Materialism and happiness as predictors of willingness to buy counterfeit luxury brands

The study examines the effect of material values – namely material success, material happiness, material essentiality and material distinctiveness – and life satisfaction and lawfulness with regards to consumers’ attitude toward counterfeit luxury brands and their affect on consumers’ willingness to buy counterfeit luxury brands. To test the research model, a new scale to measure and conceptualize materialism was developed and two new constructs: “material essentiality” and “material distinctiveness” were created

AN AUTOMATED, DEEP LEARNING APPROACH TO SYSTEMATICALLY & SEQUENTIALLY DERIVE THREE-DIMENSIONAL KNEE KINEMATICS DIRECTLY FROM TWO-DIMENSIONAL FLUOROSCOPIC VIDEO

Total knee arthroplasty (TKA), also known as total knee replacement, is a surgical procedure to replace damaged parts of the knee joint with artificial components. It aims to relieve pain and improve knee function. TKA can improve knee kinematics and reduce pain, but it may also cause altered joint mechanics and complications. Proper patient selection, implant design, and surgical technique are important for successful outcomes. Kinematics analysis plays a vital role in TKA by evaluating knee joint movement and mechanics. It helps assess surgery success, guides implant and technique selection, informs implant design improvements, detects problems early, and improves patient outcomes. However, evaluating the kinematics of patients using conventional approaches presents significant challenges. The reliance on 3D CAD models limits applicability, as not all patients have access to such models. Moreover, the manual and time-consuming nature of the process makes it impractical for timely evaluations. Furthermore, the evaluation is confined to laboratory settings, limiting its feasibility in various locations. This study aims to address these limitations by introducing a new methodology for analyzing in vivo 3D kinematics using an automated deep learning approach. The proposed methodology involves several steps, starting with image segmentation of the femur and tibia using a robust deep learning approach. Subsequently, 3D reconstruction of the implants is performed, followed by automated registration. Finally, efficient knee kinematics modeling is conducted. The final kinematics results showed potential for reducing workload and increasing efficiency. The algorithms demonstrated high speed and accuracy, which could enable real-time TKA kinematics analysis in the operating room or clinical settings. Unlike previous studies that relied on sponsorships and limited patient samples, this algorithm allows the analysis of any patient, anywhere, and at any time, accommodating larger subject populations and complete fluoroscopic sequences. Although further improvements can be made, the study showcases the potential of machine learning to expand access to TKA analysis tools and advance biomedical engineering applications

Prediction Models for Corporate Financial Distress in Southeast Asia

Building prediction models of corporate financial distress plays a critical part of evaluating and managing credit risk. There is a combination of financial statements and volatility in market values of firm assets to measure distance to default as well as discriminate between distressed and non-distressed firmshttps://ro.ecu.edu.au/ecuposters/1006/thumbnail.jp

Distance to default

This poster covers default risk and the significance of the Distance to Default (DD) model in predicting financial distress for 6 countries in the ASEAN region.https://ro.ecu.edu.au/ecuposters/1021/thumbnail.jp

EFFECTS OF INTERFACIAL ADHESION ON DEFORMATION AND FRACTURE BEHAVIOUR OF COMPOSITES BASED ON POLYPROPYLENE AND GLASS BEADS

In this study, the isotactic polypropylene, a semicrystalline polymer, was used as a matrix for composites containing 20% and 40% (by weight) of glass bead filler. Selected surface treatment was applied to obtain different adhesion between particles and polymer matrix. In addition to non-treated filler, filler treated with i) a release agent (labelled as NO adhesion) and ii) an adhesion promoter (labelled as GOOD adhesion) were incorporated into the matrix. The morphology, tensile mechanical and fracture behaviour (J-integral) were investigated. Morphology observation revealed a poor interfacial adhesion in the case of non-treated and “NO adhesion” samples represented with debonding of particles. In contrast, strong particle-matrix interactions were confirmed in “GOOD adhesion” samples. The presence of rigid filler particles increased the stiffness, while strain at break was decreased with the lowest value for the composites with strong interfacial adhesion. On the other hand, the higher rigidity and lower deformability decreased in fracture toughness

Two-Bit Bit Flipping Decoding of LDPC Codes

In this paper, we propose a new class of bit flipping algorithms for low-density parity-check (LDPC) codes over the binary symmetric channel (BSC). Compared to the regular (parallel or serial) bit flipping algorithms, the proposed algorithms employ one additional bit at a variable node to represent its "strength." The introduction of this additional bit increases the guaranteed error correction capability by a factor of at least 2. An additional bit can also be employed at a check node to capture information which is beneficial to decoding. A framework for failure analysis of the proposed algorithms is described. These algorithms outperform the Gallager A/B algorithm and the min-sum algorithm at much lower complexity. Concatenation of two-bit bit flipping algorithms show a potential to approach the performance of belief propagation (BP) decoding in the error floor region, also at lower complexity.Comment: 6 pages. Submitted to IEEE International Symposium on Information Theory 201