Close Fit Research Articles

Applications of genetic algorithm in prediction of the best achievable combination of hardness and tensile strength for graphene reinforced magnesium alloy (AZ61) matrix composite

Due to lack of knowledge about role of process variables in the hot rolling of magnesium alloy composites, the most desirable wrought products of magnesium alloy-based composites could not be successfully produced till date. Moreover, use of wrought graphene reinforced magnesium alloy-based composite has emerged as a lucrative option for developing novel light alloy products in structural applications. The implicative need to obtain the best possible properties of the composite dictates to fix the optimized process variables in rolling operation. The present study envisages the development of data driven model that enables to derive desirable properties of the said composite; so, in order to secure the optimized subset of requirements (process parameters), a metaheuristic optimization tool is employed. We invoke the use of Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ-61-graphene nanoplate (GNP) composite.Modified magnesium AZ-61 alloy composites of varying GNP content (0, 1 and 2 wt.%) are produced by stir casting. The cast composites are homogenized at 450 °C for 12 h, followed by iced water quenching. The quenched materials are subjected to hot rolling and the process variables of hot rolling operation consist of rolling temperature, number of rolling passes as well as the initial stock thickness. The rolled materials are tested for hardness and tensile strength as the response variables. Instead of using artificial neural network to fix the objective function of GA, a mathematical model is developed by regression analysis; the derived equation is used as the objective function for multi-objective optimization task. The present paper explores the usefulness of implementing evolutionary algorithm (as in genetic algorithm) to correctly forecast the effect of rolling process variables onto the hardness and tensile strength of the stir cast AZ alloy-GNP nanocomposite.The experimental results show that the maximum achievable hardness value happens to be 106.6 VHN and is very close to that predicted by GA, ∼107.41VHN. Similarly, the maximum experimental tensile strength is found to be 266.82 MPa whereas, the corresponding GA predicted value is seen to be 253 MPa. A close fit can thus be noticed between the GA predicted and the experimentally obtained values. Barring a couple of exceptions, the difference between the experimental and GA predicted data lies within 1 % to 5 % (based on [Experimental value ∼ predicted value]/ [experimental value]). This entices to believe that the adopted muti-objective optimization strategy is quite useful in determining the optimum combination of rolling parameters that aids in achieving the most desirable property combination.

Eco-friendly tool-based electrochemical polishing of additively manufactured metallic components

The elevated surface roughness of metal additive manufacturing (MAM) parts detrimentally affects wear resistance, diminishes fatigue strength, and hampers cooling efficiency, which mandates post-processing. Electrochemical polishing is a non-contact and non-thermal post-processing technique but uses non-ecofriendly acidic baths and is ineffective in removing unmolten or partially molten metal particles. To address these challenges, we propose a novel approach using a flat nano-polished cylindrical tool as the cathode and an eco-friendly electrolyte for finishing a MAM component fabricated via atomic diffusion additive manufacturing (ADAM). Our study evaluates the effectiveness of this approach through numerical simulation and optimises the process through experimental analysis. The numerical simulation developed in COMSOL incorporates the surface roughness data of the ADAM part as the anode surface profile in the 2D simulation domain. The current density distribution, viscous layer formation, reduction in surface roughness and mass material removal rate (MRRg) are analysed from the simulation results. The experimental optimisation of parameters, including polishing time, inter-electrode gap (IEG), electrolyte flow, and electrolyte composition, resulted in a substantial decrease in the average surface roughness (Ra) value of the ADAM component by 95.91 %. The polished surface is more levelled, exhibiting a glossy finish with no waviness and fewer surface cracks. The EDS and XRD analysis portrayed the presence of passive films, indicating improved corrosion resistance. Repeating the experiment for rolled and milled surfaces with the same process parameters resulted in a similar reduction in the Ra value by 97.59 % and 94.73 %, respectively. The comparative analysis, of ECP on MAM, rolled, and milled surfaces, indicates the potential to achieve a similar notable improvement in surface finish, irrespective of the process history of the manufactured part for the same ECP parameters. Comparing the anode surface profiles, Ra values, and mass of material dissolved showed a close fit between experimental and simulation results. Our study highlights the feasibility of ECP with the flat tool electrode and eco-friendly electrolytes to reduce the surface roughness of an MAM component significantly.

