Green leaching of cold filter cakes using choline chloride-maleic acid deep eutectic solvent and molecular dynamics simulation.

Deep eutectic solvents (DESs) represent a novel class of solvents characterized by their favorable biodegradability and compatibility, which have been used for dissolving various metals. In this study, choline chloride-maleic acid (ChCl-Me) DES was synthesized, and the FTIR technique was used to investigate the formation of DES. Then, ChCl-Me DES was applied to dissolve cold filter cakes (CFCs), and FTIR analysis proved the stability of DES during leaching.

Determining the characteristics of representative volume elements in severely deformed aluminum-matrix composite.

The accurate evaluation of the effective mechanical properties of composites mainly depends on the characteristics of representative volume elements (RVEs). This paper mainly investigates the RVE size. Additionally, the effect of volume fraction of reinforcement, the edge effect, and RVE types on the critical size are discussed.

Porous carbon/FeO nanocomposite as a new magnetically recoverable catalyst for the preparation of polyhydroquinolines.

A porous carbon/FeO nanocomposite (PC/FeO nanocomposite) was prepared through the pyrolysis of peanut shells as biowaste with ferrous ferric oxide to give a new magnetically recoverable catalyst. In the designed nanocomposite, magnetic iron oxide nanoparticles are properly distributed on the surface and cavities of porous carbon obtained from biomass. Increasing the active surface and magnetic recovery capability results in catalytic synergy, thereby promoting the reaction with an appropriate catalytic effect.

Fabrication of a novel graphene oxide based magnetic nanocomposite and its usage as a highly effectual catalyst for the construction of ,'-alkylidene bisamides.

At first, a novel graphene oxide-based magnetic nanocomposite namely Si-propyl-functionalized , , , -tetramethylethylenediamine- , -diium hydrogen sulfate anchored to graphene oxide-supported FeO (nano-[GO@FeO@R-NHMe][HSO]) was fabricated. After full characterization of the nanocomposite, its catalytic performance was examined for the solvent-free construction of ,'-alkylidene bisamides from aryl aldehydes (1 eq.) and primary aromatic and aliphatic amides (2 eq.

Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences.

Objective: This study delves into the impact of urban meteorological elements-specifically, air temperature, relative humidity, and atmospheric pressure-on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius), air pressure (in hectopascals), and relative humidity (in percent) were used for the statistical period 2017-2023. Various models, including the correlation coefficient, generalized additive models (GAM), generalized linear models (GLM), and support vector machines (SVM), were employed to scrutinize the data.

Sustainable utilization of mine wastes in mortar production as a part of aggregates: a case study on calcareous wastes of Angoran lead and zinc mine.

This study investigated the feasibility of large-scale utilizing calcareous wastes (CW) of Angoran lead and zinc mine as aggregates in mortar production with the maximum possible substitution of natural aggregates. The main goal was to produce mortar (concrete with fine aggregates as fine as sand or smaller) from Angoran mine's calcareous wastes for maintenance in its underground spaces. Compared to concrete, such mortars with better fluidity can enter narrow spaces more easily.

Adipokine modulation in obesity: Evaluating the integrative impact of chlorella vulgaris supplementation and interval resistance training in obese males.

Aims: To evaluate the effects of 12-week chlorella vulgaris (CV) combined with interval resistance training (IRT) on plasma levels of leptin, adiponectin and neuregulin-4 (Nrg-4) in obese men.

Methods: Obese men (n = 44, BMI of 32.1 ± 1.

Prediction of COVID-19 patients' participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches.

Background: Informal care plays an essential role in managing the COVID-19 pandemic. Expanding health insurance packages that reimburse caregivers' services through cost-sharing policies could increase financial resources. Predicting payers' willingness to contribute financially accurately is essential for implementing such a policy.

ConvLSNet: A lightweight architecture based on ConvLSTM model for the classification of pulmonary conditions using multichannel lung sound recordings.

Characterization of lung sounds (LS) is indispensable for diagnosing respiratory pathology. Although conventional neural networks (NNs) have been widely employed for the automatic diagnosis of lung sounds, deep neural networks can potentially be more useful than conventional NNs by allowing accurate classification without requiring preprocessing and feature extraction. Utilizing the long short-term memory (LSTM) layers to reveal the sequence-based properties of the LS time series, a novel architecture consisting of a cascade of convolutional long short-term memory (ConvLSTM) and LSTM layers, namely ConvLSNet is developed, which permits highly accurate diagnosis of pulmonary disease states.

Prediction of band inversion in Janus InXYZ (X, Y, and Z = S, Se, Te) monolayers.

In this work, the electronic and spin characteristics of Janus InXY and InXYZ (X, Y, and Z = S, Se, Te) monolayers are explored. The two sides of these Janus compounds have distinct vacuum levels due to their vertical asymmetry, which causes different work functions. An emerging dipole moment alters the band alignments on both surfaces of the Janus InXY structures due to electronegativity differences between various chalcogen atoms on each surface.

The preparation of [1,2,4]triazolo[1,5-]pyrimidines catalyzed by Schiff base zinc(ii) complex supported on magnetite nanoparticles under mild conditions.

