Facet Model Research Articles

"Exploring the Dynamics: Unraveling the Impact of Facets Model on Social Media Advertising among College Students"

"Exploring the Dynamics: Unraveling the Impact of Facets Model on Social Media Advertising among College Students"

Community Care as a Way of Life: Understanding and Evaluating the Impacts of Mutual Aid on Quality of Life and Ways to Implement this Framework in the US

This paper evaluates the potential for mutual aid to serve as a framework for a new way of life through a biopsychosocial theoretical lens and a literature review. This paper works to help better understand how we, as a society in the United States can reimagine what a world would look like if we used mutual aid as a framework for a way of life through a public health perspective. The study draws on examples from Blue Zones and community health interventions using mutual aid in Japan, Cameroon, Latin American populations, and other communities of the world to illustrate how mutual aid can be implemented on both micro and macro levels from local community cultures to broad nation-wide policies in the US. The findings suggest that mutual aid can be utilized as a model for other facets of life and society as a whole. By encouraging people to work together and support each other, mutual aid can create more resilient and cohesive communities. These findings have important implications for public health and can help inform future research and policy interventions aimed at promoting community-building through mutual aid.

Examining Motivational Attitudes Toward Statistics and Their Relationship to Performance in Life Science Students

A strong foundation of statistics is essential for undergraduates pursuing life science careers, but this foundation is frequently undermined by negative student attitudes toward statistics. This study explored life science students’ motivational attitudes toward statistics, and their relationship to performance, using expectancy-value theory as a guiding framework. According to expectancy-value theory, task-values—interest in a task (intrinsic value), importance of doing well on a task (attainment value), perceptions of the usefulness of a task (utility value), and negative aspects of engaging in a task (cost)—impact achievement outcomes. However, task-values may be better represented by differentiating each into more specific underlying dimensions, or facets. Using 360 undergraduate life science students enrolled in biostatistics courses across two institutions, this study assessed the fit of a task-value facets model for statistics and examined which task-value facets relate to performance on a statistics assessment. Results show that life science students distinguish among facets of attainment value (importance of achievement, personal importance), utility value (school, daily life, social, career/job), and cost (effort, emotional, opportunity) for statistics. Students’ perceptions of the emotional cost of statistics and the importance of achievement in statistics related to their statistics performance. Implications for teaching and research are discussed. Supplementary materials for this article are available online.

Study on the Erosion Resistance of Different TiN Crystal Planes by Molecular Dynamics.

In this study, we innovatively combined the Fe-Ti-N potential function file to construct simulation models of different crystal facets of TiN/Fe ((001), (110), and (111)), which had not been previously explored. Employing molecular dynamics (MD) simulations, the research investigates the microscale differences in erosion resistance and surface properties of various TiN crystal planes under continuous impacts at varying velocities and angles. The results indicate that both surface wear and internal defects of the model increase with the impact velocity. Both TiN(110) and TiN(111) exhibit damage on their surfaces and interiors, with a larger wear range. In contrast, TiN(001), due to its superior elastic recovery capability, maintains a better surface condition, showing significantly less wear compared to TiN(110) and TiN(111). This disparity in performance among different crystal planes is attributed to variations in molecular gaps between planes, bonding points within the lattice, types of forces, and modes of action. Further research revealed that the wear volume increased with the rise in impact angle, reaching its peak at 90°. Regardless of the impact angle, TiN(001) consistently outperformed TiN(110) and TiN(111). The aim of the research is to compare the surface and internal defects of different crystal facets at the microscopic level, thereby selecting superior crystal facets and providing theoretical reference for the application of TiN materials in practical fracturing environments.

