In-situ remediation of total petroleum hydrocarbon (TPH) contaminated soils via Fenton oxidation is a promising approach. However, determining the proper injection amount of H2O2 and Fe source over the Fenton reaction in the complex geological conditions for in-situ TPH soil remediation remains a daunting challenge. Herein, we introduced a practical and novel approach using soft computational models, a multilayer perception artificial neural network (MPLNN), for predicting the TPH removal performance. In this study, we conducted 48 sets of TPH removal experiments using Fenton oxidation to determine the TPH removal performance of a wide range of different ground conditions and generated 336 data points. As a result, a negative Pearson correlation coefficient was obtained in the Fe injection mass and the natural presence of Fe mineral in the soil, indicating that the excess of Fe could significantly retarded the TPH removal performance in the Fenton reaction. In addition, the MPLNN model with 6-6-1 training using Scaled conjugate gradient backpropagation (SCG) with tangent sigmoid as the transfer function demonstrated a high accuracy for TPH removal prediction with the correlation determination of 0.974 and mean square error value of 0.0259. The optimized MPLNN model achieved less than 20% error for predicting TPH removal performance in actual TPH-contaminated soil via Fenton oxidation. Hence, the proposed MPLNN can be useful in improving the Fenton oxidation of TPH removal performance in-situ soil remediation.

Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential for cancer biomarker detection. However, achieving a delicate balance between signal sensitivity and operational stability, especially at the heterostructure interface, is crucial for practical immunosensors. Herein, porous carbon (PC) integration with Ti3C2Tx-MXene (MX) and gold nanoparticles (Au NPs) constructs a versatile immunosensing platform for detecting extracellular matrix protein-1 (ECM1), a breast cancer-associated biomarker. The inclusion of PC provided robust structural support, enhancing electrolytic diffusion with an expansive surface area while synergistically facilitating charge transfer with Ti3C2Tx. The biosensor optimized with 1.0 mg PC demonstrates a robust electrochemical redox response to the surface-bound thionine (th) redox probe, utilizing an inhibition-based strategy for ECM1 detection. The robust antibody-antigen interactions across the PC-integrated Ti3C2Tx-Au NPs platform (MX-Au-C-1) enabled robust ECM1 detection within 0.1-7.5 nM, with a low limit of detection (LOD) of 0.012 nM. The constructed biosensor shows improved operational stability with a 98.6 % current retention over 1 h, surpassing MXene-integrated (MX-Au) and pristine Au NPs (63.2 % and 44.3 %, respectively) electrodes. Moreover, the successful adaptation of the artificial neural network (ANN) model for predictive analysis of the generated DPV data further validates the accuracy of the biosensor, promising its future application in AI-powered remote health monitoring.

Emerging evidence indicates that gut microbial dysbiosis is associated with the development of antipsychotic-induced weight gain in schizophrenia (SZ). However, the exact taxonomic composition and functionality that constitute the \"obesogenic\" microbial profile remain elusive. Our retrospective survey identified two groups of the SZ population separated by BMI, with 1/3 of patients developing overweight/obesity after chronic antipsychotic treatment. Based on multi-omics analysis, we observed altered gut microbiota in SZ patients with overweight/obesity, characterized by a reduction in several beneficial bacteria genera, including Bacteroides, Parabacteroides, Akkermansia, and Clostridium. This microbial dysbiosis was accompanied by disrupted energy expenditure and nutritional metabolism, worsened metabolic indices, and reduced levels of beneficial metabolites, e.g. indole-3-carboxylic acid and propionic acid. Moreover, leveraging data from first-episode drug-naïve schizophrenia (FSZ) patients at one-month and one-year follow-up, both artificial neural network and random forest classifier-based prediction models demonstrated a strong ability of microbial profiles to predict antipsychotic-induced weight gain. Importantly, FSZ patients with higher relative abundance of Parabacteria distasonis were less susceptible to antipsychotic-induced weight gain. Thus, gut microbiota could serve as a noninvasive approach to predict antipsychotic-induced weight gain, guiding clinical antipsychotics administration and developing novel therapeutic strategies for weight management in SZ.

The dyeing process of textile materials is inherently intricate, influenced by a myriad of factors, including dye concentration, dyeing time, pH level, temperature, type of dye, fiber composition, mechanical agitation, salt concentration, mordants, fixatives, water quality, dyeing method, and pre-treatment processes. The intricacy of achieving optimal settings during dyeing poses a significant challenge. In response, this study introduces a novel algorithmic approach that integrates response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA) techniques for the precise fine-tuning of concentration, time, pH, and temperature. The primary focus is on quantifying color strength, represented as K/S, as the response variable in the dyeing process of polyamide 6 and woolen fabric, utilizing plum-tree leaves as a sustainable dye source. Results indicate that ANN (R2 ~ 1) performs much better than RSM (R2 > 0.92). The optimization results, employing ANN-GA integration, indicate that a concentration of 100 wt.%, time of 86.06 min, pH level of 8.28, and a temperature of 100 °C yield a K/S value of 10.21 for polyamide 6 fabric. Similarly, a concentration of 55.85 wt.%, time of 120 min, pH level of 5, and temperature of 100 °C yield a K/S value of 7.65 for woolen fabric. This proposed methodology not only paves the way for sustainable textile dyeing but also facilitates the optimization of diverse dyeing processes for textile materials.

