Full Interaction Model Research Articles

The effect of 2020 lockdown on bank internal ratings

We investigate how banks’ internal ratings were affected by the first lockdown shock in Italy (March-June 2020). Using monthly proprietary data from a regional bank between October 2019 and January 2022, and adding credit registry data and balance sheet data of client firms, we estimate the lockdown effect on the change in six different ratings assigned by the bank to client firms in June 2020 and December 2020. We obtain three main results. First, controlling for both supply and demand factors, the lockdown is associated with a decline in four of the six ratings. Second, the lockdown effect also depends on specific firm characteristics, i.e. the lockdown effect appears to be mitigated in the presence of high turnover or for firms belonging to essential sectors. However, these interaction effects do not hold for all ratings, suggesting that each type of rating is influenced by different firm characteristics. Third, once we consider a full interaction model, the lockdown effect disappears in the medium term.

Variational quantum eigensolver for closed-shell molecules with non-bosonic corrections.

The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge that motivates research on compact system representations to reduce algorithm complexity. Toward this end, the variational quantum eigensolver (VQE) used to perform quantum simulations is considered to be one of the most promising algorithms for quantum chemistry in the NISQ era. We investigate reduced mapping of one spatial orbital to a single qubit to analyze the ground state energy in a way that the Pauli operators of qubits are mapped to the creation/annihilation of singlet pairs of electrons. To include the effect of non-bosonic (or non-paired) excitations, we introduce a simple correction scheme in the electron correlation model approximated by the geometrical mean of the bosonic (or paired) terms. Employing it in a VQE algorithm, we assess ground state energies of H2O, N2, and Li2O in good agreement with full configuration interaction (FCI) models respectively, using only 6, 8, and 12 qubits with quantum gate depths proportional to the squares of the qubit counts. With the adopted seniority-zero approximation that uses only one half of the qubit counts of a conventional VQE algorithm, we find that our non-bosonic correction method reaches reliable quantum chemistry simulations at least for the tested systems.

Dynamic analysis of offshore wind turbines subjected to the combined wind and ice loads based on the cohesive element method

Ice loads are an important and decisive factor for the safe operation of offshore wind turbines (OWTs). In severe environment load cases, it shall lead to prominent ice-induced vibration and ice-induced fatigue failure of OWT structures. Based on the cohesive element method (CEM) and considering the pile–soil interaction used by nonlinear distributed springs, the full interaction model of the ice and monopile OWT structure with an ice-breaking cone in a cold sea region is established in this study. Furthermore, the Tsai-Wu failure criterion and the empirical failure formula of maximum plastic failure strain are used to describe the mechanical behavior of ice bending failure in the collision simulation tool LS-DYNA, and the dynamic ice loads under different ice velocities and cone angles are statistically analyzed. Finally, according to the interaction process between sea ice and OWT containing the ice-breaking cone, the dynamic response of OWT under the combined wind and ice loads is studied, and the most reasonable ice-breaking cone angle is determined. The results show that the method adopted in this paper can well simulate the bending failure process of sea ice. Concurrently, the cone angle has a significant impact on the dynamic response and damage of the OWT, and the recommended optimal cone angle is 60.

Assessing mechanical vibration-altered wall shear stress in digital arteries

The aim of this study is to implement and validate a method for assessing acute vibration-altered Wall Shear Stress (WSS) in the proper volar digital artery of the non-exposed left forefinger when subjecting the right hand to mechanical vibration. These changes of WSS may be involved in Vibration White Finger. Hence, an experimental device was set-up to link a vibration shaker and an ultra-high frequency ultrasound scanner. The Womersley-based WSS was computed by picking up the maximum velocity from pulse Wave Doppler measurements and extracting the artery diameter from B-mode images through an in-house image processing technique. The parameters of the former method were optimised on numerical ultrasound phantoms of cylindrical and lifelike arteries. These phantoms were computed with the FIELD II and FOCUS platforms which mimicked our true ultrasound device. The Womersley-based WSS were compared to full Fluid Structure Interaction (FSI) and rigid wall models built from resonance magnetic images of a volunteer-specific forefinger artery. Our FSI model took into account the artery’s surrounding tissues. The diameter computing procedure led to a bias of 4%. The Womersley-based WSS resulted in misestimating the FSI model by roughly 10% to 20%. No difference was found between the rigid wall computational model and FSI simulations. Regarding the WSS measured on a group of 20 volunteers, the group-averaged basal value was 3 Pa, while the vibration-altered WSS was reduced to 1 Pa, possibly triggering intimal hyperplasia mechanisms and leading to the arterial stenoses encountered in patients suffering from vibration-induced Raynaud’s syndrome.