Efficient part orientation algorithm for additive manufacturing in industrial applications

Over the past few decades, the scientific community’s and industry’s interest in additive manufacturing technologies has surged. This technology is distinguished by the layer-by-layer deposition of the raw materials and the piece’s growth in a predetermined build orientation. This factor impacts the process’ overall cost, surface quality, and other crucial parameters. Numerous methods to solve competing aspects have been proposed in the literature, with the more promising that iteratively uses ray-tracing techniques. Existing algorithms iterate for each discrete element of the model’s bounding box projection onto the building platform. However, when optimisation algorithms are used on real-life industrial parts, computational time problems arise due to the high number of faces in the models. A new computational technique to determine the appropriate part orientation to reduce the support volume is proposed to address the problem. The method reduces the computational time, cycling the ray-tracing only on the triangles where the model surface is discretised. This approach has been integrated into an enhanced particle swarm optimisation algorithm to prove its efficiency. The approach is intended for industrial applications where it is necessary to handle complicated geometries quickly and efficiently to find the best orientation. Based on the computer’s resources and the complexity of the faceted model, a set of case studies with an industrial engineering significance is used to demonstrate the approach’s effectiveness.

Developing a technology for modeling radar portraits of complex-shape objects for intelligent recognition systems

The object of this study is the modeling of radar portraits (RPs) for intelligent recognition systems based on the use of faceted 3D models. In order to solve the problems of target identification in homing systems of high-precision missile weapons, a technology is needed that could make it possible to efficiently and quickly generate RPs of military objects of complex shape in the required quantity. The research results are based on a combination of separate component technologies, in particular: the devised technology of using faceted 3D models – their construction and further processing with invisible surfaces excluded from it for an arbitrary viewing angle. The basic part of the work is the development of an algorithm and technological procedures for the formation of a spatial tracing grid for the current observation angle. A feature of the proposed technology is the application of a facet selection algorithm using an array of tracing facets and the application of the Huygens-Fresnel principle to recognize objects of complex shape. The RP database of military objects of complex shape was built. The results of modeling faceted RP’s, in particular the armored boat "Gyurza-M", are given. The results of the experimental study showed the ability to recognize the type of military object of complex shape at the level of 80‒90 %, which makes the use of this technology appropriate for recognizing military objects of complex shape. The achieved high-speed and quality characteristics of RP generation of military objects of a complex shape makes it possible to assume that the main prospective field of practical application is the identification and visual interpretation of targets in homing systems of high-precision missile weapons

Infrared Small Target Detection Based on Density Peak Search and Local Features

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. Second, a density-peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

Optimizing Work Safety: Campaign using the Facet Model of approach Effect and the assessment sheet potential hazard IDEACM

Optimizing Work Safety: Campaign using the Facet Model of approach Effect and the assessment sheet potential hazard IDEACM

Defining model complexity: An ecological perspective

AbstractModels have become a key component of scientific hypothesis testing and climate and sustainability planning, as enabled by increased data availability and computing power. As a result, understanding how the perceived ‘complexity’ of a model corresponds to its accuracy and predictive power has become a prevalent research topic. However, a wide variety of definitions of model complexity have been proposed and used, leading to an imprecise understanding of what model complexity is and its consequences across research studies, study systems, and disciplines. Here, we propose a more explicit definition of model complexity, incorporating four facets—model class, model inputs, model parameters, and computational complexity—which are modulated by the complexity of the real‐world process being modelled. We illustrate these facets with several examples drawn from ecological literature. Overall, we argue that precise terminology and metrics of model complexity (e.g., number of parameters, number of inputs) may be necessary to characterize the emergent outcomes of complexity, including model comparison, model performance, model transferability and decision support.

A Diagnostic Facet Status Model (DFSM) for Extracting Instructionally Useful Information from Diagnostic Assessment.

