Dimensional Model Research Articles

Associations of Early-Life Deprivation and Threat with Exploratory Behavior: Moderated Mediation Models of Sensation Seeking and Executive Function.

Exploratory behavior, as a crucial aspect of decision-making, plays an indispensable role in maximizing long-term benefits and is, therefore, essential in promoting adolescents' psychological well-being and social adaptation. Recent studies have shown that this adaptive behavior is influenced by previous early experiences. However, little was known about the associations between specific types of childhood maltreatment and exploratory behavior and the roles of individual motivational and cognitive factors in these relationships. The present study aimed to examine whether the subtypes of maltreatment, that is, threat and deprivation, would influence adolescents' exploratory behavior, the mediating role of sensation seeking, and the moderating role of executive function. Using a sample of 720 Chinese adolescents (Mage = 13.29, SDage = 0.82, 54.8% female), we found that sensation seeking fully mediated the relationship between threat and exploratory behavior. That is, adolescents who experienced threat were more likely to increase sensation seeking, which further promote exploratory activities. Moreover, executive function was a second-stage moderator of this full mediation pathway, with the mediating effect of sensation seeking between threat and exploratory behavior increasing with the enhancement of executive function. However, we did not observe the mediating effect of sensation seeking and the second-stage moderating effect of executive function on the relationship between deprivation and exploration. Considering the distinct impact mechanisms of threat and deprivation on exploratory behavior, our study provides empirical support for the Dimensional Model of Adversity and Psychopathology, and highlights the critical role of sensation seeking and the necessity of implementing executive function interventions for those experiencing threat experiences.

Effect of Cavity Geometry and Location on Base Pressure in a Suddenly Expanded Flow at Mach 2.0 for Area Ratio 3.24

The primary goal of this study is to use CFD analysis to investigate the impact that a cavity has on the pressure at the base of a structure. In this analysis, we took into account the NPR, the cavity aspect ratio, and the cavity position. In this case, the area ratio is 3.24 and the Mach number is 2.0. Simulations were run with L/D ratios between 1 and 6, and NPRs of 3, 5, 7.8, 9, and 12. The 2-dimensional model was developed using ANSYS Fluent's Design Modeler. The nozzle is operating at Mach 2.0. Base pressure and wall pressure in the duct were the primary research foci. The C-D nozzle was created for this research. ANSYS Fluent was used to verify the CFD findings. When the nozzles are under-expanded and the cavity is at 0.5D, passive control as a cavity is shown to be effective. It appears that 1D is the bare minimum for duct length. Because the shear layer gets reattached to the duct wall at 1D and the boundary layer grows after reattachment, passive control is not observed in the flow process regardless of whether the cavity is located at 1D, 1.5D, 2D, or 3D. An oscillating base pressure is seen at shorter duct lengths. This phenomenon does not occur at longer duct lengths. Whether or not there are cavities in the duct, the flow field is the same.

Two-year retest reliability and predictive validity of the Self- and Informant-Personality Inventory for ICD-11 in older adults.

The International Classification of Diseases, 11th edition (ICD-11) adopted a fully dimensional model of personality disorder. The Personality Inventory for ICD-11 (PiCD) and Informant-Personality Inventory for ICD-11 (IPiC) were developed to assess the ICD-11 trait model, and the PiCD has since received significant validation support. However, there has only been one prior study of longitudinal predictive validity of the PiCD, two relatively short test-retest reliability studies of the PiCD, and no prior longitudinal tests of the IPiC. Longitudinal psychometric support for psychological assessment measures is essential. The present study provides a longer, larger, 2-year psychometric validation test of the PiCD and IPiC. Participants (N = 711) and their informants (N = 569) were recruited in the St. Louis Personality and Aging Network. The results demonstrated strong 2-year retest reliability for the PiCD and IPiC, as well as mean-level stability. Additionally, we explored the relationships between the PiCD and IPiC and important life outcome measures (depressive symptoms, satisfaction with life, and health status). The analysis revealed several significant associations between PiCD and IPiC scales and the outcome variables across time. Further, the PiCD Negative Affectivity and IPiC Detachment scales demonstrated incremental validity over each other and the outcome variables at Wave 1 in the prediction of depressive symptoms and satisfaction with life, respectively. The findings provide essential longitudinal test-retest reliability and predictive validity support for the PiCD and IPiC. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

High dimensional model representation median filter for removing salt and pepper noise

High dimensional model representation median filter for removing salt and pepper noise

A novel Bayesian computational approach for bridge-randomized quantile regression in high dimensional models

