Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.
The inherent complexities of Industrial Internet of Things (IIoT) architecture make its security and privacy issues becoming critically challenging. Numerous surveys have been published to review IoT security issues and challenges. The studies gave a general overview of IIoT security threats or a detailed analysis that explicitly focuses on specific technologies. However, recent studies fail to analyze the gap between security requirements of these technologies and their deployed countermeasure in the industry recently. Whether recent industry countermeasure is still adequate to address the security challenges of IIoT environment are questionable. This article presents a comprehensive survey of IIoT security and provides insight into today's industry countermeasure, current research proposals and ongoing challenges. We classify IIoT technologies into the four-layer security architecture, examine the deployed countermeasure based on CIA+ security requirements, report the deficiencies of today's countermeasure, and highlight the remaining open issues and challenges. As no single solution can fix the entire IIoT ecosystem, IIoT security architecture with a higher abstraction level using the bottom-up approach is needed. Moving towards a data-centric approach that assures data protection whenever and wherever it goes could potentially solve the challenges of industry deployment.
Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
MeSH terms: Humans; Lung; Sensitivity and Specificity; Thorax; X-Rays
Gellan-chitosan (GC) incorporated with CS: 0% (GC-0 CS), 10% (GC-10 CS), 20% (GC-20 CS) or 40% (GC-40 CS) w/w was prepared using freeze-drying method to investigate its physicochemical, biocompatible, and osteoinductive properties in human bone-marrow mesenchymal stromal cells (hBMSCs). The composition of different groups was reflected in physicochemical analyses performed using BET, FTIR, and XRD. The SEM micrographs revealed excellent hBMSCs attachment in GC-40 CS. The Alamar Blue assay indicated an increased proliferation and viability of seeded hBMSCs in all groups on day 21 as compared with day 0. The hBMSCs seeded in GC-40 CS indicated osteogenic differentiation based on an amplified alkaline-phosphatase release on day 7 and 14 as compared with day 0. These cells supported bone mineralization on GC-40 CS based on Alizarin-Red assay on day 21 as compared with day 7 and increased their osteogenic gene expression (RUNX2, ALP, BGLAP, BMP, and Osteonectin) on day 21. The GC-40 CS-seeded hBMSCs initiated their osteogenic differentiation on day 7 as compared with counterparts based on an increased expression of type-1 collagen and BMP2 in immunocytochemistry analysis. In conclusion, the incorporation of 40% (w/w) calcium silicate in gellan-chitosan showed osteoinduction potential in hBMSCs, making it a potential biomaterial to treat critical bone defects.
This work investigated the combined effects of CNF nucleation (3 wt.%) and PLA-g-MA compatibilization at different loadings (1-4 wt.%) on the crystallization kinetics and mechanical properties of polylactic acid (PLA). A crystallization kinetics study was done through isothermal and non-isothermal crystallization kinetics using differential scanning calorimetry (DSC) analysis. It was shown that PLA-g-MA had some effect on nucleation as exhibited by the value of crystallization half time and crystallization rate of the PLA/PLA-g-MA, which were increased by 180% and 172%, respectively, as compared to neat PLA when isothermally melt crystallized at 100 °C. Nevertheless, the presence of PLA-g-MA in PLA/PLA-g-MA/CNF3 nanocomposites did not improve the crystallization rate compared to that of uncompatibilized PLA/CNF3. Tensile strength was reduced with the increased amount of PLA-g-MA. Contrarily, Young's modulus values showed drastic increment compared to the neat PLA, showing that the addition of the PLA-g-MA contributed to the rigidity of the PLA nanocomposites. Overall, it can be concluded that PLA/CNF nanocomposite has good performance, whereby the addition of PLA-g-MA in PLA/CNF may not be necessary for improving both the crystallization kinetics and tensile strength. The addition of PLA-g-MA may be needed to produce rigid nanocomposites; nevertheless, in this case, the crystallization rate of the material needs to be compromised.
