Functional Model Research Articles

AFFECT: an R package for accelerated functional failure time model with error-contaminated survival times and applications to gene expression data

BackgroundSurvival analysis has been used to characterize the time-to-event data. In medical studies, a typical application is to analyze the survival time of specific cancers by using high-dimensional gene expressions. The main challenges include the involvement of non-informaive gene expressions and possibly nonlinear relationship between survival time and gene expressions. Moreover, due to possibly imprecise data collection or wrong record, measurement error might be ubiquitous in the survival time and its censoring status. Ignoring measurement error effects may incur biased estimator and wrong conclusion.ResultsTo tackle those challenges and derive a reliable estimation with efficiently computational implementation, we develop the R package AFFECT, which is referred to Accelerated Functional Failure time model with Error-Contaminated survival Times.ConclusionsThis package aims to correct for measurement error effects in survival times and implements a boosting algorithm under corrected data to determine informative gene expressions as well as derive the corresponding nonlinear functions.

Regression and alignment for functional data and network topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Should I stay or move? Quantifying landscape of fear to enhance environmental management of road networks in a highly transformed landscape

The development and expansion of road networks pose considerable threats to natural habitats and wildlife, fostering a landscape of fear. In addition to direct mortality caused by road collisions, road construction and maintenance often result in habitat fragmentation and loss, impeding animal movement and gene flow between populations. Mountain ungulates are already confined to fragmented habitat patches and roads can cause substantial disturbances to their critical ecological processes, such as dispersal and migration. In this study, we employed two key mountain ungulates, the wild goat (Capra aegagrus) and mouflon (Ovis gmelini), as functional models to examine how road networks impact the quantity and connectivity of natural habitats in southwestern Iran, where extensive road construction has led to significant landscape changes. We used the MaxEnt method to predict species distribution, the circuit theory to evaluate habitat connectivity, and the Spatial Road Disturbance Index (SPROADI) to assess road impacts. During the modeling process, we selected eleven important variables and employed a model parametrization strategy to identify the optimal configuration for the MaxEnt model. For SPROADI index we used three sub-indices, including traffic intensity, vicinity impact, and fragmentation grade. We then integrated the results of these analyses to identify areas with the most significant environmental impacts of roads on the coherency of the natural habitats. The findings indicate that suitable habitats for wild goats are widely distributed across the study area, while suitable habitats for mouflon are primarily concentrated in the northeastern region. Conservation gap analysis revealed that only 8% of wild goat habitats and 7% of mouflon habitats are covered by protected areas (PAs). The SPROADI map highlighted that 23% of the study area is negatively influenced by road networks. Moreover, 30.4% of highest-probability corridors for mouflon, and 25.7% for wild goat, were highly vulnerable to the impacts of roads. Our combined approach enabled us to quantitatively assess species-specific vulnerability to the impacts of heavy road networks. This study emphasizes the urgent need to address the negative effects of road networks on wildlife habitats and connectivity corridors. Our approach effectively identifies sensitive areas, which can help inform mitigation strategies and support more effective conservation planning in significantly transformed landscapes.

Dependence properties of stochastic volatility models

The concepts of physical dependence and approximability have been extensively used over the past two decades to quantify nonlinear dependence in time series. We show that most stochastic volatility models satisfy both dependence conditions, even if their realizations take values in abstract Hilbert spaces, thus covering univariate, multi‐variate and functional models. Our results can be used to apply to general stochastic volatility models a multitude of inferential procedures established for Bernoulli shifts.

Functional data regression model based on basis function expansion and Group Lasso

Functional data regression model based on basis function expansion and Group Lasso

Mapping the way: functional modelling for community-based integrated care for older people

BackgroundHealthcare system sustainability is challenged by several critical issues; one of the most pressing is the ageing population. Traditional, episodic care delivery models are not designed for older people who are medically complex and frail. These individuals would benefit from health and social care that is more comprehensive, coordinated, person-centred and accessible in the communities in which they live. Delivering this is a challenging endeavour. Community-based health and social care professionals are siloed, dispersed across various locations and sectors, each with their own mental models, electronic health information systems, and means of communication. To move away from fragmented care delivery models and towards a more integrated approach to care, an analysis of the process of community-based comprehensive geriatric assessment was conducted in an urban location in Atlantic Canada. The purpose of the study was to identify where in the community-based comprehensive geriatric assessment process challenges and opportunities existed for moving towards a more integrated model of care delivery.MethodThe functional resonance analysis method (FRAM) and dynamic FRAM (DynaFRAM) modelling were used to model the community-based health and social care system and create a hypothetical patient journey scenario. Data collected to inform modelling consisted of document review, focus groups, and semi-structured interviews with health and social care professionals providing care and service to older people in the community setting.Findings.Challenges and opportunities for implementing integrated care in the local context were identified. Findings from the FRAM and DynaFRAM analysis informed the co-design of multi-level process improvement recommendations that aim to move the local community-based comprehensive geriatric assessment process towards a more integrated model of care.ConclusionsA transformative redesign of community-based health and social care in the local context is necessary but cannot be accomplished without an understanding of how health and social care professionals conduct their work and how older people may receive care under the dynamic conditions. The FRAM and DynaFRAM modelling provided an enhanced understanding of system operations and functionality and demonstrated a critical step that should not be overlooked for decision-makers in their efforts to implement a more integrated model of care.