A bridge-randomized penalization that employs a prior for the shrinkage parameter, as opposed to the conventional bridge penalization with a fixed penalty, often delivers more superior performance compared to many other traditional shrinkage methods. In this paper, we develop an efficient Bayesian computational algorithm via the two-block Markov Chain Monte Carlo method for the bridge-randomized penalization in quantile regression to perform inference in the high-dimensional ‘large-p’ and ‘large-p-small-n’ settings. To construct a fully Bayesian formulation, we utilize the asymmetric Laplace distribution as an auxiliary error distribution and the generalized Gaussian distribution prior for the regression coefficients. Simulation studies encompassing a wide range of scenarios indicate that the proposed method performs at least as well as, and often better than, other existing procedures in terms of both parameter estimation and variable selection. Finally, a real-data application is provided for illustrative purposes.

Research on the residual stress of stainless steel pipe after overlay repair

The weld overlays are widely used to repair metal butt welds or piping components by depositing weld metal on the outside surface of the welds and piping components. This paper presents the detailed residual stress distribution of girth weld in stainless steel pipe after butt welding and weld overlay repair by using finite element analysis. Two dimensional (2D) cross-section models, axisymmetric assumptions for piping girth welds are used to simulate the welding process in order to predict the residual stress distribution in the weld and base material. A project has been implemented to validate the analytical models used to perform welding residual stress analysis against measured residual stresses data in literature. It is found that the simulation results of butt welding are consistent with the experimental results in literature, which means that the analysis model and its results are accurate and reliable. A series of simulations are conducted to investigate the changing rules of influencing factors of overlay repair for stainless steel pipe. The results show that appropriate interpass temperature and heat input, increasing the thickness of overlay can effectively increase the range of compressive stress in pipe, so as to prevent and limit the generation and propagation of cracks. This research may provide an important support for the formulation of weld overlay repair standards in service.

Competing Models of Personality Disorder: Relations With Psychosocial Functioning.

The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5; American Psychiatric Association, 2013), includes 10 categorical personality disorders (PD) in Section II (Section II PD) and a dimensional alternative model of PD (AMPD) in Section III. We compared the two models in explaining concurrent psychosocial functioning levels in psychiatric outpatients and community residents screened as at risk for PD pathology (N = 600). The AMPD's fully dimensional form showed stronger associations with psychosocial difficulties and explained more of their variance compared with the categorical Section II PD. AMPD Criterion A (personality functioning impairment) and Criterion B (pathological traits) incrementally predicted psychosocial functioning about equally with some unique predictions. Finally, AMPD's six categorical PD diagnoses did not show stronger associations with psychosocial functioning than the corresponding Section II PD diagnoses. Findings directly comparing the two models remain important and timely for informing future conceptualizations of PD in the diagnostic system.

A brief screener for impairment in personality functioning: Psychometric validation of the Five-Item Screening Scale for Personality Disorders in a Dutch-speaking clinical sample.

Personality disorders (PDs) are characterized by problems with identity and self-direction. Since the recent dimensional PD models of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) and the International Statistical Classification of Diseases and Related Health Problems, 11th edition, the role of identity and self-direction in personality pathology has been made explicitly by including these problems in a general personality pathology criterion. This criterion reflects impairment in personality functioning (IPF), which is assessed on a continuum to determine the severity of personality pathology. The present study aimed to psychometrically evaluate the Five-Item Screening Scale for Personality Disorders (FISSPD; Skodol et al., 2011), a short screener for IPF, in a Dutch-speaking clinical sample of 820 adults. This screener mainly taps into self-related impairment, with four items measuring self-related impairment and one item measuring interpersonal impairment. The factor structure, scale reliability, measurement invariance (across sex, age, and patients with vs. without PD), and convergent validity were investigated. The Dutch FISSPD showed a unidimensional structure and good scale reliability. Scalar measurement invariance was established across sex, age (patients below vs. above age 40), and patients with versus without PD. The Dutch FISSPD was significantly related to identity and all DSM-IV/5 Section-II PD symptoms. The present study indicates that the Dutch FISSPD has potential as a reliable and valid screener for personality pathology in adult patients. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Measure-valued affine and polynomial diffusions

We introduce a class of measure-valued processes, which – in analogy to their finite dimensional counterparts – will be called measure-valued polynomial diffusions. We show the so-called moment formula, i.e. a representation of the conditional marginal moments via a system of finite dimensional linear PDEs. Furthermore, we characterize the corresponding infinitesimal generators obtaining a representation analogous to polynomial diffusions on R+m, in cases where their domain is large enough. In general the infinite dimensional setting allows for richer specifications strictly beyond this representation. As a special case, we recover measure-valued affine diffusions, sometimes also called Dawson–Watanabe superprocesses. From a mathematical finance point of view, the polynomial framework is especially attractive since it allows to transfer many famous finite dimensional models and their tractability properties to an infinite dimensional measure-valued setting.