Natural rubber is of significant economic importance owing to its excellent resilience, elasticity, abrasion and impact resistance. Despite that, natural rubber has been identified with some drawbacks such as low modulus and strength and therefore opens up the opportunity for adding a reinforcing agent. Apart from the conventional fillers such as silica, carbon black and lignocellulosic fibers, nanocellulose is also one of the ideal candidates. Nanocellulose is a promising filler with many excellent properties such as renewability, biocompatibility, non-toxicity, reactive surface, low density, high specific surface area, high tensile and elastic modulus. However, it has some limitations in hydrophobicity, solubility and compatibility and therefore it is very difficult to achieve good dispersion and interfacial properties with the natural rubber matrix. Surface modification is often carried out to enhance the interfacial compatibilities between nanocellulose and natural rubber and to alleviate difficulties in dispersing them in polar solvents or polymers. This paper aims to highlight the different surface modification methods employed by several researchers in modifying nanocellulose and its reinforcement effects in the natural rubber matrix. The mechanism of the different surface medication methods has been discussed. The review also lists out the conventional filler that had been used as reinforcing agent for natural rubber. The challenges and future prospective has also been concluded in the last part of this review.
The wide availability and diversity of dangerous microbes poses a considerable problem for health professionals and in the development of new healthcare products. Numerous studies have been conducted to develop membrane filters that have antibacterial properties to solve this problem. Without proper protective filter equipment, healthcare providers, essential workers, and the general public are exposed to the risk of infection. A combination of nanotechnology and biosorption is expected to offer a new and greener approach to improve the usefulness of polysaccharides as an advanced membrane filtration material. Nanocellulose is among the emerging materials of this century and several studies have proven its use in filtering microbes. Its high specific surface area enables the adsorption of various microbial species, and its innate porosity can separate various molecules and retain microbial objects. Besides this, the presence of an abundant OH groups in nanocellulose grants its unique surface modification, which can increase its filtration efficiency through the formation of affinity interactions toward microbes. In this review, an update of the most relevant uses of nanocellulose as a new class of membrane filters against microbes is outlined. Key advancements in surface modifications of nanocellulose to enhance its rejection mechanism are also critically discussed. To the best of our knowledge, this is the first review focusing on the development of nanocellulose as a membrane filter against microbes.
This paper investigates the use of a Magnetite Polydimethylsiloxane (PDMS) Graphene array sensor in ultra-wide band (UWB) spectrum for microwave imaging applications operated within 4.0-8.0 GHz. The proposed array microwave sensor comprises a Graphene array radiating patch, as well as ground and transmission lines with a substrate of Magnetite PDMS-Ferrite, which is fed by 50 Ω coaxial ports. The Magnetite PDMS substrate associated with low permittivity and low loss tangent realized bandwidth enhancement and the high conductivity of graphene, contributing to a high gain of the UWB array antenna. The combination of 30% (ferrite) and 70% (PDMS) as the sensor's substrate resulted in low permittivity as well as a low loss tangent of 2.6 and 0.01, respectively. The sensor radiated within the UWB band frequency of 2.2-11.2 (GHz) with great energy emitted in the range of 3.5-15.7 dB. Maximum energy of 15.7 dB with 90 × 45 (mm) in small size realized the integration of the sensor for a microwave detection system. The material components of sensor could be implemented for solar panel.
In this investigation, biodegradable composites were fabricated with polycaprolactone (PCL) matrix reinforced with pine cone powder (15%, 30%, and 45% by weight) and compatibilized with graphite powder (0%, 5%, 10%, and 15% by weight) in polycaprolactone matrix by compression molding technique. The samples were prepared as per ASTM standard and tested for dimensional stability, biodegradability, and fracture energy with scanning electron micrographs. Water-absorption and thickness-swelling were performed to examine the dimensional stability and tests were performed at 23 °C and 50% humidity. Results revealed that the composites with 15 wt % of pine cone powder (PCP) have shown higher dimensional stability as compared to other composites. Bio-composites containing 15-45 wt % of PCP with low graphite content have shown higher disintegration rate than neat PCL. Fracture energy for crack initiation in bio-composites was increased by 68% with 30% PCP. Scanning electron microscopy (SEM) of the composites have shown evenly-distributed PCP particles throughout PCL-matrix at significantly high-degrees or quantities of reinforcing.