Modern assessment demands, resulting from educational reform efforts, call for strengthening diagnostic testing capabilities to identify not only the understanding of expected learning goals but also related intermediate understandings that are steppingstones on pathways to learning goals. An accurate and nuanced way of interpreting assessment results will allow subsequent instructional actions to be targeted. An appropriate psychometric model is indispensable in this regard. In this study, we developed a new psychometric model, namely, the diagnostic facet status model (DFSM), which belongs to the general class of cognitive diagnostic models (CDM), but with two notable features: (1) it simultaneously models students' target understanding (i.e., goal facet) and intermediate understanding (i.e., intermediate facet); and (2) it models every response option, rather than merely right or wrong responses, so that each incorrect response uniquely contributes to discovering students' facet status. Given that some combination of goal and intermediate facets may be impossible due to facet hierarchical relationships, a regularized expectation-maximization algorithm (REM) was developed for model estimation. A log-penalty was imposed on the mixing proportions to encourage sparsity. As a result, those impermissible latent classes had estimated mixing proportions equal to 0. A heuristic algorithm was proposed to infer a facet map from the estimated permissible classes. A simulation study was conducted to evaluate the performance of REM to recover facet model parameters and to identify permissible latent classes. A real data analysis was provided to show the feasibility of the model.

Comparison of the SRP spherical model between LEO and GEO satellites

Solar Radiation Pressure (SRP) is a phenomenon caused by the pressure exerted by solar photons on a satellite’s surface when it is exposed to sunlight. It is a form of radiation force and can significantly impact the motion and behaviour of satellites in space. SRP influences a satellite’s orbit by causing changes in its semimajor axis, eccentricity, inclination, argument of perigee, right ascension of the ascending node, and mean anomaly. SRP models are used to simulate the effects of solar radiation pressure on satellites. These models are essential for accurately predicting satellite trajectories and orbital behaviour. There are several types of SRP models, such as spherical model, flat model, box-wing model, faceted model, and analytical SRP models. This research focuses on Telkom 1 and LAPAN A1 satellites, both belonging to Indonesia and positioned in Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) orbits, respectively. The study aims to find a comparison of the effects of SRP spherical model on LEO and GEO satellites. Our modelling shows that the semimajor axis and eccentricity are sensitive to SRP, while the inclination and right ascension of the ascending node are not significantly affected. Comparing the effects of SRP on LEO and GEO satellites, we concluded that both LEO and GEO orbit experience the most significant fluctuations in January (perihelion), likely due to the influence of solar radiation pressure.

Nexus between Organizational Justice and Innovative Work Behavior: The mediating role of Knowledge Sharing

This research paper has assessed the nexus between organizational justice and innovative work behavior. Also mediating role of knowledge sharing was assessed. Three facets model of OJ has been extensively researched in the last couple of decades. However, spatial and temporal justice has been incorporated in the current paper. Thus, SJ, TJ, Interactional, Procedural and Distributive justice were conceptualized as facets of OJ and their impact on IWB was assessed. Academicians were inquired whether or not perceptions relating to fairness affect their IWB’s level at workplace. Furthermore, mediating effect of KS between OJ and IWB was also assessed. A strong positive association was found among the research variables. Predictor significantly affect criterion variable and composite as well as component level assessment of OJ revealed KS as partial mediator between IV and DV.

ISLAMIC INTEGRATED MARKETING COMMUNICATION AND THE EMPOWERMENT OF ENTREPRENEURS

Products offered by entrepreneurs require some form of promotion so consumers will become aware of them. This means that integrated marketing communication (IMC) is an important field that warrants further exploration. The seemingly elusive concept of Islamic IMC defines the statement of the problem and encourages its study from the theoretical and practical perspectives. Hence, the purpose of this study was to examine the IMC campaigns of students at a public university in the academic year 2022/2023. The scope of the study was focused on 13 campaigns that promoted products sold by 13 entrepreneurs. The research questions posed were: What made the Islamic IMC campaigns effective? Which models or theories were used to guide the Islamic IMC campaigns? Which Islamic values were used in the Islamic IMC campaigns? Grounded theory was used as the method to construct theory from data by using a comparative analysis. Themes and codes were derived from the data analysis. Most campaigns reported sales as being the main measurement of campaign success, whereas the campaign objectives guided by the AIDA model formed the important measures of success. Other components, such as awareness, engagement, advertisement recall, and food taste, were minor measurements of success. Twelve campaigns adopted the AIDA model, while one campaign adopted the Facets Model of Effects. Several combinations of Islamic values were used in the campaigns, with “honesty” being the most highlighted Islamic value by students in their campaign reports. The theory of the holistic components of Islamic IMC campaigns was derived from the data projecting the importance of the three components—self, brands, and messages—against the backdrop of sales as being the main measurement of campaign effectiveness and the AIDA model as the main model used to guide the campaign strategies. It is obvious from the study that IMC campaigns can be successful when conducted within Islamic parameters and that entrepreneurs are empowered when their IMC campaigns are effective.