R&D&I support system for innovatively active enterprises

RDI is an important area of innovative growth of domestic economic entities. Isolation on the internal system of innovation management may not lead to the results planned within the framework of key strategic documents at the state level. Expanding its boundaries and involving external participants in solving problems of innovation will become the basis for creating a powerful integrated structure that will provide an innovation lift and scientific and technological renewal of enterprises. A scientifically based approach to the formation of a balanced system of collaborative innovation management is the key to the sustainable functioning of innovatively active enterprises. The purpose of this study is to formulate scientific and practical recommendations for creating an RDI system to support innovatively active enterprises to ensure the effectiveness of their functioning in the context of global economic challenges. In the process of achieving this goal, the following methods of scientific knowledge were used: structural analysis and synthesis, system analysis, generalization and description, modeling. Based on the results of the study, a structural and functional model of the RDI system for supporting innovatively active enterprises was developed, in contrast to existing ones, which creates a platform for balanced innovative development and increased competitiveness through the coherent organization of integrated innovation chains and effective innovation management in a single synergetic environment. Scientific conclusions and proposals are of high importance for the development of theoretical and methodological principles of the theory of innovation, management of innovation activities and solving problems of innovatively active enterprises of the Russian Federation. The study is based on modern works of leading domestic and foreign economists. The RDI system of support for innovatively active enterprises creates the basis for their sustainable functioning under conditions of sanctions restrictions and economic turmoil. The author has determined the vector of development of domestic enterprises to ensure the effective implementation of the mission and achievement of strategic goals.

CONSTRUCTION METHOD AND VERIFICATION OF DIGITAL TWIN MECHANISM MODEL OF PULMONARY VENTILATION

The establishment of a fully functional and accurate pulmonary ventilation mechanism model is of great significance to simulate the real respiratory states of the specified human and configure respiratory therapeutic equipment (such as ventilators and simulated lungs). This paper proposed an alternative method to establish the digital twin mechanism model of pulmonary ventilation, which is a composition of the geometric and physical model mapping to the real respiratory process of humans. Through simulation, it could more intuitively reflect respiratory states and generate reliable respiratory data. The consistency of the digital twin of pulmonary ventilation was verified by the respiratory experiments. According to the verification results, the correlation coefficient and goodness of fit were 98.4% and 96.8%, respectively, in different respiratory patterns; 98.4% and 96.2%, respectively, in different examinees. It can be concluded that the digital twin of pulmonary ventilation could well mimic various respiratory patterns of different human bodies. Then it can generate reliable data to support the parameter settings of the respiratory therapeutic devices and auxiliary respiratory devices such as simulated lungs and ventilators.

Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the d-Block.

First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, electronic structure information is generally lost. Particularly in the field of heterogeneous catalysis, simulated electron band structures provide fundamental insights into catalytic reactivity. This ab initio knowledge is preserved in semiempirical methods such as density functional tight binding (DFTB), which extend the accessible computational length and time scales beyond first-principles approaches. In this paper we present Shell-Optimized Atomic Confinement (SOAC) DFTB electronic-part-only parametrizations for bulk and surface band structures of all d-block transition metals that enable efficient predictions of electronic descriptors for large structures or high-throughput studies on complex systems outside the computational reach of density functional theory.

External breast prostheses after mastectomy: production and selection of a low-cost functional model to be performed in developing countries.

