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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.

The performance of text classification methods has improved greatly over the last decade for text instances of less than 512 tokens. This limit has been adopted by most state-of-the-research transformer models due to the high computational cost of analyzing longer text instances. To mitigate this problem and to improve classification for longer texts, researchers have sought to resolve the underlying causes of the computational cost and have proposed optimizations for the attention mechanism, which is the key element of every transformer model. In our study, we are not pursuing the ultimate goal of long text classification, i.e., the ability to analyze entire text instances at one time while preserving high performance at a reasonable computational cost. Instead, we propose a text truncation method called Text Guide, in which the original text length is reduced to a predefined limit in a manner that improves performance over naive and semi-naive approaches while preserving low computational costs. Text Guide benefits from the concept of feature importance, a notion from the explainable artificial intelligence domain. We demonstrate that Text Guide can be used to improve the performance of recent language models specifically designed for long text classification, such as Longformer. Moreover, we discovered that parameter optimization is the key to Text Guide performance and must be conducted before the method is deployed. Future experiments may reveal additional benefits provided by this new method.

Ferrites; MFe 2 O 4 ( M  = Co, Ni, Cu, Mg ,and Zn) nanocrystals were prepared using sucrose auto-combustion. X-ray diffraction showed complete formation of ferrites with tetragonal structure for CuFe 2 O 4 and cubic for all others. Fourier transform-infrared confirmed ferrites' formation, and the obtained data were used to estimate the different elastic properties. Transmission electron microscopy exhibited agglomerated spherically shaped clusters for CoFe 2 O 4 , MgFe 2 O 4 , and ZnFe 2 O 4 while NiFe 2 O 4 and CuFe 2 O 4 showed cubic morphology. Magnetic measurements via vibrating sample magnetometer revealed ferromagnetic properties of all ferrites except for ZnFe 2 O 4 indicating paramagnetic one. The coercivity measurements indicated magnetically hard ferrites for CoFe 2 O 4 and CuFe 2 O 4 , while others showed soft magnetic. ac-conductivity indicated semiconducting properties with a magnetic phase transition from ferro- to paramagnetic for all ferrites except for CoFe 2 O 4 . The deviation from Arrhenius plots at > 500 K revealed the conduction mechanism change from electron hopping to polaron conduction. This change was also proved via conductivity vs. frequency and through dielectric relaxation. Graphical abstract XRD indicated cubic structure for all the studied ferrites except for CuFe 2 O 4 which showed tetragonal structure.

Ternary hybrid composites; MFe 2 O 4 /PANI/MWCNTs (M = Co 2+ , Cu 2+ , Mg 2+ , Ni 2+ , and Zn 2+ ) were synthesized using sucrose-assisted auto-combustion route followed by in situ polymerization. The newly developed composites indicated improved thermal stability as well as high conductivity and dielectric constants. XRD and FT-IR measurements indicated some type of interaction between the composite components. TEM exhibited the adsorption of MFe 2 O 4 to the MWCNTs, followed by their complete coating with PANI layers. This interaction between composites’ components leads to improving t the PANI component’s thermal stability. VSM indicated an obvious reduction in the magnetization owing to the complete coating of magnetic MFe 2 O 4 with non-magnetic PANI. Electrical properties measurements revealed an improvement in the ferrites’ conductivity by more than 1000 times than the individual ferrites, with the predominance of the semiconducting properties of all composites. The dielectric constants were also found to be improved by the inclusion of ferrites in their composites. Graphical abstract

In this work, a series of Sr-substituted lanthanum orthoferrite perovskites, La 1− x Sr x FeO 3 ( x  = 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0), were prepared using the sucrose-assisted auto-combustion route to study the effect of Sr-substitution on the structural, magnetic, and electrical properties and to investigate the impact of the entire method on different properties. The auto-combustion process and the perovskites formation were followed using differential thermal analysis–thermogravimetry techniques. The obtained different phases were characterized using X-ray diffractometer (XRD), Fourier transform infrared spectroscopy, and high-resolution transmission electron microscopy HRTEM measurements. XRD revealed peaks attributed to SrCO 3 secondary phase increases in their intensity by increasing Sr-content till predominate at x  = 1.0. It also showed a transfer from orthorhombic symmetry to rhombohedral one by increasing Sr-content. The obvious contraction in the unit cell parameters by Sr-substitution could be attributed to the Fe 3+  → Fe 4+ oxidation occurred to balance the total charge on molecule. The obvious increase in the magnetization by increasing Sr could be attributed to the formation of Fe 4+ ions, strengthening the ferromagnetic component through sharing in the double-exchange interaction, Fe 4+ –O–Fe 3+ , as well as the formation of oxygen vacancies that disturb the uncompensated surface spin. Ac-conductivity measurements indicated a change in the entire conduction mechanism from electronic to ionic with improving conductivity by increasing Sr-content. Generally, the utilized sucrose method indicated an improvement in the obtained magnetization accompanied by lowering conductivity than previously reported systems in literature.