The dynamical bulk boundary correspondence and dynamical quantum phase transitions in the Benalcazar–Bernevig–Hughes model

In this article we demonstrate that dynamical quantum phase transitions (DQPTs) occur for an exemplary higher order topological insulator, the Benalcazar–Bernevig–Hughes model, following quenches across a topological phase boundary. A dynamical bulk boundary correspondence is also seen both in the eigenvalues of the Loschmidt overlap matrix and the boundary return rate. The latter is found from a finite size scaling analysis for which the relative simplicity of the model is crucial. Contrary to the usual two dimensional case the DQPTs in this model show up as cusps in the return rate, as for a one dimensional model, rather than as cusps in its derivative as would be typical for a two dimensional model. We explain the origin of this behaviour.

Smooth Transonic Flows with Nonzero Vorticity to a Quasi Two Dimensional Steady Euler Flow Model

Smooth Transonic Flows with Nonzero Vorticity to a Quasi Two Dimensional Steady Euler Flow Model

Hybrid Natural Language Processing Model for Sentiment Analysis during Natural Crisis

This paper introduces a novel natural language processing (NLP) model as an original approach to sentiment analysis, with a focus on understanding emotional responses during major disasters or conflicts. The model was created specifically for Croatian and is based on unigrams, but it can be used with any language that supports the n-gram model and expanded to multiple word sequences. The presented model generates a sentiment score aligned with discrete and dimensional emotion models, reliability metrics, and individual word scores using affective datasets Extended ANEW and NRC WordEmotion Association Lexicon. The sentiment analysis model incorporates different methodologies, including lexicon-based, machine learning, and hybrid approaches. The process of preprocessing includes translation, lemmatization, and data refinement, utilized automated translation services as well as the CLARIN Knowledge Centre for South Slavic languages (CLASSLA) library, with a particular emphasis on diacritical mark correction and tokenization. The presented model was experimentally evaluated on three simultaneous major natural crises that recently affected Croatia. The study’s findings reveal a significant shift in emotional dimensions during the COVID-19 pandemic, particularly a decrease in valence, arousal, and dominance, which corresponded with the two-month recovery period. Furthermore, the 2020 Croatian earthquakes elicited a wide range of negative discrete emotions, including anger, fear, and sadness, with the recuperation period much longer than in the case of COVID-19. This study represents an advancement in sentiment analysis, particularly in linguistically specific contexts, and provides insights into the emotional landscape shaped by major societal events.

Mechanism Analysis and Optimization Design of Exoskeleton Robot with Non-Circular Gear–Pentabar Mechanism

To address the complex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi−rod mechanisms, an exoskeleton knee robot with a differential non−circular gear–pentarod mechanism is designed based on non−circular gears with arbitrary transmission ratios to constrain the degrees of freedom of the R-para-rod mechanism. In this study, the kinematic model of a non-circular gear–five−rod mechanism is established based on motion mapping theory by obtaining the normal motion positions of the human lower limb. An optimization design software for the non-circular gear–five−rod mechanism is developed using the MATLAB 2018b visualization platform, with the non−circular active gear as the sole input variable. A set of ideal parameters is obtained through parameter adjustment and optimal parameter selection, and the corresponding trajectories are compared with human trajectories. The three−dimensional model of the mechanism is established according to the obtained parameters, and the motion simulation of the non−circular gear–five−bar mechanism demonstrates that the mechanism can better reproduce the expected human knee joint motion posture, meeting the working requirements of an exoskeleton knee robot.

Viscoelastic nanofluids flow through Darcy–Forchheimer porous media with Newtonian conditions

This research is conducted by considering the Darcy–Forchheimer viscoelastic non-Newtonian fluids (NNF) generated by the movement of surface. Two types NNF namely Walter’s liquid B (WLB) and second grade (SG) fluids are chosen as the working fluids. Cattaneo–Christov energy diffusion model is carried out for the energy transmission analysis. Newtonian heating (NH) and Newtonian concentration (NC) conditions are imposed for the solutal and thermal analysis. The model is formulated by the application of boundary layer technique. These equations are converted into non dimensional model by the implication of similarity variables. The numeric solutions are executed by the utilization of Runge–Kutta–Fehlberg-45 order scheme. The results are presented by numeric benchmarks and graphical illustrations. A comparative discussion of WLB and SG fluids is reported. A comparative visualization shows that the higher velocity profile is achieved for SG fluid case while temperature and concentration profiles are larger in the case of SG fluid. The Brownian movement and thermophoretic constraints have similar influences on temperature. The Brownian movement constraint has reverse trends on concentration and temperature.