Physically Based Thermal Infrared Snow/Ice Surface Emissivity for Fast Radiative Transfer Models

Accurate thermal infrared (TIR) fast-forward models are critical for weather forecasting via numerical weather prediction (NWP) satellite radiance assimilation and operational environmental data record (EDR) retrieval algorithms. The thermodynamic and compositional data about the surface and lower troposphere are derived from semi-transparent TIR window bands (i.e., surface-sensitive channels) that can span into the far-infrared (FIR) region under dry polar conditions. To model the satellite observed radiance within these bands, an accurate a priori emissivity is necessary for the surface in question, usually provided in the form of a physical or empirical model. To address the needs of hyperspectral TIR satellite radiance assimilation, this paper discusses the research, development, and preliminary validation of a physically based snow/ice emissivity model designed for practical implementation within operational fast-forward models such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Community Radiative Transfer Model (CRTM). To accommodate the range of snow grain sizes, a hybrid modeling approach is adopted, combining a layer scattering model based on the Mie theory (viz., the Wiscombe–Warren 1980 snow albedo model, its complete derivation provided in the Appendices) with a specular facet model. The Mie-scattering model is valid for the smallest snow grain sizes typical of fresh snow and frost, whereas the specular facet model is better suited for the larger sizes and welded snow surfaces typical of aged snow. Comparisons of the model against the previously published spectral emissivity measurements show reasonable agreement across zenith observing angles and snow grain sizes, and preliminary observing system experiments (OSEs) have revealed notable improvements in snow/ice surface window channel calculations versus hyperspectral TIR satellite observations within the NOAA NWP radiance assimilation system.

Human ratings take time: A hierarchical facets model for the joint analysis of ratings and rating times.

Performance assessments increasingly utilize onscreen or internet-based technology to collect human ratings. One of the benefits of onscreen ratings is the automatic recording of rating times along with the ratings. Considering rating times as an additional data source can provide a more detailed picture of the rating process and improve the psychometric quality of the assessment outcomes. However, currently available models for analyzing performance assessments do not incorporate rating times. The present research aims to fill this gap and advance a joint modeling approach, the "hierarchical facets model for ratings and rating times" (HFM-RT). The model includes two examinee parameters (ability and time intensity) and three rater parameters (severity, centrality, and speed). The HFM-RT successfully recovered examinee and rater parameters in a simulation study and yielded superior reliability indices. A real-data analysis of English essay ratings collected in a high-stakes assessment context revealed that raters exhibited considerably different speed measures, spent more time on high-quality than low-quality essays, and tended to rate essays faster with increasing severity. However, due to the significant heterogeneity of examinees' writing proficiency, the improvement in the assessment's reliability using the HFM-RT was not salient in the real-data example. This discussion focuses on the advantages of accounting for rating times as a source of information in rating quality studies and highlights perspectives from the HFM-RT for future research on rater cognition.