Breast cancer is one of the most common types of cancer affecting women. Despite advancements in early diagnosis, neoadjuvant therapy, and various treatment modalities, mastectomy remains a common procedure for many women. Although some women opt for reconstructive surgery (BR), many do not have the indication, desire, or opportunity to undergo this procedure. An easily manufactured, washable, lightweight, and inexpensive external breast prosthesis (EBP) model was developed specifically for the study. Participants were presented with five EBP models-one commercially available, three manufactured options, and one created for the study-and were asked to choose a prosthesis. We also evaluated the factors associated with non-adherence to EBP among women who had undergone mastectomy without BR. The chi-square test was used to assess adherence or non-adherence to EBP, while logistic regression was used to identify factors associated with non-adherence. We introduced a low-cost, lightweight, washable EBP model. When participants were asked to choose between two prostheses, the silicone prosthesis was the first choice for 33.9% of the participants, while the prosthesis created for the study emerged as the second choice for 70.5%. Out of the 72 women assessed, 45.8% (33) opted not to use any of the EBP models. Our analysis revealed that age and BMI were significantly associated with non-use of EBP. Multiple barriers contribute to non-adherence to EBP, underscoring the need for interventions aimed at improving patient knowledge and adherence. This study introduces a lightweight, easily reproducible, and low-cost EBP model.

Functional index coefficient models for locally stationary time series

In the analysis of nonlinear time series, we propose a novel functional index coefficient model for the locally stationary data. The proposed model can effectively capture the dynamic interaction effects between variables and the nature of data evolution. Drawing the idea from the spline-backfitted method, we propose a three-step estimation procedure and establish the asymptotic properties of the resulting nonparametric estimators. We further construct simultaneous confidence bands for the time-varying functions to explore the global variation of the original data. We also develop a test statistic to check the time-varying properties based on a bootstrap procedure. Simulation studies have been conducted to investigate the finite sample performance of the proposed methods. Two real applications in the finance market and the Hong Kong respiratory and circulatory disease data are analysed for illustration.

DecorPGNet: Functional Area Division and Layout Algorithm Model in Living Rooms of Chinese Apartment-Style Family Homes

DecorPGNet: Functional Area Division and Layout Algorithm Model in Living Rooms of Chinese Apartment-Style Family Homes

Health-related quality of life for children with anterior cruciate ligament deficiency: Ensuring content validity of the new KIDS-KNEES-ACL questionnaire.

The existing knee-specific pediatric patient-reported outcome measures (PROMs) lack content and construct validity for children with anterior cruciate ligament (ACL) injury. This impairs their accuracy which can lead to false interpretations of data and inaccurate clinical guidelines. The purpose of this study was to develop a content-valid PROM for children with an ACL injury. The process adhered to the COnsensus-based Standards for the selection of health Measurement INstruments guidelines for PROM development. Informants were children with ACL deficiency and sampled based on age, sex, and treatment. Semistructured interviews were conducted exploring themes within the International Classification of Functioning, Disability and Health model. Interviews continued beyond data saturation. By thematic analysis and by probing items from the adult PROM 'KNEES-ACL', new themes and items emerged. Content coverage, relevance and understandability were continuously evaluated. All interviews were recorded and transcribed verbatim. The NVivo 12 software was used for data analysis and coding of items. A PROM of 60 items across nine subscales was formed. From cognitive interviews, 19 new items emerged. Forty-one of 55 items from KNEES-ACL were endorsed as relevant; however, all required rewording to ensure understandability. Substantial differences in the psychosocial impact between adults and children were observed. The children experienced a more considerable negative psychosocial impact caused by a loss of participation in sports, lower self-confidence and loss of social networks. This resulted in four new domains. The physical issues were similar to adults, with few exceptions. The 'KIDS-KNEES-ACL' 1.0 (qualitative version) was developed. This version will be subjected to psychometric analysis, resulting in adequate measurement properties of the final KIDS-KNEES-ACL 2.0. As the only adequate pediatric ACL-specific PROM, its use in clinical trials and databases will enhance PROM data quality, and strengthen clinical guidelines and thus the treatment of children with ACL injury. Not applicable.

Theoretical study on equilibrium, thermodynamic, and density functional models for doped and amination@TiO2 removal of cadmium (II) ion from wastewater

Theoretical study on equilibrium, thermodynamic, and density functional models for doped and amination@TiO2 removal of cadmium (II) ion from wastewater

Functional characteristics and computational model of abundant hyperactive loci in the human genome.

Enhancers and promoters are classically considered to be bound by a small set of TFs in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1,003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected 5 distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.