Primary moult of adult Laughing Doves Spilopelia senegalensis: introduction of the relative duration index and a comparison of moult indices

A new moult index, the relative duration index (RDI), was developed to quantify progress through moult using the Underhill–Zucchini model of primary avian moult, with the Laughing Dove Spilopelia senegalensis as a case study. The RDI weights individual primaries in accordance with their estimated relative moult durations in the same way that proportion feather mass grown (PFMG) weights primaries in accordance with their relative masses. A sample of 1 893 moult records of adult Laughing Doves was used to study their primary moult. Moult parameters were estimated for three moult indices: the traditional moult score (TMS), PFMG and the RDI, and for each of the 10 primaries. Comparing the moult parameter estimates of each of the three indices to their respective cumulative growth curves obtained from the progression of moult of individual primaries, PFMG had the closest fit. However, any of the three moult indices would give satisfactory results for the Laughing Dove. Laughing Doves had an estimated primary moult duration of 215 days, with mean start and end dates of 3 November and 6 June, respectively. The standard deviation parameter was 66 days, which implies a lack of synchronisation of moult in adult doves. It was hypothesised that the RDI might be an appropriate moult index for species that moult one feather at a time and for which the individual moult durations are not proportional to their mass, as seen in Laughing Doves. However, this was not the case as PFMG provided the best fit. The RDI might also prove to be an appropriate moult index for species that moult multiple feathers simultaneously during part of the moult period and these feathers grow more slowly than when fewer feathers are moulting simultaneously.

A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data.

We didactically derive a correlated traits correlated (methods - 1) [CTC(M - 1)] multitrait-multimethod (MTMM) model for dyadic round-robin data augmented by self-reports. The model is an extension of the CTC(M - 1) model for cross-classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round-robin designs. It can be specified as a traditional structural equation model. We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter estimation inaccuracy. Further, neglecting the reciprocity covariance-structure of the augmented round-robin data does not severely bias the remaining parameter estimates. All analyses (including data, R scripts, and results) and the simulation study are provided in the Supporting Information. Implications and limitations are discussed.

Study of Influential Factors That Affect LGBTQ’s Travel Destination Choice Decision

LGBTQ tourism is a very promising market with annual worldwide economic impact at more than 140 billion USD. The global LGBT tourism market is expected to reach US$ 568.5 Billion by 2030. The LGBT tourism market is important both economically and socially. LGBT travelers are often high-spending and frequent travelers, and their tourism dollars can have a significant impact on local economies. With the introduction of the 'pink dollar,' organizations hope to tap into members of the LGBTQ+ community who are willing to spend big. The 'pink dollar' refers to money spent by members of the LGBTQ community in the United States. It has boosted the US economy by billions of dollars. Additionally, by creating welcoming and inclusive travel experiences for LGBT individuals, the tourism industry can help promote greater acceptance and understanding of LGBT people around the world. LGBT travelers have unique needs and preferences when it comes to travel, and the tourism industry has responded with a range of products and services designed to meet these needs. These may include LGBT-friendly accommodation, events, and tours, as well as marketing and outreach efforts that specifically target the LGBT community.The purpose of this research is to study factors influencing LGBTQ’s Travel Destination Choice Decision. These factors include nine independent variables: Recommendations (RE), Popularity (PO), Destination Information (DI), Special Offers (SO), Nearness (NN), Amenities/Facilities (AF), Safety & Security (SS), Destination Features (DF), Strategic Fit (SF) and one dependent variable: Travel Behavior (TB). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .04 (<.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .904 (>.90), the model seems to fit well according to the descriptive measures of fit. More importantly, Recommendations (RE) and Safety & Security (SS) are significantly defined as significant influential factors that affect LGBTQ’s travel destination choice decision due to their p-values are equal and less than .05. That means LGBTQ choose their travel destination if it’s strongly recommended by friends & family, online & social media, and customer positive review & sharing tips. Moreover LGBTQ prefer the destinations that offer personal safety and safe accommodation and destinations that they won’t be taken advantage of financially i.e. safe and sound destinations without being scammed.

Attributions to discrimination in multiracial contexts: Isolating the effect of target group membership.