In this manuscript, ternary hybrid composites (PMG) have been synthesized via combination of polypyrrole (PPy), Mn-Zn ferrite (MZF) and graphite oxide (GO) components. The MZF was synthesized via acid leaching process of spent Zn-C batteries and green auto-combustion processes using different fuels. The in-situ polymerization process was then used to prepare the entire composites. XRD of ternary hybrids showed only broad peak characteristic of PPy without any indication for the MZF or GO suggesting the inclusion of MZF and GO into PPy matrix. In addition, the TEM images and EDS analysis exhibited that both MZF particles and GO layers are embedded and completely coated with PPy layers. FT-IR spectra of PMG composites indicated only identical bands to that of pure PPy. The very weak magnetic characters obtained using VSM further confirming the core-shell structure. TG curve of PMG ternary composite showed weight loss agrees well with the loss of all PPy and GO contents and confirms the weight ratio of MZF. It also showed that, the thermal stability of PPy was reduced by including in this entire composite attributed to the formed core-shell structure. An appropriate mechanism for the ternary hybrid composites formation was suggested and discussed. The electrical measurements exhibited constant behavior of conductivity by increasing temperature up to 420 K at which the dehydration and skeletal chain decomposition of both GO and PPy initiate. The GO does not greatly affected the conductivity due to the complete coating phenomena besides the pronouncing of PPy conductivity. Generally, the conductivities for all PMG composites are slightly dependent on the MZF preparation method and improved than that of pure PPy attributed to the presence of GO. Graphical Abstract Schematic diagram for the formation process of PPy/Mn 0.8 Zn 0.2 Fe 2 O 4 /GO (PMG) composites via in-situ polymerization process. Research highlights Mn0.8Zn0.2Fe2O4 was prepared using spent Zn-C batteries using variety of auto-combustion methods. PPy/Mn0.8Zn0.2Fe2O4/GO (PMG) composites were prepared via in-situ polymerization. An appropriate mechanism for the composites formation was suggested and discussed. XRD indicated the complete coating of GO and MZF with the PPy matrix. Effect of MZF preparation method on different properties could be neglected due to the coreshell structure formed. Electrical measurements indicated that, GO does not greatly affected conductivity due tocomplete coating phenomena.

In the present work, the prepared Mn 0.8 Zn 0.2 Fe 2 O 4 ferrites (MZF) via recycling process of Zn–C batteries and auto-combustion route (using different fuels) were used to synthesize MZF/polypyrroly (PPy) nanocomposites via the in-situ polymerization technique. The core–shell structure formed was confirmed using X-ray diffraction, transmission electron microscopy and Fourier transform infrared techniques. The thermogravimetric measurements suggests that the presence of MZF was found to catalyze PPy thermal decomposition and decreasing its thermal stability through increasing its exposed surface. The core–shell structure was found also to vanish the MZF magnetic properties through the insulation effect of the non-magnetic PPy coat. A possible schematic diagram for the core–shell formation mechanism was suggested and discussed. AC-conductivity vs. temperature clearly reveals a metallic behavior of all the samples with a dramatic increase in the MZF conductivities by addition of PPy. The main conduction mechanism was found to be through polarons. The higher dielectric values obtained suggests their use as a microwave absorbing materials besides being a promising candidates in the electromagnetic shielding applications. Generally, we can conclude that, the complete coating of MZF particles with PPy not only greatly influence the magnetic property but also greatly affected and improved the electrical properties. The ferrites’ preparation method was found not to affect the structural, magnetic or electrical properties owing to the core–shell structure formed.

Herein, ternary composites from polypyrrole (PPy), Mn0.8Zn0.2Fe2O4 (MZF), and graphite oxide (GO) were prepared to remove acid red dye (AR1) from wastewater. MZF was synthesized using spent Zn–C batteries, acid leaching, and sucrose auto-combustion processes; GO was prepared via oxidation and exfoliation of graphite. The composites were prepared by adding MZF and GO during the in-situ polymerization of pyrrole. Different PPy/MZF/GO (PMG) composites were prepared by changing the weight ratios of the PPy, MZF, and GO. We investigated the prepared composites’ structural, magnetic, and electrical/dielectric properties. We evaluated different experimental conditions’ influences on dye removal performance, such as pH, dosage, dye concentration, temperature, and contact time. XRD, FT-IR, and magnetic properties indicated that PPy completely coated the other contents. The electrical/dielectric properties improved while increasing the GO ratio. The PMG at GO content 50 wt.% (PMG50) showed the most efficient ratio for better removing AR1 from wastewater.

We have synthesized cobalt ferrite (CoFe 2 O 4 ) using the sucrose auto-combustion method and subsequently employed the in situ polymerization technique to fabricate ternary composites comprising CoFe 2 O 4 , polyaniline (PANI), and multi-walled carbon nanotubes (MWCNTs). In this novel investigation, we explored the influence of varying MWCNTs ratios on these composites’ structural, magnetic, thermal, and electrical properties. The crystal structures of the synthesized composites were analyzed using X-ray diffraction (XRD), while Fourier transform infrared (FT-IR) spectroscopy revealed changes in bonding patterns, including the disappearance of ferrite bonds and the emergence of new ones. Transmission electron microscopy (TEM) images illustrated a complete coating of PANI on both MWCNTs and CoFe 2 O 4 particles, resulting in a substantial reduction in magnetization compared to pure CoFe 2 O 4 ferrite due to PANI’s nonmagnetic nature. Vibrating sample magnetometer (VSM) measurements confirmed this reduction, indicating a decrease to 7.3 emu.g −1 . Thermal analysis demonstrated an enhancement in thermal stability with increasing MWCNTs content, as evidenced by an increase in the temperature equivalent for half decomposition (T 50 ) from 486 to 522 °C for composites with 40% MWCNTs. Moreover, the electrical conductivity showed a corresponding rise with MWCNTs content, increasing from 3.1 × 10 −3 Ω −1 .cm −1 to 2.2 × 10 −2 Ω −1 .cm −1 , possibly indicating charge transfer from PANI to MWCNTs. To assess practical applications, we investigated the ability of the composite with 40% MWCNTs to remove phenol red (PR) dye from aqueous solutions. Through a systematic study of adsorption parameters and kinetics, we determined optimal conditions for effective dye removal and elucidated the underlying adsorption mechanism. Our results demonstrated the composite’s efficiency in dye removal, with a 6.4 mg·g −1 capacity for PR dye, highlighting its potential for environmental remediation efforts.