Neural correlates of distress and comfort in individuals with avoidant, anxious and secure attachment style: an fMRI study

ABSTRACT Despite a growing literature, experiments directly related to attachment are still needed. We explored brain processes involved in two aspects of attachment, distress and comfort. Seventy-eight healthy adult males with different attachment styles (secure, avoidant, and anxious) viewed distress, comfort, complicity-joy and neutral images (picture database BAPS-Adult) in an fMRI block design. ROIs from the modules described in the functional Neuro-Anatomical Model of Attachment (Long et al. 2020) were studied. Secure participants used more co- and self-regulation strategies and exhibited a higher activation of the reward network in distress and comfort viewing, than insecure participants. Avoidant participants showed the lower brain activations. Their approach and reward modules were the least activated in distress and comfort. Anxious participants presented both higher activations of the approach and aversion modules during complicity-joy. In addition, comfort and complicity-joy were processed differently according to attachment styles and should be differentiated among positive stimuli to disentangle attachment processes.

Function theory in the bfd-norm on an elliptical region

Let E be the open region in the complex plane bounded by an ellipse. The B. and F. Delyon norm ‖⋅‖bfd on the space Hol(E) of holomorphic functions on E is defined by‖f‖bfd=defsupT∈Fbfd(E)⁡‖f(T)‖, where Fbfd(E) is the class of operators T such that the closure of the numerical range of T is contained in E. The name of the norm recognizes a celebrated theorem of the brothers Delyon, which implies that ‖⋅‖bfd is equivalent to the supremum norm ‖⋅‖∞ on Hol(E).The purpose of this paper is to develop the theory of holomorphic functions of bfd-norm less than or equal to one on E. To do so we shall employ a remarkable connection between the bfd norm on Hol(E) and the supremum norm ‖⋅‖∞ on the space H∞(G) of bounded holomorphic functions on the symmetrized bidisc, the domain G in C2 defined byG=def{(z+w,zw):|z|<1,|w|<1}. It transpires that there exists a holomorphic embedding τ:E→G having the property that, for any bounded holomorphic function f on E,‖f‖bfd=inf⁡{‖F‖∞:F∈H∞(G),F∘τ=f}, and moreover, the infimum is attained at some F∈H∞(G). This result allows us to derive, for holomorphic functions of bfd-norm at most one on E, analogs of the well-known model and realization formulae for Schur-class functions. We also give a second derivation of these models and realizations, which exploits the Zhukovskii mapping from an annulus onto E.

A decomposition-ensemble-integration framework for carbon price forecasting

Accurate carbon price forecasting is crucial for effective carbon trading policies that mitigate climate change. The volatility and uncertainty of carbon prices pose significant challenges. Traditional frameworks that use a single model for all intrinsic mode functions (IMFs) fail to leverage the diverse strengths of different models. In this paper, we propose DecEnsInt, a novel framework that utilizes an ensemble of models tailored to distinct frequency domains. By using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose historical prices into IMFs and optimizing ensemble parameters through Sequential Least Squares Programming (SLSQP) for each frequency domain, DecEnsInt enhances forecasting performance. Extensive experiments on four Emissions Trading System (ETS) datasets, evaluated using RMSE, MAPE, MAE and R2, demonstrate DecEnsInt’s superiority, achieving relative improvements in key metrics by 10.58% to 17.72% over runner-up ensemble methods. DecEnsInt also achieves better robustness and stability across different forecasting steps, proving its effectiveness in handling the complexities of carbon price forecasting.

Development of a Multi-Material 3D Printer for Functional Anatomic Models.

Anatomic models are important in medical education and pre-operative planning as they help students or doctors prepare for real scenarios in a risk-free way. Several experimental anatomic models were made with additive manufacturing techniques to improve geometric, radiological, or mechanical realism. However, reproducing the mechanical behavior of soft tissues remains a challenge. To solve this problem, multi-material structuring of soft and hard materials was proposed in this study, and a three-dimensional (3D) printer was built to make such structuring possible. The printer relies on extrusion to deposit certain thermoplastic and silicone rubber materials. Various objects were successfully printed for testing the feasibility of geometric features such as thin walls, infill structuring, overhangs, and multi-material interfaces. Finally, a small medical image-based ribcage model was printed as a proof of concept for anatomic model printing. The features enabled by this printer offer a promising outlook on mimicking the mechanical properties of various soft tissues.

The arrival of commercial bioprinters - Towards 3D bioprinting revolution!

The dawn of commercial bioprinting is rapidly advancing the tissue engineering field. In the past few years, new bioprinting approaches as well as novel bioinks formulations have emerged, enabling biological research groups to demonstrate the use of such technology to fabricate functional and relevant tissue models. In recent years, several companies have launched bioprinters pushing for early adoption and democratisation of bioprinting. This article reviews the progress in commercial bioprinting since the inception, with a particular focus on the comparison of different available printing technologies and important features of the individual technologies as well as various existing applications. Various challenges and potential design considerations for next generations of bioprinters are also discussed.