Historically, psychological models of how people make judgments of discrimination have relied on a binary conceptualization of intergroup relations, making it unclear how people make judgments of discrimination in diverse, multigroup contexts. We propose that groups can vary in the extent to which they fit the prototype for targets of discrimination and that this variation influences judgments of discrimination in ambiguous circumstances. The present research examined attributions to discrimination when job applicants are rejected for a white-collar position. People consistently made more attributions to discrimination (ATDs) when managers rejected Black American as compared to Asian American job applicants, and when managers rejected Asian American as compared to White American job applicants. People also made more ATDs for rejected Black American as compared to Latino American applicants, but ATDs were similar for Latino and Asian American applicants. Overall, similar patterns were observed in majority White American samples and a Black/African American sample; only an Asian American sample did not make more ATDs for rejected Black than Asian American applicants. Six experiments (N = 2,321) found strong support for the relative fit hypothesis and suggest that, in a white-collar employment context, White Americans are a distant fit to the prototype for targets of discrimination, Asian and Latino Americans are an intermediate fit, and Black Americans are a close fit. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The pigeon as a machine: Complex category structures can be acquired by a simple associative model

Never known for its smarts, the pigeon has proven to be a prodigious classifier of complex visual stimuli. What explains its surprising success? Does it possess elaborate executive functions akin to those deployed by humans? Or does it effectively deploy an unheralded, but powerful associative learning mechanism? In a series of experiments, we first confirm that pigeons can learn a variety of category structures- some devised to foil the use of advanced cognitive processes. We then contrive a simple associative learning model to see how effectively the model learns the same tasks given to pigeons. The close fit of the associative model to pigeons' categorization behavior provides unprecedented support for associative learning as a viable mechanism for mastering complex category structures and for the pigeon's using this mechanism to adapt to a rich visual world. This model will help guide future neuroscientific research into the biological substrates of visual cognition.

Induced synthesis of SO2-promoting and H2O-tolerant sorbents for elemental mercury removal from flue gas

In this study, novel S-doped sorbents synthesized via one-step co-pyrolysis of biomass and scrap tires under SO2 atmosphere were developed for elemental mercury (Hg0) removal from coal-fired flue gas. The presence of SO2 could regulate the sulfur species in S-doped sorbents and improve their Hg0 removal performance. Sample characterization exhibited that SO2 activation increased the sulfur content and decreased the carbon content in sorbents due to the occurrence of carbothermal reduction reaction. The specific surface area, pore volume, and micropore content in S-doped sorbents were obviously improved compared with raw sorbents. After SO2 activation, partial inorganic sulfide was converted into sulfur-containing functional groups and more oxygen-containing functional groups were generated on the sorbents. The S-doped sorbents presented more excellent performance for Hg0 removal compared with raw sorbents. The optimal pyrolysis temperature and SO2 concentration in activation atmosphere were 700 °C and 10%, respectively. It was surprising that SO2 facilitated Hg0 removal and the S-doped sorbents showed good water-resistance due to the presence of ZnS. O2 and NO had positive effects on Hg0 removal. A close fitting to the pseudo-second-order model was obtained for the behavior of Hg0 removal over S-doped sorbents. Aliphatic sulfur, sulfoxide, carbonyl and carboxyl/ester groups functioned as active components for Hg0 removal.

Synthesis, characterization, and density functional theory calculation studies of a novel Rb-based lead halide perovskite material

Inorganic lead halide perovskites have appeared as favorable and novel materials for their effective use in photovoltaics. The synthesis route of such materials via the simple wet chemistry technique renders these inorganic halide perovskites the ideal property for light-harvesting materials. Despite these novel properties, the inherently unstable nature under increased heat and ambient moisture conditions is still a conjecture that needs to be addressed. This work shows the wet chemistry method as a synthesis route of the novel RbPbCl3 perovskite using four different solvents for photovoltaic applications. Interestingly, the synthesized perovskite was stable in only one solvent with a band gap of 2.6 eV, whereas the material degraded in the other three. The DFT calculations performed post-geometric optimization revealed well-defined electronic bandgap and optical properties, nearly imitating the experimental data of our synthesized perovskite. The copious properties such as electronic, optical, and formation energy revealed that the perovskite possesses huge charge screening ability, a low recombination rate of electron-hole pairs, board absorption spectrum, and high stability. Henceforth, establishes its suitability for photovoltaic devices. The close fit of the experimental results with our theoretical trend demonstrates the importance of developing a computational strategy to screen for new perovskite materials for photovoltaic cells.

