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A new nonlinear technique for analysis of brain dynamics called spatiotemporal analysis of relative convergence (STARC) of electroencephalograms (EEGs) is introduced, based on the relative convergence of EEGs of different loci. This technique shows how many times EEGs of each loci pair converge together, which in turn is used as an indicator to determine the different neuronal regions involved in performing the same task. A higher STARC value indicates that more regions are recruited to perform the same task. The STARC methodology was used to reveal sex difference pathophysiology and brain dynamics, using EEG data from 11 male and 11 female adults with major depressive disorder (MDD). The results show significant differences in relative convergences of EEGs of intraleft temporal and frontoleft temporal lobes at δ band, between male and female patients.

Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and stereotyped behavior are the major autistic symptoms, visible after a certain age. It is one of the fastest growing disabilities. Its current prevalence rate in the U.S. estimated by the Centers for Disease Control and Prevention is 1 in 68 births. The genetic and physiological structure of the brain is studied to determine the pathology of autism, but diagnosis of autism at an early age is challenging due to the existing phenotypic and etiological heterogeneity among ASD individuals. Volumetric and neuroimaging techniques are explored to elucidate the neuroanatomy of the ASD brain. Nuroanatomical, neurochemical, and neuroimaging biomarkers can help in the early diagnosis and treatment of ASD. This paper presents a review of the types of autism, etiologies, early detection, and treatment of ASD.

This article presents a new methodology for investigation of the organization of the overall and hemispheric brain network of patients with attention-deficit hyperactivity disorder (ADHD) using theoretical analysis of a weighted graph with the goal of discovering how the brain topology is affected in such patients. The synchronization measure used is the nonlinear fuzzy synchronization likelihood (FSL) developed by the authors recently. Recent evidence indicates a normal neocortex has a small-world (SW) network with a balance between local structure and global structure characteristics. Such a network results in optimal balance between segregation and integration which is essential for high synchronizabilty and fast information transmission in a complex network. The SW network is characterized by the coexistence of dense clustering of connections (C) and short path lengths (L) among the network units. The results of investigation of C show the local structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is recognizable in the delta electroencephalograph (EEG) sub-band. Also, the results of investigation for L show the global structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is observable in the delta EEG sub-band. It is concluded that the changes in left-hemisphere brain's structure of ADHD from that of the non-ADHD are so much that L and C can distinguish the ADHD brain from the non-ADHD brain in the delta EEG sub-band.

Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.

The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem. The potential impact of these technologies on reducing carbon emissions and accelerating the transition to cleaner energy sources is a powerful incentive. With continuous innovation, informed decision‐making, and strategic implementation, the fusion of blockchain and artificial intelligence holds the promise of reshaping carbon trading into a more transparent, efficient, and sustainable practice.

The objective of this study is to evaluate the prevalence of electrographic seizures in hospitalized patients with altered mental status and no significant risk factors for seizures. We retrospectively reviewed over a six-year period (2013-2019) the medical records of all adults admitted at Ohio State University Wexner Medical Center (OSUWMC), who underwent continuous electroencephalography (cEEG) monitoring for > 48 hours. Our primary objective was to identify the prevalence of electrographic seizures in patients with altered mental status and no significant acute or remote risk factors for seizures. A total of 1966 patients were screened for the study, 1892 were excluded (96.2%) and 74 patients met inclusion criteria. Electrographic seizures were identified in seven of 74 patients (9.45%). We found a significant correlation between electrographic seizures and a history of hepatic cirrhosis, n= 4 (57%), (p=0.035), acute chronic hepatic failure during admission, 71% (n=5), (p=0.027), and hyperammonemia (p =0.009). In this retrospective study of patients with altered mental status and no significant acute or remote risk factors for seizures who underwent cEEG monitoring for > 48 hours, electrographic seizures were identified in 9.45%. Electrographic seizures were associated with hepatic dysfunction and hyperammonemia. Based on our results, cEEG monitoring should be considered in patients with altered mental status and hepatic dysfunction even in the absence of other seizure risk factors.

A man in his mid-20s developed three episodes of right facial weakness over 5 months. He had a history of B-cell acute lymphoblastic leukaemia (ALL) in remission following allogenic stem cell transplantation. MR scan of brain during the second presentation showed facial nerve enhancement; cerebrospinal fluid (CSF) cytology and flow cytometry were negative. Re-assessment at the third presentation identified CSF B-lymphoblasts, and he was subsequently treated for central nervous system relapse of leukaemia. This case highlights an infrequent presenting symptom of ALL relapse and a rare cause of recurrent facial nerve palsy.

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.

This paper proposes a geostatistical approach for geological modelling and for validating an interpreted geological model, by identifying the areas of an ore deposit with a high probability of being misinterpreted, based on quantitative coregionalised covariates correlated with the geological categories. This proposal is presented through a case study of an iron ore deposit at a stage where the only available data are from exploration drill holes. This study consists of jointly simulating the quantitative covariates with no previous geological domaining. A change of variables is used to account for stoichiometric closure, followed by projection pursuit multivariate transformation, multivariate Gaussian simulation, and conditioning to the drill hole data. Subsequently, a decision tree classification algorithm is used to convert the simulated values into a geological category for each target block and realisation. The determination of the prior (ignoring drill hole data) and posterior (conditioned to drill hole data) probabilities of categories provides a means of identifying the blocks for which the interpreted category disagrees with the simulated quantitative covariates.

Identifying mineralization zones is a critical component of quantifying the distribution of target minerals using well-established mineral resource estimation techniques. Domains are used to define these zones and can be modelled using techniques such as manual interpretation, implicit modelling, and advanced geostatistical methods. In practise, domaining is commonly a manual exercise that is labour-intensive and prone to subjective judgement errors, resulting in a largely deterministic output that ignores the significant uncertainty associated with manual domain interpretation and boundary definitions. Addressing these issues requires an objective framework that can automatically define mineral domains and quantify the associated uncertainty. This paper presents a comparative study of PluriGaussian Simulation (PGS) and a Hybrid Domaining Framework (HDF) based on simulated assay grades and XGBoost, a machine-learning classification technique trained on lithological properties. The two domaining approaches are assessed on the basis of the domain boundaries produced using data from an Iron Oxide Copper Gold deposit. The results show that the proposed HDF domaining framework can quantify the uncertainty of domain boundaries and accommodate complex multiclass problems with imbalanced features. Geometallurgical models of the Net Smelter Return and grinding time are used to demonstrate the effectiveness of HDF. In addition, a preprocessing step involving a noise filtering method is used to improve the performance of the ML classification, especially in cases where domain boundaries are difficult to predict due to the similarity in geological characteristics and the inherent noise in the data.