Defining the geomechanical operating limits for subsurface CO2 storage

Carbon capture and storage (CCS) is a critical component of proposed pathways to limit global warming, though considerable upscaling is required to meet emissions reduction targets. Quantifying and managing the risks of fault reactivation is a leading barrier to scaling global CCS projects from current levels of ~40 million tonnes of carbon dioxide(CO2) per year (to target levels of several gigatonnes of CO2 per year), because CO2 injection into reservoirs can result in increased pore-fluid pressure and temperature changes, which can reduce the strength of rocks and faults and induce brittle failure. This can result in induced seismicity, whilst hydraulic fracturing of seals could provide pathways for CO2 leakage. Consequently, identifying favourable geomechanical conditions (typically determined through data on pre-injection rock stress, mechanical and elastic properties, and pore-fluid pressures) to minimise deformation of reservoirs and seals represents a key challenge in the selection of safe and effective sites for CCS projects. Critically, however, such geomechanical data are typically spatially limited (i.e. restricted to wells) and mainly consist of pre-injection crustal stress orientation measurements, rather than a full 3D description of the stress tensor and related geomechanical properties. This paper reviews some key geomechanical issues and knowledge gaps (particularly those associated with data availability and limitations) that need to be understood to enable successful reservoir and seal management for CCS projects. We also highlight recent advances in multi-scale and dimensional geomechanical modelling approaches that can be used to assess sites for the secure storage of CO2 as well as other gases, including hydrogen.

Extracellular Vesicles in Breast Cancer: From Intercellular Communication to Therapeutic Opportunities.

Breast cancer, a multifaceted and heterogeneous disease, poses significant challenges in terms of understanding its intricate resistance mechanisms and devising effective therapeutic strategies. This review provides a comprehensive overview of the intricate landscape of extracellular vesicles (EVs) in the context of breast cancer, highlighting their diverse subtypes, biogenesis, and roles in intercellular communication within the tumour microenvironment (TME). The discussion spans various aspects, from EVs and stromal cells in breast cancer to their influence on angiogenesis, immune response, and chemoresistance. The impact of EV production in different culture systems, including two dimensional (2D), three dimensional (3D), and organoid models, is explored. Furthermore, this review delves into the therapeutic potential of EVs in breast cancer, presenting emerging strategies such as engineered EVs for gene delivery, nanoplatforms for targeted chemotherapy, and disrupting tumour derived EVs as a treatment approach. Understanding these complex interactions of EV within the breast cancer milieu is crucial for identifying resistance mechanisms and developing new therapeutic targets.

Detecting Users' Emotional States during Passive Social Media Use

The widespread use of social media significantly impacts users' emotions. Negative emotions, in particular, are frequently produced, which can drastically affect mental health. Recognizing these emotional states is essential for implementing effective warning systems for social networks. However, detecting emotions during passive social media use---the predominant mode of engagement---is challenging. We introduce the first predictive model that estimates user emotions during passive social media consumption alone. We conducted a study with 29 participants who interacted with a controlled social media feed. Our apparatus captured participants' behavior and their physiological signals while they browsed the feed and filled out self-reports from two validated emotion models. Using this data for supervised training, our emotion classifier robustly detected up to 8 emotional states and achieved 83% peak accuracy to classify affect. Our analysis shows that behavioral features were sufficient to robustly recognize participants' emotions. It further highlights that within 8 seconds following a change in media content, objective features reveal a participant's new emotional state. We show that grounding labels in a componential emotion model outperforms dimensional models in higher-resolutional state detection. Our findings also demonstrate that using emotional properties of images, predicted by a deep learning model, further improves emotion recognition.