Chain entanglement-driven tough, fatigue-resistant PEG-based injectable hydrogel adhesive for joint skin wound healing

The development of effective wound dressings that can withstand the frequent movement and stretching of joint skin wounds remains a significant challenge. Traditional wound dressings are prone to damage or shedding, which increases the risk of bacterial infection and delays wound healing. To address this issue, a new type of injectable, stretchable, low hysteresis, and fatigue-resistant hydrogel (LZM/PEG) adhesive has been designed for wound healing in movable parts. The hydrogel is formed through the flexible chain entanglement of high molecular weight polyethylene glycol functionalized by Succinimide Carbonate (SC-PEG-SC) and Amidation crosslinking between LZM-NH2 and SC-PEG-SC. The long-chain of SC-PEG-SC enables the hydrogel to penetrate the surface of the target tissue, generating physical entanglements and covalent bonds with functional groups in the matrix, enabling close fitting of joint skin and wound healing without the need of secondary fixation. In a neck trauma model of full-thickness skin defect in vivo, the hydrogel dressing significantly promoted wound healing with milder inflammation, higher granulation tissue thickness and collagen disposition, demonstrating its potential for joint skin wound treatment in clinics. Overall, the LZM/PEG hydrogel is capable of adapting to the frequent movement of joint, promoting healing of joint skin wounds, which makes it a promising approach for joint skin wound treatment.

New Pricing Models, Same Old Phillips Curves?

Abstract We show that in a broad class of menu cost models, the first-order dynamics of aggregate inflation in response to arbitrary shocks to aggregate costs are nearly the same as in Calvo models with suitably chosen Calvo adjustment frequencies. We first prove that the canonical menu cost model is first-order equivalent to a mixture of two time-dependent models, which reflect the extensive and intensive margins of price adjustment. We then show numerically that in any plausible parameterization, this mixture is well approximated by a single Calvo model. This close numerical fit carries over to other standard specifications of menu cost models. Thus, for shocks that are not too large, the Phillips curve for a menu cost model looks like the New Keynesian Phillips curve, but with a higher slope.

The Nurse-Patient Interaction Scale: Translation and Psychometric Properties Among Hospitalized Cancer Patients in Turkey.

Background and Purpose: Cancer patients experience physical and psychological ailments resulting from cancer illness, a difficult and long treatment process and fear of death. Nurse-patient interaction seems to play an important role when responding to the psychological needs of patients with cancer. The aim of this study is to adapt and examine the psychometric properties of the Nurse-Patient Interaction Scale (NPIS) among Turkish cancer patients. Design and Method: In a cross-sectional design, patients receiving cancer treatment at Farabi hospital in Turkey responded to the Personal Information Form and the NPIS. Descriptive statistics, principal component analysis (PCA), and confirmatory factor analysis (CFA) were applied. Findings: The NPIS was found to be suitable for a single factor structure, including 11 out of the original 14 items. The 11-items uni-dimensional model showed an acceptable/good fit with the data: χ2 = 154.369, (df = 44), χ2/df = 3.51, p = .0001, RMSEA = 0.100, p-value for test of close fit = 0.0001, CFI = 0.98, TLI = 0.97 and SRMR = 0.012.

Promoting young autistic students’ social functioning and engagement in the classroom: Positive response strategies and close student-teacher relationships

BackgroundClassrooms can create barriers to young autistic students’ social and behavioral success at school. Better quality student-teacher relationships (STRs) have been associated with improved student social and behavioral outcomes. When teachers use more positive response strategies (e.g., praise), they create more opportunities for positive interactions with students, likely developing closer STRs. MethodThe present study uses SEM to investigate a path by which teachers’ use of positive response strategies is associated with closer STRs, which lead to improved social functioning and classroom engagement for N = 145 young autistic students (age 4–7 years). Factors contributing to teachers’ reported frequency of using positive response strategies were also explored using multiple linear regression. ResultsThe model was a close fit to the data (χ2 (18, N = 145) = 18.4, p = .43, TLI = 1.0, CFI = 1.0, RMSEA = .01). Teachers who reported using positive response strategies more frequently had significantly closer STRs, which were associated with lasting improvements in students’ social functioning and engagement in the classroom. Positive response strategies that teachers reported using most frequently were praise, positive comments, and incentives. Teachers’ perceived usefulness of positive response strategies and regular trainings in autism were significantly associated with frequency of positive response strategy use. ConclusionsPositive, supportive classrooms in which teachers utilized more positive response strategies and developed closer STRs were associated with young autistic students’ academic engagement and social functioning. Regular autism trainings and perceived usefulness of positive response strategies emerged as significant factors for teachers’ use of strategies.

