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The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Objective: To identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients., Methods: We prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional connectivity and complexity of 5-minute EEG segments after each assessment. We used penalized regression to select EEG metrics associated with delirium or coma, and compared mixed-effects models predicting delirium with and without the selected EEG metrics., Results: Delta variability, high-beta variability, relative theta power, and relative alpha power contributed independently to EEG-based identification of delirium or coma. A model with these metrics achieved better prediction of delirium or coma than a model with clinical variables alone (Akaike Information Criterion: 36 vs 43, p = 0.006 by likelihood ratio test). The area under the receiver operating characteristic curve for an ad hoc hypothetical delirium score using these metrics was 0.94 (95%CI 0.83-0.99)., Conclusions: We identified four EEG metrics that, in combination, provided excellent discrimination between delirious/comatose and non-delirious mechanically ventilated ICU patients., Significance: Our findings give insight to neurophysiologic changes underlying delirium and provide a basis for pragmatic, EEG-based delirium monitoring technology., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: Many intensive care unit (ICU) survivors suffer disabling long-term cognitive impairment (LTCI) after critical illness. We compared EEG characteristics during critical illness with patients' 1-year neuropsychological outcomes., Methods: We performed a post hoc analysis of patients in the BRAIN-ICU study who had undergone EEG for clinical purposes during admission (n = 10). All survivors underwent formal cognitive assessments at 12-month follow-up. We evaluated EEGs by conventional visual inspection and computed 10 quantitative features. We explored associations between EEG and patterns of LTCI using Wilcoxon rank-sum tests and Spearman's rank correlations., Results: Of 521 Vanderbilt patients enrolled in the parent study, 24 had EEG recordings during admission. Ten survivors had EEG tracings available and completed follow-up cognitive testing. All but one inpatient EEG showed generalized background slowing. All patients demonstrated cognitive impairment in at least one domain at follow-up. The most common deficits occurred in delayed memory (DM-median index 62) and visuospatial/constructional (VC-median index 69) domains. Relative alpha power correlated with VC score (ρ = 0.78, P = .008). Peak interhemispheric coherence correlated negatively with DM (ρ = -0.81, P = .018)., Conclusions: Quantitative EEG features during critical illness correlated with domain-specific cognitive performance in our small cohort of ICU survivors. Further study in larger prospective cohorts is required to determine whether these relationships hold., Significance: EEG may serve as a prognostic biomarker predicting patterns of long-term cognitive impairment.

The inexorable industrialization and modern agricultural practices to meet the needs of the increasing population have polluted the environment with toxic heavy metals such as Cr(VI), Cu 2+ , Cd 2+ , Pb 2+, and Zn 2+ . Among the hazardous heavy metal(loid)s contamination in agricultural soil, water, and air, hexavalent chromium [Cr(VI)] is the most virulent carcinogen. The metallurgic industries, tanneries, paint manufacturing, petroleum refineries are among various such human activities that discharge Cr(VI) into the environment. Various methods have been employed to reduce the concentration of Cr(VI) contamination with nano and bioremediation being the recent advancement to achieve recovery at low cost and higher efficiency. Bioremediation is the process of using biological sources such as plant extracts, microorganisms, and algae to reduce the heavy metals while the nano-remediation uses nanoparticles to adsorb heavy metals. In this review, we discuss the various activities that liberate Cr(VI). We then discuss the various conventional, nano-remediation, and bioremediation methods to keep Cr(VI) concentration in check and further discuss their efficiencies. We also discuss the mechanism of nano-remediation techniques for better insight into the process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Recently, snake-like robots are proposed to assist experts during medical procedures on internal organs via natural orifices. Despite their well-spelt advantages, applications in radiosurgery is still hindered by absence of suitable designs required for spatial navigations within clustered and confined parts of human body, and inexistence of precise and fast inverse kinematics (IK) models. In this study, a deeply-learnt damped least squares method is proposed for solving IK of spatial snake-like robot. The robot's model consists of several modules, and each module has a pair of serial-links connected with orthogonal twists. For precise control of the robot's end-effector, damped least-squares approach is used to minimize error magnitude in a function modeled over analytical Jacobian of the robot. This is iteratively done until an apt joint vector needed to converge the robot to desired positions is obtained. For fast control and singularity avoidance, a deep network is built for prediction of unique damping factor required for each target point in the robot's workspace. The deep network consists of 11 x 15 array of neurons at the hidden layer, and deeply-learnt with a huge dataset of 877,500 data points generated from workspace of the snake robot. Implementation results for both simulated and actual prototype of an eight-link model of the robot show the effectiveness of the proposed IK method. With error tolerance of 0.01 mm, the proposed method has a very high reachability measure of 91.59% and faster mean execution time of 9.20 (±16.92) ms for convergence. In addition, the method requires an average of 33.02 (±39.60) iterations to solve the IK problem. Hence, approximately 3.6 iterations can be executed in 1 ms. Evaluation against popularly used IK methods shows that the proposed method has very good performance in terms of accuracy and speed, simultaneously., (Copyright © 2018 Elsevier Ltd. All rights reserved.)