Facets of school leadership: contributions of a phenomenological research approach

ABSTRACT The impact of principalship on school effectiveness has become one of the central topics of school leadership research. Meanwhile, while it is assumed with some certainty that a relationship exists between leadership and student achievement, there is a lack of research on how school leadership and student learning are related. Particularly in German-speaking countries, only a few studies have been conducted in this area. This paper responds to this deficit and discusses the results of a recent research project conducted at 28 schools across Germany that won the German School Award. Using a phenomenological approach, the project investigates how school leadership action is reflected in successful schools. Observations and interviews with principals, teachers, and students were conducted during three-day site visits to each school. Grounded theory sensitized our data coding, which relied on inductively formulated categories that reflected ‘facets’ of leadership experiences. The different facets are presented in this paper from a phenomenological research perspective as a kind of ‘response register’. We propose that these can form the basis of a facet model for successful school leadership, and use empirical data to illustrate the applicability of the facets.

Mechanism analysis of surface structure-regulated Cu2O in photocatalytic antibacterial process

The effects of exposing crystal planes and vacancy defect engineering can induce unique surface atom arrangements that strongly influence the physicochemical properties of semiconductor materials. This paper used Cu2O with different surface structures as a research model. A liquid-phase method was chosen for surface structure regulation to prepare Cu2O semiconductors (Vo-(111)Cu2O, Vo-(100)Cu2O, Vo-(110)Cu2O) with different exposed crystalline surfaces analyze the antibacterial mechanisms of other faceted models in the photodynamic antibacterial process. The bactericidal effect of Vo-(111)Cu2O (40 μg/mL, 100%) was better than that of Vo-(100)Cu2O and Vo-(110)Cu2O. DFT simulations show that the photocatalytic antimicrobial performance of Vo-(111)Cu2O is improved due to surface defect structures caused by unsaturated coordination bonds and suspension bonds on its exposed crystalline surfaces. The suspension bonds act as active centres for trapping electrons, leading to a lower carrier complexation rate on the material surface. The antibacterial mechanism of Vo-(111)Cu2O showed that oxidative sterilization by reactive oxygen species (ROS) was the dominant factor (61.98%) in the antibacterial process. The most potent depolarizing effect on E. coli, the highest copper ion solubilization, and the highest ROS yield. Therefore, ROS oxidative sterilization, copper ion leaching sterilization, and contact damage synergistically affect E. coli from the inside out.

FacetClumps: A Facet-based Molecular Clump Detection Algorithm

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. The experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, and a high consistency between the region of detected clump and that of simulated clump, and the experiments demonstrate that FacetClumps is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises 13CO (J = 1−0) emission line of the MWISP within 11.°7 ≤ l ≤ 13.°4, 0.°22 ≤ b ≤ 1.°05, and 5 km s−1 ≤ v ≤ 35 km s−1 and simulated clumps, reaches 90.2%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.

Algorithm 1031: MQSI—Monotone Quintic Spline Interpolation

MQSI is a Fortran 2003 subroutine for constructing monotone quintic spline interpolants to univariate monotone data. Using sharp theoretical monotonicity constraints, first and second derivative estimates at data provided by a quadratic facet model are refined to produce a univariate C 2 monotone interpolant. Algorithm and implementation details, complexity and sensitivity analyses, usage information, a brief performance study, and comparisons with other spline approaches are included.

Infrared Small Target Detection Based on Singularity Analysis and Constrained Random Walker

Effective infrared small target detection is still challenging due to small target sizes and the clutter in the background. Unfortunately, many advanced methods do not perform well in preserving and detecting multi-scale objects in complex scenes. We propose an infrared small targets method to suppress the background and adapt the infrared small targets with different sizes. Based on the singular value analysis in the facet model, we propose a multi-derivative descriptor to enhance the targets and suppress various clutter in the dual derivative channels. In the first-order derivative channel, we design four facet kernels with different directions to enhance and preserve the isotropic small targets and suppress the block clutter. In the second-order derivative channel, we use the facet kernel to enhance the center pixels of targets and suppress the band clutter. In order to adapt to the targets with various sizes, we propose a constrained random walker technique, including an adaptive matching algorithm to extract the local regions of each candidate adaptively based on the constraint of size and shape. The experimental results demonstrate that the proposed method can accurately detect multi-scale small targets in complex scenes, resulting in better detection performance than the state-of-the-art methods.