on the unconventional behavior of fractional quasiparticles, with implications toward the generation of novel quasi-particles/holes and non-local entanglements.

PMID:36720855 | DOI:10.1038/s41467-023-36080-4