Herein, we report recent developments in order to explore chitin and chitosan derivatives for energy-related applications. This review summarizes an introduction to common polysaccharides such as cellulose, chitin or chitosan, and their connection with carbon nanomaterials (CNMs), such as bio-nanocomposites. Furthermore, we present their structural analysis followed by the fabrication of graphene-based nanocomposites. In addition, we demonstrate the role of these chitin- and chitosan-derived nanocomposites for energetic applications, including biosensors, batteries, fuel cells, supercapacitors and solar cell systems. Finally, current limitations and future application perspectives are entailed as well. This study establishes the impact of chitin- and chitosan-generated nanomaterials for potential, unexplored industrial applications.
The demand for wound care products, especially advanced and active wound care products is huge. In this study, gellan gum (GG) and virgin coconut oil (VCO) were utilized to develop microemulsion-based hydrogel for wound dressing materials. A ternary phase diagram was constructed to obtain an optimized ratio of VCO, water, and surfactant to produce VCO microemulsion. The VCO microemulsion was incorporated into gellan gum (GG) hydrogel (GVCO) and their chemical interaction, mechanical performance, physical properties, and thermal behavior were examined. The stress-at-break (σ) and Young's modulus (YM) of GVCO hydrogel films were increased along with thermal behavior with the inclusion of VCO microemulsion. The swelling degree of GVCO hydrogel decreased as the VCO microemulsion increased and the water vapor transmission rate of GVCO hydrogels was comparable to commercial dressing in the range of 332-391 g m-2 d-1. The qualitative antibacterial activities do not show any inhibition against Gram-negative (Escherichia coli and Klebsiella pneumoniae) and Gram-positive (Staphylococcus aureus and Bacillus subtilis) bacteria. In vivo studies on Sprague-Dawley rats show the wound contraction of GVCO hydrogel is best (95 ± 2%) after the 14th day compared to a commercial dressing of Smith and Nephew Opsite post-op waterproof dressing, and this result is supported by the ultrasound images of wound skin and histological evaluation of the wound. The findings suggest that GVCO hydrogel has the potential to be developed as a biomedical material.
Graphene oxide (GO) is extensively studied as a template material for mesenchymal stem cell application due to its two-dimensional nature and unique functionalization chemistries. Herein, a new type of peptide-conjugated multilayer graphene oxide (peptide/m-GO film) was fabricated and used as biomaterial for culturing human Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs). The characterization of the peptide/m-GO films was performed, and the biocompatibility of the WJ-MSCs on the peptide/m-GO films was investigated. The results demonstrated that the peptide conjugate on the m-GO film did not hamper the normal growth of WJ-MSCs but supported the growth of WJ-MSCs after the 6-day culture period. In addition, the osteogenic differentiation of WJ-MSCs on the peptide/m-GO films was enhanced as compared with the parent m-GO film. Therefore, such peptide-conjugated m-GO films could provide a highly biocompatible and multifunctional 2D material to tailor the potential application of WJ-MSCs in bone tissue regeneration.
