Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network's generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications.

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI

The new 'normal' defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses.

Controlled Release of Chlorhexidine Diacetate from a Porous Methacrylate System: Supercritical Fluid Assisted Foaming and Impregnation

The release of chlorhexidine diacetate (CX) from a self-curing polymeric system based on poly(ethylmethacrylate) and tetrahydrofurfurylmethacrylate (PEM/THFM) was developed in this study. Supercritical fluid assisted impregnation and foaming was employed for preparing porous CX-PEM/THFM drug release system. X-ray diffraction (XRD) and differential scanning calorimetry (DSC) show that the crystallinity of CX significantly decreased after supercritical processing, whilst Raman spectroscopy suggested a hydrogen bonding interaction between the CX and PEM in the product. A UV-Vis dissolution study revealed that the drug release rate is almost as seven times faster in the SCF processed drug delivery system than conventional cured samples.

Synthesis of poly(sebacic anhydride)-indomethacin controlled release composites via supercritical carbon dioxide assisted impregnation

Poly(sebacic anhydride), PSA and indomethacin drug composite (DC) formulations were prepared using supercritical CO(2) (sc-CO(2)) aided mixing. The effect of the experimental temperature and sebacic acid purity on the physical properties of PSA-indomethacin DCs was investigated using a range of analytical techniques. The nature of the PSA-indomethacin interaction in composites after processing in sc-CO(2) under various conditions was investigated using differential scanning calorimetry (DSC), Fourier transform infrared (FTIR) spectroscopy, and powder X-ray diffraction (XRD) methods, respectively. The results indicate that processing at 130 degrees C of a 4:1 (w/w) ratio PSA-indomethacin mixture, renders the indomethacin amorphous and dispersed within the polymer matrix. The primary interaction between PSA and indomethacin appears to be hydrogen bonding between the carboxylic acid OH of indomethacin and the carbonyl group of PSA. In vitro dissolution studies revealed that the processed composites exhibit a substantially enhanced dissolution rate compared to the physical mixtures. Also, through the control of experimental conditions, the initial burst effect of the drug release was largely alleviated. Instead, the erosion of PSA (zero order degradation) became the dominant factor in controlling the drug release rate.

Supercritical fluid assisted impregnation of indomethacin into chitosan thermosets for controlled release applications

Supercritical carbon dioxide (sc-CO(2)) was used to impregnate indomethacin (a non-steroidal anti-inflammatory drug) into chitosan thermosets for the preparation of controlled release formulations. The products were analyzed by a range of methods including powder X-ray diffraction (XRD) and scanning electron microscopy (SEM). The effects of the experimental temperature and pressure of the sc-CO(2) on the thermal behavior of chitosan-indomethacin drug composites (DCs) was investigated via differential scanning calorimeter (DSC). The interaction of chitosan and indomethacin after impregnation was then studied by Fourier transform infrared (FTIR) and Raman spectroscopy, respectively. Our results suggest that the supercritical fluid impregnation process results in indomethacin being amorphously dispersed within the chitosan matrix. FTIR data suggest that the aliphatic carbonyl group of indomethacin interacts with the NH(2) group of the chitosan backbone. In vitro dissolution studies (via UV-vis spectroscopy) reveal that the dissolution rate of indomethacin substantially increases after processing in sc-CO(2), particularly, under the experimental conditions 20.7 MPa and 70 degrees C.

Formation and characterization of porous indomethacin-PVP coprecipitates prepared using solvent-free supercritical fluid processing

Supercritical carbon dioxide (sc-CO2) was used to prepare coprecipitates of indomethacin (IM) and poly(vinylpyrrolidone) (PVP) with the aim to improve the dissolution rate of IM. The coprecipitates of IM and PVP at various proportions were prepared using a stirred batch reactor containing sc-CO2 as a gas saturated solution (i.e., the compressible CO2 is dissolved in the molten compound). Temperatures between 40 and 90 degrees C and pressure of 150 or 200 bar were employed. The coprecipitates prepared at 75 degrees C and 150 bar were characterized using differential scanning calorimetry (DSC), powder X-ray diffraction (PXD), scanning electron microscopy (SEM), and dissolution testing. The results suggested that IM was totally amorphous at PVP weight fraction of 0.80 and above (indeed, as a molecular composite in which the drug molecules interact with the polymer backbone). As the PVP weight fraction decreased, IM displayed an increasing amount of crystalline material. The SEM photographs of coprecipitates showed a foamed and porous structure. The dissolution rate of IM was increased by incorporation of PVP. IM and PVP at various weight fractions exhibited comparatively higher dissolution rates than that of crystalline IM alone. The sc-CO2 based process produced a solvent free, completely amorphous porous IM solid dispersion with a rapid dissolution rate.

Titania nanospheres from supercritical fluids

Surfactant-coated amorphous titania nanospheres have been synthesised using templating 'water-in-supercritical carbon dioxide' emulsion droplets; the process represents a clean and controlled method for the manufacture of high-purity nanoparticles.

Metal organic chemical vapour deposition (MOCVD) of bone mineral like carbonated hydroxyapatite coatingsElectronic supplementary information (ESI) available: experimental data. See http://www.rsc.org/suppdata/cc/b3/b312855p/

For the first time, the MOCVD technique has been used to deposit carbonated hydroxyapatite onto Ti6AL4V substrates using volatile monomeric (liquid) complexes [Ca(beta-diketonate)(2)(L)] and P(OEt)(3).

Biodegradable polyurethanes: biodegradable low adherence films for the prevention of adhesions after surgery

Adhesions commonly occur after internal disease or surgery. The natural healing response leads to the formation of vascular and avascular adhesions after inflammatory diseases and surgical interventions. A barrier film could be incorporated during surgery between layers of tissues that must not adhere to one another. The film would be biodegradable so that it disappears over a period of time, and would ideally be two sided, allowing relative movement at that interface, while being firmly anchored on the opposite side to prevent displacement. Polyesterurethane-polydimethylsiloxane graft polymers are synthesised. Chemical characterisation of the polymer is performed by using Fourier transform infrared spectroscopy and gel permeation chromatography. In vitro hydrolytic degradation is carried out in which films are immersed at 37 degrees C in alkaline solution. Degradation is assessed by tensile testing as a function of time to determine the degradation of mechanical strength, infrared spectroscopy, and mass loss. A titration method is also used to determine quantitatively the hydrolytic degradation. In order to study the adhesions of films, an in-vitro model based on a gelatine test, which is simple and rapid, is described. Suitable candidate films investigated from the in-vitro work are subjected to in vivo tests for both biodegradation and their ability to prevent adhesion.