UNASSIGNED: Keratoconus is a progressive eye condition in which the normally round cornea thins and bulges outwards into a cone shape. This irregular shape causes light to scatter in multiple directions as it enters the eye, leading to distorted vision, increased sensitivity to light and frequent changes in the prescription of glasses or contact lenses. Detecting keratoconus at an early stage is not only difficult but also challenging.
UNASSIGNED: The study has proposed an ensemble-based machine learning (ML) technique named KeratoEL to detect keratoconus at an early stage. The proposed KeratoEL model combines the basic machine learning algorithms, namely support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN). Before employing the ML model for keratoconus detection, the data set is first preprocessed manually by eliminating some features that don\'t contribute any significant value to predict the exact class. Moreover, the output features are labelled into three different classes and Extra Trees Classifier is used to find out the important features. Then, the features are sorted in descending order and top 45, 30, and 15 features are taken as input datasets against the output. Finally, different machine learning models are tested using the input datasets and performance metrics are measured.
UNASSIGNED: The proposed model obtains 98.0%, 98.9% and 99.8% accuracy for top 45, 30, and 15 number of features respectively. Overall experimental results show that the proposed ensemble model outperforms the existing machine learning models.
UNASSIGNED: The proposed KeratoEL model effectively detects keratoconus at an early stage by combining SVM, DT, RF, and ANN algorithms, demonstrating superior performance over existing models. These results underscore the potential of the KeratoEL ensemble approach in enhancing early detection and treatment of keratoconus.

Prevailing over the bottleneck of von Neumann computing has been significant attention due to the inevitableness of proceeding through enormous data volumes in current digital technologies. Inspired by the human brain\'s operational principle, the artificial synapse of neuromorphic computing has been explored as an emerging solution. Especially, the optoelectronic synapse is of growing interest as vision is an essential source of information in which dealing with optical stimuli is vital. Herein, flexible optoelectronic synaptic devices composed of centimeter-scale tellurium dioxide (TeO2) films detecting and exhibiting synaptic characteristics to broadband wavelengths are presented. The TeO2-based flexible devices demonstrate a comprehensive set of emulating basic optoelectronic synaptic characteristics; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), conversion of short-term to long-term memory, and learning/forgetting. Furthermore, they feature linear and symmetric conductance synaptic weight updates at various wavelengths, which are applicable to broadband neuromorphic computations. Based on this large set of synaptic attributes, a variety of applications such as logistic functions or deep learning and image recognition as well as learning simulations are demonstrated. This work proposes a significant milestone of wafer-scale metal oxide semiconductor-based artificial synapses solely utilizing their optoelectronic features and mechanical flexibility, which is attractive toward scaled-up neuromorphic architectures.

Exploring feasible and renewable alternatives to reduce dependency on traditional fossil-based plastics is critical for sustainable development. These alternatives can be produced from biomass, which may have large uncertainties and variabilities in the feedstock composition and system parameters. This study develops a modeling framework that integrates cradle-to-grave life cycle assessment (LCA) with a rigorous process model and artificial intelligence (AI) models to conduct uncertainty and variability analyses, which are highly time-consuming to conduct using only the process model. This modeling framework examines polylactic acid (PLA) produced from corn stover in the U.S. An analysis of uncertainty and variability was conducted by performing a Monte Carlo simulation to show the detailed result distributions. Our Monte Carlo simulation results show that the mean life-cycle Global Warming Potential (GWP) of 1 kg PLA is 4.3 kgCO2eq (P5-P95 4.1-4.4) for composting PLA with natural gas combusted for the biorefinery, 3.7 kgCO2eq (P5-P95 3.4-3.9) for incinerating PLA for electricity with natural gas combusted for the biorefinery, and 1.9 kgCO2eq (P5-P95 1.6-2.1) for incinerating PLA for electricity with wood pellets combusted for the biorefinery. Tradeoffs for different environmental impact categories were identified. Based on feedstock composition variations, two AI models were trained: random forest and artificial neural networks. Both AI models demonstrated high prediction accuracy; however, the random forest performed slightly better.

UNASSIGNED: Data clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self-organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4-64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.
UNASSIGNED: In this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two-dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.
UNASSIGNED: The adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.
UNASSIGNED: It is found that SOM is superior to or on par with other clustering approaches, like, k-means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.

Global warming caused by the use of fossil fuels is a common concern of the world today. It is of practical importance to conduct in-depth fundamental research and optimal design for modern engine combustors through high-fidelity computational fluid dynamics (CFD), so as to achieve energy conservation and emission reduction. However, complex hydrocarbon chemistry, an indispensable component for predictive modeling, is computationally demanding. Its application in simulation-based design optimization, although desirable, is quite limited. To address this challenge, we propose a methodology for representing complex chemistry with artificial neural networks (ANNs), which are trained with a comprehensive sample dataset generated by the Latin hypercube sampling (LHS) method. With a given chemical kinetic mechanism, the thermochemical sample data is able to cover the whole accessible pressure/temperature/species space in various turbulent flames. The ANN-based model consists of two different layers: the self-organizing map (SOM) and the back-propagation neural network (BPNN). The methodology is demonstrated to represent a 30-species methane chemical mechanism. The obtained ANN model is applied to simulate both a non-premixed turbulent flame (DLR_A) and a partially premixed turbulent flame (Flame D) to validate its applicability for different flames. Results show that the ANN-based chemical kinetics can reduce the computational cost by about two orders of magnitude without loss of accuracy. The proposed methodology can successfully construct an ANN-based chemical mechanism with significant efficiency gain and a broad scope of applicability, and thus holds a great potential for complex hydrocarbon fuels.

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.