Polyelectrolyte membranes (PEMs) are a novel type of material that is in high demand in health, energy and environmental sectors. If environmentally benign materials are created with biodegradable ones, PEMs can evolve into practical technology. In this work, we have fabricated environmentally safe and economic PEMs based on sulfonate grafted sodium alginate (SA) and poly(vinyl alcohol) (PVA). In the first step, 2-acrylamido-2-methyl-1-propanesulphonic acid (AMPS) and sodium 4-vinylbenzene sulfonate (SVBS) are grafted on to SA by utilizing the simple free radical polymerization technique. Graft copolymers (SA-g-AMPS and SA-g-SVBS) were characterized by 1H NMR, FTIR, XRD and DSC. In the second step, sulfonated SA was successfully blended with PVA to fabricate PEMs for the in vitro controlled release of 5-fluorouracil (anti-cancer drug) at pH 1.2 and 7.4 and to remove copper (II) ions from aqueous media. Moreover, phosphomolybdic acids (PMAs) incorporated with composite PEMs were developed to evaluate fuel cell characteristics, i.e., ion exchange capacity, oxidative stability, proton conductivity and methanol permeability. Fabricated PEMs are characterized by the FTIR, ATR-FTIR, XRD, SEM and EDAX. PMA was incorporated. PEMs demonstrated maximum encapsulation efficiency of 5FU, i.e., 78 ± 2.3%, and released the drug maximum in pH 7.4 buffer. The maximum Cu(II) removal was observed at 188.91 and 181.22 mg.g-1. PMA incorporated with PEMs exhibited significant proton conductivity (59.23 and 45.66 mS/cm) and low methanol permeability (2.19 and 2.04 × 10-6 cm2/s).
Natural fibre as a reinforcing agent has been widely used in many industries in this era. However, the reinforcing agent devotes a better strength when embedded with a polymer matrix. Nevertheless, the characteristic of natural fibre and polymer matrix are in contrast, as natural fibre is hydrophilic, while polymer is hydrophobic in nature. Natural fibre is highly hydrophilic due to the presence of a hydroxyl group (-OH), while polymer matrix has an inherent hydrophobic characteristic which repels water. This issue has been fixed by modifying the natural fibre's surface using a chemical treatment combining an alkaline treatment and a silane coupling agent. This modifying process of natural fibre might reduce the attraction of water and moisture content and increase natural fibre surface roughness, which improves the interfacial bonding between these two phases. In this paper, the effect of alkaline and silane treatment has been proven by performing the mechanical test, Scanning Electron Micrograph (SEM), and Fourier Transform Infrared spectrometry (FTIR) to observe the surface structure. The chemical compositions and thermal properties of the composites have been obtained by performing Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) tests. 1.0% silane treatment displayed better strength performance as compared to other composites, which was proven by performing Scanning Electron Micrograph (SEM). The assumption is that by enduring chemical treatment, kenaf fibre composites could develop high performance in industry applications.
Polyhydroxyalkanoates (PHAs) are storage granules found in bacteria that are essentially hydroxy fatty acid polyesters. PHA molecules appear in variety of structures, and amongst all types of PHAs, polyhydroxybutyrate (PHB) is used in versatile fields as it is a biodegradable, biocompatible, and ecologically safe thermoplastic. The unique physicochemical characteristics of these PHAs have made them applicable in nanotechnology, tissue engineering, and other biomedical applications. In this review, the optimization, extraction, and characterization of PHAs are described. Their production and application in nanotechnology are also portrayed in this review, and the precise and various production methods of PHA-based nanoparticles, such as emulsion solvent diffusion, nanoprecipitation, and dialysis are discussed. The characterization techniques such as UV-Vis, FTIR, SEM, Zeta Potential, and XRD are also elaborated.
The aim of this work was to improve the processability of triglycidyl-p-aminophenol (TGPAP) epoxy resin. To achieve this improvement, a diluent, the diglycidyl ether of bisphenol F (DGEBF or BPF), was added to TGPAP, and the blended epoxy was then cured with 4, 4'-diaminodiphenyl sulfones (DDS). A response surface methodology (RSM) was used, with the target response being to achieve a blended resin with a high glass transition temperature (Tg) and maximum pot life (or processing window, PW). Characterization through dynamic mechanical thermal analysis (DMTA) and using a rheometer indicated that the optimum formulation was obtained at 55.6 wt.% of BPF and a stoichiometric ratio of 0.60. Both values were predicted to give Tg at 180 °C and a processing window of up to 136.1 min. The predicted values were verified, with the obtained Tg and processing window (PW) being 181.2 ± 0.8 °C and 140 min, respectively, which is close to the values predicted using the RSM.