Photonic Characterization and Modeling of Highly Efficient Color Conversion Layers With External Reflectors

We propose a new method for fabricating arrays of colloidal quantum dots for color conversion layers (CCLs) with atomic layer deposited (ALD) encapsulation. Semiconductor processes were used to transfer the CCL onto a transparent glass substrate. This method enables pixels as small as 5 μm, and the pixel's size and pitch can be controlled through the semiconductor process and mask layout. A dual-color array with a red-and-green CCL was also demonstrated to be suitable for microdisplay applications in experiments. The best color conversion efficiency of a single color CCL reached as high as 20.7%. A set of distributed Bragg reflector mirrors was used to increase photon recycling, and these mirrors yielded an increase in peak intensity of 35.7%. We formulated a model of incoherent reflection and transmission to calculate the changes in CCL properties when mirrors are added; this model had close fit with results from experiments in which mirrors of different reflection coefficients were used. Through the use of ALD, the CCL can be stored at a normal room-temperature environment for more than 9000 hours, and the projected lifetime is linearly extrapolated to be 44,041 hours.

Mechanistic understanding of KCNQ1 activating polyunsaturated fatty acid analogs.

The KCNQ1 channel is important for the repolarization phase of the cardiac action potential. Loss of function mutations in KCNQ1 can cause long QT syndrome (LQTS), which can lead to cardiac arrythmia and even sudden cardiac death. We have previously shown that polyunsaturated fatty acids (PUFAs) and PUFA analogs can activate the cardiac KCNQ1 channel, making them potential therapeutics for the treatment of LQTS. PUFAs bind to KCNQ1 at two different binding sites: one at the voltage sensor (Site I) and one at the pore (Site II). PUFA interaction at Site I shifts the voltage dependence of the channel to the left, while interaction at Site II increases maximal conductance. The PUFA analogs, linoleic-glycine and linoleic-tyrosine, are more effective than linoleic acid at Site I, but less effective at Site II. Using both simulations and experiments, we find that the larger head groups of linoleic-glycine and linoleic-tyrosine interact with more residues than the smaller linoleic acid at Site I. We propose that this will stabilize the negatively charged PUFA head group in a position to better interact electrostatically with the positively charges in the voltage sensor. In contrast, the larger head groups of linoleic-glycine and linoleic-tyrosine compared with linoleic acid prevent a close fit of these PUFA analogs in Site II, which is more confined. In addition, we identify several KCNQ1 residues as critical PUFA-analog binding residues, thereby providing molecular models of specific interactions between PUFA analogs and KCNQ1. These interactions will aid in future drug development based on PUFA-KCNQ1 channel interactions.

MST resistive wall tearing mode simulations

The Madison Symmetric Torus (MST) is a toroidal device that, when operated as a standard tokamak, does not have major disruptions. Unlike most tokamaks, the MST plasma is surrounded by a close fitting highly conducting wall, with a resistive wall penetration time two orders of magnitude longer than in JET or DIII-D, and three times longer than in ITER. The MST can operate with edge , unlike standard tokamaks. Simulations presented here indicate that the MST is unstable to resistive wall tearing modes (RWTMs) and resistive wall modes (RWMs). They could in principle cause disruptions, but the predicted thermal quench (TQ) time is much longer than the experimental pulse time. If the MST TQ time were comparable to measurements in JET and DIII-D, theory and simulations predict that disruptions would have been observed in MST. This is consistent with the modeling herein, predicting that disruptions are caused by RWTMs and RWMs. In the low regime of MST, the RWTM asymptotically satisfies the RWM dispersion relation. The transition from RWTM to RWM occurs smoothly at , where are poloidal and toroidal mode numbers.

Couple-based interpersonal psychotherapy for first-time parents: A process evaluation

PurposeFirst-time parents have an increased risk of depression during the transition to parenthood, which has long-term adverse effect on the infant development. Interpersonal psychotherapy (IPT) has been shown to effectively reduce postnatal depression. The objectives of this study were to explore first-time parents' perceptions of a couple-based IPT program and to identify positive and negative influences on the effectiveness of the intervention through a process evaluation. Design and methodsA process evaluation was conducted as part of a randomized controlled trial of a couple-based IPT program. A program satisfaction questionnaire was used to assess the participants' satisfaction with the structure, process and outcome of the program. Semi-structured telephone interviews were conducted with a purposive sample of 44 first-time parents who had received the couple-based IPT. The interview data were analyzed by thematic analysis. ResultsThe qualitative findings showed that the parents perceived couple-based IPT as useful for enhancing their interpersonal relationship, emotional control and competence in child care. The successful implementation of the couple-based IPT program was influenced by its delivery by midwives, the interactive lessons used to engage the participants, the close fit of the teaching contents to the needs of first-time parents and the flexible program schedule and delivery mode. ConclusionsThe process evaluation indicates that couple-based IPT is an acceptable and feasible intervention for first-time parents to facilitate a healthy transition to parenthood. Practice implicationsThe couple-based IPT can be used as an adjunct to standard care to promote perinatal health.

