Removal of hydrochlorothiazide from molecular liquids using carbon nanotubes: Radial basis function neural network modeling and culture algorithm optimization

Fabrication of date palm kernel biochar-sulfur (DPKB-S) for super adsorption of methylene blue dye from water

A novel form of biochar was created by dehydration of Date palm kernel with 85% sulfuric acid. It was examined how the newly produced biochar (DPKB-S) affected the aqueous solution's capacity to extract Methylene Blue (MB) dye. The prepared DPKB-S was categorized by BET, BJH, FT-IR, SEM, EDX, DSC, and TGA analyses. The ideal pH for the MB dye adsorption by DPKB-S is 8. With 0.75 g L-1 of DPKB-S and an initial concentration of 50 ppm MB dye, Date Palm Kernel Biochar-Sulfur (DPKB-S) had the highest removal percentage of 100%. The Langmuir and Freundlich isotherm models were used to investigate the collected data. Freundlich model is the model that best covers MB dye adsorption in DPKB-S at low concentrations (0.75-1.25 g L-1) and the Langmuir model at high concentrations (1.5-1.75 g L-1). The Langmuir model maximum adsorption capacity (Qm) of the DPKB-S was 1512.30 mg g-1. Furthermore, a variety of error function models were applied to investigate the isotherm models derived data, including Marquardt's percent standard deviation (MPSD), the sum of absolute errors (EABS), the sum of the errors squared (ERRSQ), root mean square errors (RMS), Chi-square error (X2), the average relative error (ARE), average percent errors (APE), and hybrid error function (HYBRID). Kinetic data were calculated by intraparticle diffusion (IPD), pseudo-second-order (PSO), pseudo-first-order (PFO), and film diffusion (FD) models. A PSO rate model with a strong correlation (R2 = 1.00) largely regulated the adsorption rate. The removal mechanism of MB dye by DPKB-S is based on the principle that these positively charged dyes are attracted by electrostatic attraction forces due to the growth in the number of negatively charged regions at basic pH value. According to the results, DPKB-S shows promise as an affordable and competent adsorbent for the adsorption of MB dye. It can be used frequently without experiencing a discernible decrease in adsorption efficiency.

Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms (HIOAs). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms (HIOAs) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach

In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.

Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis

Background

Chest CT is considered to be a more accurate method for diagnosing suspected patients. However, with the spread of the epidemic, traditional diagnostic methods have been unable to meet the requirements of efficiency and speed. Therefore, it is necessary to use artificial intelligence to help people make efficient and accurate judgments. A number of studies have shown that it is feasible to use deep learning methods to help people diagnose COVID-19. However, most of the existing methods are single-layer neural network structures, and their accuracy and efficiency need to be improved.

Method

In this scheme, a hybrid model is adopted. Firstly, the gray co-occurrence matrix is used to extract the features of the images, and then the extreme learning machine is used for classification.

Results

The experimental results show that the model proposed in this paper is feasible and can help medical staff to accurately determine suspected patients for subsequent isolation and treatment.

Adsorption Characteristics of Pristine and Magnetic Olive Stones Biochar with Respect to Clofazimine

Olive stone biochars (OSBC), both pristine and following magnetization (MAG-OSBC), were utilized as eco-friendly and cost-effective sorbents for the antituberculosis, clofazimine (CLOF). Morphologies, textures, surface functionalities, and thermal stabilities of both adsorbents were explored using SEM, EDX, TEM, BET, FT-IR, Raman, XRD and TGA analyses. SEM analysis showed meso- and macroporous surfaces. BET data showed that the MAG-OSBC possesses a larger surface area (33.82 m²/g) and pore volume. Batch adsorption studies were conducted following the experimental scenario of Box-Behnken (BB) design. The adsorption efficiency of both adsorbents was evaluated in terms of the % removal (%R) and the sorption capacity (q_e, mg/g). Dependent variables (%R and q_e) were maximized as a function of four factors: pH, sorbent dose (AD), the concentration of CLOF ([CLOF]), and contact time (CT). A %R of 98.10% and 98.61% could be obtained using OSBC and MAG-OSBC, respectively. Equilibrium studies indicated that both Langmuir and Freundlich models were perfectly fit for adsorption of CLOF. Maximum adsorption capacity (q_max) of 174.03 mg/g was obtained using MAG-OSBC. Adsorption kinetics could be best illustrated using the pseudo-second-order (PSO) model. The adsorption-desorption studies showed that both adsorbents could be restored with the adsorption efficiency being conserved up to 92% after the sixth cycles.