Investigation of hybrid ultrasonic machining process of Nomex honeycomb composite using a toothed disc cutter

Nomex honeycomb composite (NHC) has been increasingly used in the automotive, aerospace and defence applications due to its excellent thermal and mechanical properties. Its complex cellular hexagonal thin-walled configuration along with heterogeneous, soft and brittle nature pose substantial processing defects such as burr formation, tearing of walls, surface roughness, dimensional inaccuracy and low machining quality during conventional machining (CM). These surface defects have a substantial influence on the operating life and functional performance of its sandwiched structural members. Hybrid ultrasonic vibration assisted machining (HUSVAM) technology has been introduced to overcome such limitations. For an in depth research on HUSVAM of NHC using ultrasonic toothed disc cutter (UTDC), three dimensional finite element model was developed and experimental validation was carried out. The numerical simulation and experimental results were found to be in good agreement with one another. The influence of various machining parameters including the ultrasonic tool vibration amplitude (UTVA), feed rate (FR), depth of cut (DoC) and spindle angular speed (SAS) on NHC cutting forces, chip formation and surface quality was investigated using both HUSVAM and CM techniques. Furthermore, stresses, deformation and forces near tool-workpiece interaction along with the surface topography and morphologies were also analysed and compared for various operating conditions. A reduction in the cutting forces was found with the increase of SAS and UTVA (up to 65.47 % and 65.74 %, respectively). Although, the cutting forces were observed to increase by increasing the DoC and FR (up to 159.45 % and 126.33 %, respectively). DoC has a greater impact on the cutting forces among all machining parameters according to 4 levels, 4 factors (L16) orthogonal experiments based on Taguchi method. The results show that the chip formation and machining quality of NHC core can be improved with HUSVAM technique using UTDC. HUSVAM also reduced the cutting forces (up to 73 %) compared to CM. The ultrasonic toothed disc cutter generated large number of burr with very short length, no tearing defects and no uncut fibers as observed from scanning electron microscopy of NHC hexagonal cell structure, walls and triple points. A burr formation of less than 10 % was realized during HUSVAM of NHC cores for Fx≤3N, while it was found up to 20 % if Fx>3N, compared to at least 40 % during CM. The finite element model developed can be used to investigate the influence of HUSVAM on modern difficult to machine materials for enhanced surface quality.

Lump Kink interactional and breather-type waves solutions of (3+1)-dimensional shallow water wave dynamical model and its stability with applications

The shallow water equations are used to describe the behavior of water waves in various shallow regions such as coastal areas, lakes, rivers, etc. These equations are derived by making simplifying assumptions about the water depth relative to the wavelength of the waves. In this paper, the generalized exponential rational function method (gERFM) is used to construct novel wave solutions of the (3+1)-dimensional shallow water wave ((3+1)-dSWW) dynamical model. These solutions encompass distinct kinds of waves, such as solitary waves, solitons, Kink and anti-kink solitons, lump Kink interactional waves, traveling breathers-type waves and multi-peak solitons. The dynamical behavior of these wave solutions is discussed, examining the influence of free parameters on the resulting wave shapes. Furthermore, to provide a scientific elucidation of the obtained results, the solutions are presented graphically, making it easy to distinguish the dynamical features, which have practical implications in different areas of applied sciences and engineering. The stability of this dynamical model is revealed via modulational instability analysis, signifying that all analytical results are stable. The obtained results show that the given technique is universal and efficient. Through comparing the projected technique with the existing techniques, the obtained results demonstrate that the given technique is universal, pithy and efficient.

A space marching method for sonic boom near field predictions

The conventional sonic boom propagation prediction method widely adopted in supersonic aircraft design involves a two-step procedure. In the first step, a compressible viscous or inviscid Computational Fluid Dynamics analysis is applied to the aircraft geometry at flight conditions to produce a flow field solution near the aircraft. Then in the second step, a one dimensional nonlinear Burgers’ equation model is used to propagate sonic boom signature traces from the near field solution at flight altitude to the ground along a ray path. For an accurate ground signature prediction the near field signature must be accurately modeled at a sufficient distance from the aircraft flight path in order to minimize errors in the one dimensional propagation model. This is a very challenging task for general purpose CFD tools in a design environment because the cost of maintaining highly accurate off-body solutions increases dramatically as the radial distance is enlarged in the computational domain. It is also particularly difficult to apply these tools for wave propagation because the algorithms are normally lower order and numerically dissipative and dispersive. In this work a space marching procedure based on an optimized higher order finite difference method is developed and applied in conjunction with a CFD solution concentrated in the close vicinity of the aircraft. This new approach is much more efficient, compared to previous methods, in providing highly accurate near field signatures for full carpet ground predictions. Results indicate that near field signatures retain more waveform shape information farther from the aircraft geometry while reducing the CFD cost significantly. The predicted ground signature is also shown to converge in shape as the radial distance of the near field signature grows, which is indicative of a more ideal initial condition being supplied to the one dimensional wave propagation to ground. This feature is very difficult to replicate in a tractable manner with common CFD approaches used in design.