Search Results (8)

We explored the correlation between consumer characteristics and international coffee chain brand membership apps. From the perspective of a gamified system, we analyzed differences in members’ experiences when using the app. Three elements of the gamification system of the MDE theory were used to analyze members’ gamification sentiments: “Achievement”, “Challenges”, and “Immediate Feedback”. Variables representing members’ characteristics included gender, age, education level, occupation, monthly income, and recent consumption frequency. In using branded apps, males experienced a stronger sense of “Achievement” compared to females. However, there was no significant difference among members with varying monthly incomes regarding these three gamification elements. Members who made one to four purchases displayed higher levels of “Challenge” and “Immediate Feedback” than those who made no purchases. Such results underscore the importance of member characteristics in diverse gamification experiences. It is recommended that coffee chain brands customize their apps to align with the distinct preferences of their member demographics. Additionally, for members who make frequent purchases, brands must design various challenge levels and provide advanced feedback to enhance their engagement in the gamified system. Full article

Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness, damping, friction, coupling effects, and defects. Consequently, their vibration signals display nonlinear characteristics. Entropy techniques prove to be effective in detecting these nonlinear dynamic characteristics. Recently, an approach called fuzzy dispersion entropy (DE–FDE) was introduced to quantify the uncertainty of time series. FDE, rooted in dispersion patterns and fuzzy set theory, addresses the sensitivity of DE to its parameters. However, FDE does not adequately account for the presence of multiple time scales inherent in signals. To address this limitation, the concept of multiscale fuzzy dispersion entropy (MFDE) was developed to capture the dynamical variability of time series across various scales of complexity. Compared to multiscale DE (MDE), MFDE exhibits reduced sensitivity to noise and higher stability. In order to enhance the stability of MFDE, we propose a refined composite MFDE (RCMFDE). In comparison with MFDE, MDE, and RCMDE, RCMFDE’s performance is assessed using synthetic signals and three real bearing datasets. The results consistently demonstrate the superiority of RCMFDE in detecting various patterns within synthetic and real bearing fault data. Importantly, classifiers built upon RCMFDE achieve notably high accuracy values for bearing fault diagnosis applications, outperforming classifiers based on refined composite multiscale dispersion and sample entropy methods. Full article

Understanding patterns of species richness along an altitudinal gradient is one of the central focuses of ecological research and can offer us insights into the mechanisms of species diversity. However, few studies have focused on a large scale. Based on the distribution data of 3538 species collected from the local flora of the entire Qinling Mountains, the patterns of seed plant richness along the altitude were analyzed; three hypotheses proposed to explain the pattern were tested, and five methods verifying the applicability of Rapoport’s rule were examined. The results of the research were as follows: species richness of seed plants along an altitudinal gradient in the Qinling Mountains displayed a single peak, occurring at 1000–1500 m (R² = 0.67, p < 0.001), and similar patterns were also observed at the genus and family level; The proportion and density of endemic species were increased monotonously with altitude; the altitudinal patterns of species richness in the Qinling Mountains were not fully explained by a single hypothesis such as species area relationship (SAR), mid-domain effect (MDE) and ecological metabolism theory (MTE), while the interpretation amount of SAR and MDE was 80.4%, indicating that area and mid-domain effect significantly affect the patterns of plant species richness in the Qinling Mountains; only the Pagel upper-bound method supported Rapoport’s rule. However, all species were grouped according to their altitude distribution; most of results of the species-by-species method, Rohde midpoint method, and Stevens method conformed to Rapoport’s rule. The test of Rapoport’s rule is obviously affected by the test method, so more advanced and universal methods are expected to be developed in future. Full article