Drilling mud's rheological characteristics, such as plastic viscosity and yield point, are adversely affected with an inappropriate mud formulation. Native starch is one of the most important components in water-based mud because it improves the rheological and filtration characteristics of the mud. The native starch stability under various temperature and exposure time regimes is an important concern for utilizing starch in oil and gas drilling operations. In this work, tapioca starch was modified using carboxymethylation for the first time in order to improve its performance in non-damaging water-based muds. The modified starch was characterized by Fourier-transform infrared spectroscopy and X-ray diffraction. The thermal stability was tested using thermal gravimetric analysis. Various mud blends were formulated based on the experimental design using response surface methodology (RSM) to investigate their performance at various temperature conditions. Thirty experimental runs were carried out based on the selected factors and responses considering the optimal (custom) design, and the results were analyzed through ANOVA. The Fourier-transform infrared spectroscopy and X-ray diffraction results confirmed the carboxymethylation of starch. The TGA analysis revealed strong thermal stability after modification. Additionally, the Power law model (PLM) described the obtained rheological data for the selected formulations, resulting in determination coefficients of more than 0.95. Furthermore, the examined samples showed a reduction in the flow behavior index from 0.30 to 0.21 and an increase in the consistency index from 5.6 to 15.1. Optimization and confirmation results revealed the adequacy of the generated empirical models for both plastic viscosity and yield point. The obtained consistency index values provided a direct relationship with the modified starch concentration, indicating an improvement in the cutting carrying capacity of mud. Based on the current literature survey, the studied formulation has not been reported in the literature.
A multi-objective optimization of in situ sol-gel process was conducted in preparing oil palm fiber-reinforced polypropylene (OPF-PP) composite for an enhancement of mechanical and thermal properties. Tetraethyl orthosilicate (TEOS) and butylamine were used as precursors and catalysts for the sol-gel process. The face-centered central composite design (FCCD) experiments coupled with response surface methodology (RSM) has been utilized to optimize in situ silica sol-gel process. The optimization process showed that the drying time after the in-situ silica sol-gel process was the most influential factor on silica content, while the molar ratio of TEOS to water gave the most significant effect on silica residue. The maximum silica content of 34.1% and the silica residue of 35.9% were achieved under optimum conditions of 21.3 h soaking time, 50 min drying time, pH value of 9.26, and 1:4 molar ratio of TEOS to water. The untreated oil palm fiber (OPF) and silica sol-gel modified OPF (SiO2-OPF) were used as the reinforcing fibers, with PP as a matrix and maleic anhydride grafted polypropylene (MAgPP) as a compatibilizer for the fiber-reinforced PP matrix (SiO2-OPF-PP-MAgPP) composites preparation. The mechanical and thermal properties of OPF-PP, SiO2-OPF-PP, SiO2-OPF-PP-MAgPP composites, and pure PP were determined. It was found that the OPF-S-PP-MAgPP composite had the highest toughness and stiffness with values of tensile strength, Young's modulus, and elongation at break of 30.9 MPa, 881.8 MPa, and 15.1%, respectively. The thermal properties analyses revealed that the OPF-S-PP-MAgPP exhibited the highest thermally stable inflection point at 477 °C as compared to pure PP and other composites formulations. The finding of the present study showed that the SiO2-OPF had the potential to use as a reinforcing agent to enhance the thermal-mechanical properties of the composites.
The application of epoxy adhesive is widespread in electronic packaging. Epoxy adhesives can be integrated with various types of nanoparticles for enhancing thermal conductivity. The joints with thermally conductive adhesive (TCA) are preferred for research and advances in thermal management. Many studies have been conducted to increase the thermal conductivity of epoxy-based TCAs by conductive fillers. This paper reviews and summarizes recent advances of these available fillers in TCAs that contribute to electronic packaging. It also covers the challenges of using the filler as a nano-composite. Moreover, the review reveals a broad scope for future research, particularly on thermal management by nanoparticles and improving bonding strength in electronic packaging.