Parameter Identification of Bivariate Fractal Interpolation Surfaces by Using Convex Hulls

The scope of this article is to identify the parameters of bivariate fractal interpolation surfaces by using convex hulls as bounding volumes of appropriately chosen data points so that the resulting fractal (graph of) function provides a closer fit, with respect to some metric, to the original data points. In this way, when the parameters are appropriately chosen, one can approximate the shape of every rough surface. To achieve this, we first find the convex hull of each subset of data points in every subdomain of the original lattice, calculate the volume of each convex polyhedron and find the pairwise intersections between two convex polyhedra, i.e., the convex hull of the subdomain and the transformed one within this subdomain. Then, based on the proposed methodology for parameter identification, we minimise the symmetric difference between bounding volumes of an appropriately selected set of points. A methodology for constructing continuous fractal interpolation surfaces by using iterated function systems is also presented.

A Novel Approach for Optimization of Soft Material Constitutive Model Parameters Based on a Genetic Algorithm and Drucker's Stability Criterion.

The growing interest in soft materials to develop flexible devices involves the need to create accurate methodologies to determine parameter values of constitutive models to improve their modeling. In this work, a novel approach for the optimization of constitutive model parameters is presented, which consists of using a genetic algorithm (GA) to obtain a set of solutions from data of uniaxial tensile tests, which are later used to simulate the mechanical test using finite element analysis (FEA) software to find an optimal solution considering Drucker's stability criterion. This approach was applied to the elastomer Ecoflex 00-30 considering the Warner and Yeoh models and Rivlin's phenomenological theory. The correlation between the experimental and the predicted data by the models was determined using the root mean squared error (RMSE), where the found parameter sets provided a close fit to the experimental data with RMSE values of 0.022 (ANSYS) and 0.024 (ABAQUS) for Warner's model, while for Yeoh's model were 0.014 (ANSYS) and 0.012 (ABAQUS). It was found that the best parameter values accurately follow the experimental material behavior using FEA. The proposed GA not only optimizes the material parameters but also has a high reproducibility level with average RMSE values of 0.024 for Warner's model and 0.009 for Yeoh's model, fulfilling Drucker's stability criterion.

Study of Factors Influencing Consumer to Adopt Cryptocurrency

Cryptocurrencies have become one of the most traded financial assets in the last decade. The first half of 2022 will not be easily forgotten by the crypto faithful as it shifted investor sentiment after a monster cryptocurrency rally in 2021 that saw bitcoin (BTC) and altcoins scale multiple record peaks. Just as users were getting comfortable with their passive DeFi incomes, Terra and its non-collaterised stablecoin collapsed in a sensational turn of events. Three Arrows Capital (3AC), a crypto hedge fund that managed assets worth about $10bn at its peak, went bankrupt as falling crypto prices forced liquidations of collateralized loans and leveraged trading positions across the industry, aggravating the sell-off. Investor sentiment plummeted as crypto lending platforms like Celsius and Babel suspended withdrawals for clients. Investor sentiment plummeted as crypto lending platforms like Celsius and Babel suspended withdrawals for clients. The future of cryptocurrency still remains unclear. Given all these mess, we would like to know what factors could actually influence consumer adoption of cryptocurrency from here on out.The purpose of this research is to study factors influencing consumer to adopt cryptocurrency. These factors include seven independent variables: Convenience (CV), Popularity (PL), Usefulness (UF), Credibility (CD), Recommendations (RC), Price Stability (PS) and Risk (RK) and one dependent variable: Crypto Behavior (BH). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that Since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .047 (<.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .934 (>.90), the model seems to fit well according to the descriptive measures of fit. More importantly Popularity (PL), Usefulness (UF), Credibility (CD), Recommendations (RC) and Risk (RK) seem to have significant effects on influencing consumer to adopt cryptocurrency due to their p-values are both less than .05. That means as long as cryptocurrency is becoming more popular, useful, credible and highly recommended by key stakeholders (friends & family, professionals and influencers) with better risk management, consumer will be more welcome to adopt cryptocurrency as both day-to-day currency and investment alternative. One interesting finding is that Price Stability (PS) of cryptocurrency that we’ve seen over and over again especially post-covid doesn’t seem to significantly impact much of consumer adoption.