The application of single chain mean-field theory (SCMFT) on semiflexible chain brushes is reviewed. The worm-like chain (WLC) model is the best mode of semiflexible chain that can continuously recover to the rigid rod model and Gaussian chain (GC) model in rigid and flexible limits, respectively. Compared with the commonly used GC model, SCMFT is more applicable to the WLC model because the algorithmic complexity of the WLC model is much higher than that of the GC model in self-consistent field theory (SCFT). On the contrary, the algorithmic complexity of both models in SCMFT are comparable. In SCMFT, the ensemble average of quantities is obtained by sampling the conformations of a single chain or multi-chains in the external auxiliary field instead of solving the modified diffuse equation (MDE) in SCFT. The precision of this calculation is controlled by the number of bonds $N_m$ used to discretize the chain contour length L and the number of conformations M used in the ensemble average. The latter factor can be well controlled by metropolis Monte Carlo simulation. This approach can be easily generalized to solve problems with complex boundary conditions or in high-dimensional systems, which were once nightmares when solving MDEs in SCFT. Moreover, the calculations in SCMFT mainly relate to the assemble averages of chain conformations, for which a portion of conformations can be performed parallel on different computing cores using a message-passing interface (MPI). Full article

We fabricate hybrid magnetoactive materials (hMAMs) based on cotton fibers, silicone oil, carbonyl iron and graphene nanoplatelets (nGr) at various mass concentrations ${\mathsf{\Phi }}_{\mathrm{nGr}}$. The obtained materials are used as dielectric materials for manufacturing plane electrical capacitors. The equivalent electrical capacitance $C_p$ and resistance $R_p$ are measured in an electric field of medium frequency f, without and respectively with a magnetic field of magnetic flux density B in the range from 0.1 T up to 0.5 T. The results are used to extract the components ${ϵ}_r^{\prime }$ and ${ϵ}_r^{″}$ of the complex relative permittivity ${ϵ}_r^{*}$, and to reveal the magnitude of the induced magnetoelectric couplings $k_x$ and magnetodielectric effects $MDE$. It is shown that ${ϵ}_r^{\prime }$, ${ϵ}_r^{″}$, $k_x$ and MDE are significantly influenced by $f,B$ and ${\mathsf{\Phi }}_{\mathrm{nGr}}$. We describe the underlying physical mechanisms in the framework of dipolar approximation and using elements of dielectric theory. The tunable magnetoelectric and magnetodielectric properties of hMAMs are useful for manufacturing electrical devices for electromagnetic shielding of living organisms. Full article

The first part of this paper outlined the Statistical Agent-based Model of Development and Evaluation (SAbMDE) and demonstrated the model’s ability to estimate development cycle resource utilization. This second part of the paper explores the model’s ability to compute development cycle information content and process risk. Risk managers focus mostly on outcome risk, i.e., the likelihood that a running system will behave in an undesirable manner. SAbMDE assumes that a subset of outcome risks are not inherent and immutable but are, instead, the result of defects and vulnerabilities introduced during the system’s development process. The likelihood of defect and vulnerability introduction is a process risk. SAbMDE further assumes that measuring process risk is a prerequisite for minimizing defects and vulnerabilities and, therefore, outcome risk. The model implements the measurement with Shannon’s information–probability relationship similar to its use in Axiomatic Design Theory (ADT). This paper details the SAbMDE’s information and risk calculations and demonstrates those calculations with examples. The process risk calculation is consistent with and offers a mechanism for the ADT Information Axiom. Full article

This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values. Full article

Model-Driven Engineering (MDE) is widely applied in the industry to develop new software functions and integrate them into the existing run-time environment of a Cyber-Physical System (CPS). The design of a software component involves designers from various viewpoints such as control theory, software engineering, safety, etc. In practice, while a designer from one discipline focuses on the core aspects of his field (for instance, a control engineer concentrates on designing a stable controller), he neglects or considers less importantly the other engineering aspects (for instance, real-time software engineering or energy efficiency). This may cause some of the functional and non-functional requirements not to be met satisfactorily. In this work, we present a co-design framework based on timing tolerance contract to address such design gaps between control and real-time software engineering. The framework consists of three steps: controller design, verified by jitter margin analysis along with co-simulation, software design verified by a novel schedulability analysis, and the run-time verification by monitoring the execution of the models on target. This framework builds on CPAL (Cyber-Physical Action Language), an MDE design environment based on model-interpretation, which enforces a timing-realistic behavior in simulation through timing and scheduling annotations. The application of our framework is exemplified in the design of an automotive cruise control system. Full article