Fabrication and characterization of biomimetic dry adhesives supported by foam backing material

Using sacrificial templates to create 3D structures is commonly employed in various fields such as tissue engineering and water remediation to create complex and high surface area scaffolds. Herein, several sacrificial templating techniques are tried, tested, and evaluated and several methods for creating 3D porous material are discussed, including: solvent casting particulate leaching (SCPL) and simple sugar and salt leaching. The porous material is then integrated with polymer soft lithography patterning to create a single functionally graded adhesive (FGA) material to use in dry adhesive applications. The use of a soft foam backing layer helps to improve the compliance and flexibility of the adhesive pad, thus enhancing peel tolerance, buckling, and deflection and vibration resistance. A dry FGA based on film-terminated silicone foam is developed utilizing the polymer foam's capacity to absorb large amounts of energy and so deliver high adhesion and peel resistance. The fabrication technique is based on simple sugar cube templating of common elastomers, followed by film termination of the polymer cubes using the same material. Dependencies of the pull-off adhesive force and energy release rate on preload and foam thickness are systematically investigated through a series of axisymmetric indentation/de-bonding tests. The contribution of the foam backing layer to the overall compliance and adhesion is analysed and discussed. The developed elastic film-terminated structure strongly enhances the pull-off force and work of adhesion, and can be employed in the transport of delicate objects, as demonstrated in the pick and place of a silicon wafer. Furthermore, the proposed foam-based FGAs can be readily detached from the adherent surface by applying shear deformation between the pad and the surface. This research clarifies the role of mechanical graded properties in adhesion and can have technical implications in the development of a simple but effective dry adhesive material for mounting and transporting objects using automated robotic devices. The film terminated dry adhesive pads were further developed to investigate the feasibility of using a foam backing material as a universal platform to improve the adhesive properties of other terminal surface morphologies. Integrating other fast prototyping technologies as an alternative to lithographic templating techniques, scaled acrylonitrile butadiene styrene (ABS) 3D printed mushroom capped terminal structures are determined to be comparable to polyacrylate microstructure templated moulds. The effect of the foam is systematically evaluated using a similar axisymmetric indentation/de-bonding test with a probe of a large radius of curvature. Contact splitting through the control of terminal structures in both micro and millimetre scales shows improved contact properties with the addition of foam backing material. The mushroom capped adhesive pads are employed to demonstrate shear peel tolerance and cold temperature surface tolerance demonstrations. Lastly, various sugar and salt templating techniques are explored and optimized for consistency and repeatability to select the material most suitable for current research. Statistical analysis is used in the selection process. A linearly approximated model to determine the pull-off force from foam porosity and stiffness parameters are reported as sample candidates. Model estimates find that the density of sugar granules and the applied preload force are the mostly significant contributors to increasing pull-off force

Innovative Pedagogical Strategies in Health Professions Education: Active Learning in Dental Materials Science.

Dental materials science education is frequently delivered via traditional didactic lectures in preclinical dental programs. This review aimed to appraise the current evidence on innovative pedagogical strategies in teaching dental materials science courses. English-language articles on teaching methods for dental materials science published between January 1990 to October 2022 were searched in nine online databases (Google Scholar, PubMed, Web of Science [WoS], Science Direct, Cochrane Library, EBSCO, LILACS, Open Grey, and EMBASE) according to PRISMA guidelines. The risk of bias (RoB) was assessed using the Cochrane RoB-2 and ROBIN-I tools, whereas the level of evidence was determined based on the OCEBM guidelines. Only 12 primary studies were included. Two randomized studies (RCTs) were deemed as being of "some concern", and one showed a high risk of bias (RoB). Three non-randomized controlled studies (NRS) demonstrated a moderate RoB, whereas the remaining seven were low. Most studies were ranked at Levels 2 and 3 of evidence. Several innovative pedagogical strategies were identified: flipped classrooms, clinical-based learning, computer-assisted learning, group discussion, microteaching with the BOPPPS (bridge-in, learning objective, pre-test, participatory learning, post-test, and summary) model, and game-based learning. The evidence suggested that students generally showed positive perceptions toward these pedagogical strategies. Dental educators should revise their current undergraduate dental materials science curricula and integrate more effective teaching methods

Factor Affecting Adoption of E-Wallet in Sarawak

E-wallet was an innovative payment instrument that arises under financial technology. E-wallet helps to ease the user’s daily life, in which users can make their daily transactions without using the notes or coins. Indirectly, E-wallet also helps to reduce the risk of cash being stolen. Undeniably, E-wallet brings more benefits than disadvantages. The primary aim of this study is to examine the factors affecting the adoption of E-wallet services in Sarawak. The questionnaire, which consisted of 26 questions were distributed to the respondents and successfully collected 450 feedbacks. Firstly, this study applied factor analysis to construct all the variables. Also, Cronbach’s α coefficient was computed to determine internal consistency reliabilities. Then, this study used regression analysis to test the relationship between the variables. The results of the regression analysis showed that the users would adopt E-wallet when they perceive that the E-wallet is useful and easy to be used. Meanwhile, the findings of this study also showed that rewards tend to attract users to use E-wallet. Besides that, this study also found that higher perceived risk may act as a barrier to stop users from using E-wallet. These results help the E-wallet service providers to identify the significant factors that influence the user’s intention to use E-wallet services. Lastly, this study recommended the E-wallet service providers to take the security systems and rewards into consideration for the enhancement of their payment system

K-means Clustering In Knee Cartilage Classification: Data from the OAI

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model

Generating Images Instead of Retrieving Them : Relevance Feedback on Generative Adversarial Networks