Nano-[CuFeO@SiO/propyl-1-(-vanillinaldimine)][ZnCl] was prepared by placing a Schiff base zinc(ii) complex on a magnetite core and fully characterized by various analyses such as FT-IR, FE-SEM, EDAX, SEM-coupled EDX, TGA, VSM and TEM. The complexes supported on silica-coated magnetite copper ferrite nanoparticles were used as a reusable catalyst for the synthesis of 5-methyl-,7-diphenyl-4,7-dihydro-[1,2,4]triazolo[1,5-] pyrimidine-6-carboxamides resulting in 40% to 96% yield in the reactions of various aldehydes, acetoacetanilide, and 3-amino-1,2,4-triazole at 60 °C under solvent-free conditions. The zinc complex can change its structure from tetrahedral to square planar and catalyze the reaction.

Partial discharge localization in power transformer tanks using machine learning methods.

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank.

Modeling the influence of bacteria concentration on the mechanical properties of self-healing concrete (SHC) for sustainable bio-concrete structures.

In this research paper, the intelligent learning abilities of the gray wolf optimization (GWO), multi-verse optimization (MVO), moth fly optimization, particle swarm optimization (PSO), and whale optimization algorithm (WOA) metaheuristic techniques and the response surface methodology (RSM) has been studied in the prediction of the mechanical properties of self-healing concrete. Bio-concrete technology stimulated by the concentration of bacteria has been utilized as a sustainable structural concrete for the future of the built environment. This is due to the recovery tendency of the concrete structures after noticeable structural failures.

GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses.

Introduction: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep learning (DL) model optimized by an evolutionary algorithm (genetic algorithm/GA) to determine sex and age using paranasal sinus parameters based on cone-beam computed tomography (CBCT).

Multicomponent synthesis of pyrimido[4,5-b] quinolines over a carbocationic catalytic system.

Trityl chloride (TrCl) was efficiently used as a neutral catalyst for the multicomponent cyclization reaction of an aldehyde with dimedone and 6-amino-1,3-dimethyluracil for the preparation of pyrimido[4,5-b] quinolines in chloroform under reflux condition.

Dynamic causal modeling of reorganization of memory and language networks in temporal lobe epilepsy.

Objective: To evaluate the alterations of language and memory functions using dynamic causal modeling, in order to identify the epileptogenic hemisphere in temporal lobe epilepsy (TLE).

Methods: Twenty-two patients with left TLE and 13 patients with right TLE underwent functional magnetic resonance imaging (fMRI) during four memory and four language mapping tasks. Dynamic causal modeling (DCM) was employed on fMRI data to examine effective directional connectivity in memory and language networks and the alterations in people with TLE compared to healthy individuals.

CollectiveNet-AltSpec: A collective concurrent CNN architecture of alternate specifications for EEG media perception and emotion tracing aided by multi-domain feature-augmentation.

Enhancing computability of cerebral recordings and connections made with human/non-human brain have been on track and are expected to propel in our current era. An effective contribution towards said ends is improving accuracy of attempts at discerning intricate phenomena taking place within human brain. Here and in two different capacities of experiments, we attempt to distinguish cerebral perceptions shaped and affective states surfaced during observation of samples of media incorporating distinct audio-visual and emotional contents, through employing electroencephalograph/EEG recorded sessions of two reputable datasets of DEAP and SEED.

Increased noise relates to abnormal excitation-inhibition balance in schizophrenia: a combined empirical and computational study.

Electroencephalography studies link sensory processing issues in schizophrenia to increased noise level-noise here is background spontaneous activity-as measured by the signal-to-noise ratio. The mechanism, however, of such increased noise is unknown. We investigate if this relates to changes in cortical excitation-inhibition balance, which has been observed to be atypical in schizophrenia, by combining electroencephalography and computational modeling.

Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization.

Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation method is proposed for the fully automated grading of brain tumors in magnetic resonance (MR) images. First, the tumor is segmented based on two characteristics of the tumor appearance (intensity and edges information).

TiO/porous carbon as a new nanocomposite and catalyst for the preparation of 4-pyrimido[2,1-]benzimidazoles.

A nano TiO/porous carbon nanocomposite (TiO/PCN) was designed by the pyrolysis of peanut shells as bio waste with nano titanium dioxide. In the presented nanocomposite, titanium dioxide is properly placed in the positions and pores of the porous carbon, so that it acts as an optimal catalyst in the nanocomposite structure. The structure of TiO/PCN was studied by various analyses such as Fourier transform infrared spectroscopy (FT-IR), energy-dispersive X-ray Spectroscopy (EDX), scanning electron microscopy (SEM), SEM coupled EDX (SEM mapping), transmission electron microscopy (TEM), X-ray fluorescence (XRF) and BET.

Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model.

Background: Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential.

Biomass derived reduced-graphene-oxide supported α-FeO/ZnO S-scheme heterostructure: Robust photocatalytic wastewater remediation.

The emergence of new diseases and the unplanned industrialization of cities have led to new diseases and the subsequent use of antibiotics. Hence the remediation of wastewater containing antibiotics and their severe pollution has raised serious concerns in recent years. Herein coral-shaped α-FeO/ZnO/reduced graphene oxide (r-GO)-like carbon heterojunction in-situ were prepared from basil seed as a sustainable biomass resource and applied for the photodegradation of the oxytetracycline (OTC) as a typical antibiotic in a helical plug flow photoreactor (HPFPR) via persulfate activation under visible light irradiation.