Finding images matching a user’s intention has been largely basedon matching a representation of the user’s information needs withan existing collection of images. For example, using an exampleimage or a written query to express the information need and re-trieving images that share similarities with the query or exampleimage. However, such an approach is limited to retrieving onlyimages that already exist in the underlying collection. Here, wepresent a methodology for generating images matching the userintention instead of retrieving them. The methodology utilizes arelevance feedback loop between a user and generative adversarialneural networks (GANs). GANs can generate novel photorealisticimages which are initially not present in the underlying collection,but generated in response to user feedback. We report experiments(N=29) where participants generate images using four differentdomains and various search goals with textual and image targets.The results show that the generated images match the tasks andoutperform images selected as baselines from a fixed image col-lection. Our results demonstrate that generating new informationcan be more useful for users than retrieving it from a collection ofexisting information.Peer reviewe

Effectiveness of visible and ultraviolet light emitting diodes for inactivation of Staphylococcus aureus, Pseudomonas aeruginosa,and Escherichia coli: a comparative study

The rapid use of ultraviolet light emitting diodes (UV-LEDs) in various disinfection applications is growing tremendously due to their advantages unachievable using UV lamps. In this study, a comparison of standard LED at 460 nm wavelength and UVA LED at 385 nm was conducted to determine their effectiveness in disinfection of frequently isolated pathogens in hospitals (Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). Determination of disinfection efficiency was carried out by measuring inhibition zone. Effects of varied exposure time on the inactivation of pathogenic microorganisms was studied. The results demonstrated that LED does not have germicidal activities. The highest inactivation for UVA LED was achieved for Pseudomonas aeruginosa. Linear relationship was found between exposure time and log reduction. This study showed that UVA LEDs can effectively inactivate significantly higher number of microorganisms hence can be used in disinfection of various applications

Proteomic assessment of a cell model of spinal muscular atrophy

Background Deletion or mutation(s) of the survival motor neuron 1 (SMN1) gene causes spinal muscular atrophy (SMA), a neuromuscular disease characterized by spinal motor neuron death and muscle paralysis. Complete loss of the SMN protein is embryonically lethal, yet reduced levels of this protein result in selective death of motor neurons. Why motor neurons are specifically targeted by SMN deficiency remains to be determined. In this study, embryonic stem (ES) cells derived from a severe SMA mouse model were differentiated into motor neurons in vitro by addition of retinoic acid and sonic hedgehog agonist. Proteomic and western blot analyses were used to probe protein expression alterations in this cell-culture model of SMA that could be relevant to the disease. Results When ES cells were primed with Noggin/fibroblast growth factors (bFGF and FGF-8) in a more robust neural differentiation medium for 2 days before differentiation induction, the efficiency of in vitro motor neuron differentiation was improved from ~25% to ~50%. The differentiated ES cells expressed a pan-neuronal marker (neurofilament) and motor neuron markers (Hb9, Islet-1, and ChAT). Even though SMN-deficient ES cells had marked reduced levels of SMN (~20% of that in control ES cells), the morphology and differentiation efficiency for these cells are comparable to those for control samples. However, proteomics in conjunction with western blot analyses revealed 6 down-regulated and 14 up-regulated proteins with most of them involved in energy metabolism, cell stress-response, protein degradation, and cytoskeleton stability. Some of these activated cellular pathways showed specificity for either undifferentiated or differentiated cells. Increased p21 protein expression indicated that SMA ES cells were responding to cellular stress. Up-regulation of p21 was confirmed in spinal cord tissues from the same SMA mouse model from which the ES cells were derived. Conclusion SMN-deficient ES cells provide a cell-culture model for SMA. SMN deficiency activates cellular stress pathways, causing a dysregulation of energy metabolism, protein degradation, and cytoskeleton stability

Hybrid phishing detection using joint visual and textual identity

In recent years, phishing attacks have evolved considerably, causing existing adversarial features that were widely utilised for detecting phishing websites to become less discriminative. These developments have fuelled growing interests among security researchers towards an anti-phishing strategy known as the identity-based detection technique. Identity-based detection techniques have consistently achieved high true positive rates in a rapidly changing phishing landscape, owing to its capitalisation on fundamental brand identity relations that are inherent in most legitimate webpages. However, existing identity-based techniques often suffer higher false positive rates due to complexities and challenges in establishing the webpage’s brand identity. To close the existing performance gap, this paper proposes a new hybrid identity-based phishing detection technique that leverages webpage visual and textual identity. Extending earlier anti-phishing work based on the website logo as visual identity, our method incorporates novel image features that mimic human vision to enhance the logo detection accuracy. The proposed hybrid technique integrates the visual identity with a textual identity, namely, brand-specific keywords derived from the webpage content using textual analysis methods. We empirically demonstrated on multiple benchmark datasets that this joint visual-textual identity detection approach significantly improves phishing detection performance with an overall accuracy of 98.6%. Benchmarking results against an existing technique showed comparable true positive rates and a reduction of up to 3.4% in false positive rates, thus affirming our objective of reducing the misclassification of legitimate webpages without sacrificing the phishing detection performance. The proposed hybrid identitybased technique is proven to be a significant and practical contribution that will enrich the anti-phishing community with improved defence strategies against rapidly evolving phishing schemes

Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a lockedin state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain- Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements

Formulation of a novel HRV classification model as a surrogate fraudulence detection schema

Lie detection has been studied since a few decades ago, usually for the purpose of producing a scheme to assist in the investigation of identifying the culprit from a list of suspects. Heart Rate Variability (HRV) may be used as a method in lie detection due to its versatility and suitability. However, since its analysis is not instantaneous, a new experiment is described in this paper to overcome the problem. Additionally, a preliminary HRV classification model is designed to further enhance the classification model which is able to distinguish the lie from the truth for up to 80%