Experimental and Finite Element (FE) Studies on the Behaviour of Horizontally Curved Composite Bridges Deck

This paper aims to study the behaviour of horizontally curved composite steel-concrete bridges deck slab and beams under static load, using experimental tests and nonlinear finite element analysis. Two different specimens were examined in the structure laboratory, one of them was horizontally curved composite deck slab and steel girders with full interaction between the concrete deck and the steel girders, while the other with partial interaction. Each specimen has three curved I-girders acted parallel connected with X shape cross frames. Linear Variable Differential Transducers (LVDT) and strain gauges were used at critical points to measure deflections and strain respectively. Three dimensional finite element (FE) models with 137119 elements were built and analysed using ANSYS software version 14. Models dimensions were 1/10 in both length and radius of curvature of the Federal Highway Administration (FHWA) full scale model. Numerical results have been compared with the experimental test results. Maximum values and distribution of both stresses and strains at top flanges, bottom flanges and the web tension zone for numerical modelling results, showed close agreement with the experimental tests results in both full and partial interaction models. Also, similar failure modes were encountered from both numerical modelling and experimental tests. Maximum deflection values obtained from numerical modelling were slightly below the experimental values and this was attributed to the stiffer numerical model. Further, mesh refinement could have increase accuracy of numerical predictions.

Modeling Multicomponent Interventions in Network Meta-Analysis.

There is a rapid increase in trials assessing healthcare interventions consisting of a combination of drugs (polytherapies) or multiple components. In the latter type of interventions (also known as complex interventions), the aspect of complexity is of paramount importance. For example, nonpharmacological interventions, such as psychological interventions or self-management interventions, usually share common components that relate to the nature of intervention, who delivers it, or where and how. In a network of trials, there is often the need to identify the most effective (or safest) component and/or combination of components. Four key meta-analytical approaches have been presented in the literature to handle complex interventions. These include (a) the single-effect model, (b) the full interaction model, (c) the additive main effects model, and (d) the two-way interaction model. In this chapter, we present and discuss the advantages and limitations of these approaches. We illustrate these methods using a network that assesses the relative effects of self-management interventions on waist size in patients with type 2 diabetes.

SNPxE: SNP-environment interaction pattern identifier

BackgroundInteractions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary.ResultsSNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair.ConclusionsSNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).

Steel-Concrete Composite Beams with Precast Hollow-Core Slabs: A Sustainable Solution

Industrialization of construction makes building operation more environmental friendly and sustainable. This change is necessary as it is an industry that demands large consumption of water and energy, as well as being responsible for the disposal of a high volume of waste. However, the transformation of the construction sector is a big challenge worldwide. It is also well known that the largest proportion of the material used in multistory buildings, and thus its carbon impact, is attributed to their slabs being the main contributor of weight. Steel-Concrete composite beams with precast hollow-core slabs (PCHCSs) were developed due to their technical and economic benefits, owing to their high strength and concrete self-weight reduction, making this system economical and with lower environmental footprint, thus reducing carbon emissions. Significant research has been carried out on deep hollow-core slabs due to the need to overcome larger spans that resist high loads. The publication SCI P401, in accordance with Eurocode 4, is however limited to hollow-core slabs with depths from 150 to 250 mm, with or without a concrete topping. This paper aims to investigate hollow-core slabs with a concrete topping to understand their effect on the flexural behavior of Steel-Concrete composite beams, considering the hollow-core-slab depth is greater than the SCI P401 recommendation. Consequently, 150 mm and 265 mm hollow-core units with a concrete topping were considered to assess the increase of the hollow core unit depth. A comprehensive computational parametric study was conducted by varying the in situ infill concrete strength, the transverse reinforcement rate, the shear connector spacing, and the cross-section of steel. Both full and partial interaction models were examined, and in some cases similar resistances were obtained, meaning that the same strength can be obtained for a smaller number of shear studs, i.e., less energy consumption, thus a reduction in the embodied energy. The calculation procedure, according to Eurocode 4 was in favor of safety for the partial-interaction hypothesis.

Bayesian analysis of differential effects in multi-group regression methods

In regression analysis, the data sample is often composed of diverse sub-populations such as ethnicities and geographical regions. In multiple application areas, it is important to identify the groups where each covariate has a positive, negative or null impact on the response. If the number of sub-populations is small, a full interaction model may be fit with group-specific covariate effects. However, if the number of groups is very large, for example, hospitals or other clustering units, such a model is not identifiable. Therefore, we propose a prior distribution which combines the information across sub-populations with a similar covariate effect. This Bayesian analysis of differential effects (BADE) classifies the group-specific covariate effects as positive, negative or null. Besides allowing the analysis of differential effects for many sub-populations, the proposed approach improves substantially the identification of important interactions in cases with few groups. This is illustrated via simulations. The procedure is motivated on, and applied to, a large study related to patients’ satisfaction with hospitals, where we show that classifying group-specific covariate effects based on methods such as mixed-effects models may be strongly misleading.

Identification of native protein structures captured by principal interactions

BackgroundEvaluation of protein structure is based on trustworthy potential function. The total potential of a protein structure is approximated as the summation of all pair-wise interaction potentials. Knowledge-based potentials (KBP) are one type of potential functions derived by known experimentally determined protein structures. Although several KBP functions with different methods have been introduced, the key interactions that capture the total potential have not studied yet.ResultsIn this study, we seek the interaction types that preserve as much of the total potential as possible. We employ a procedure based on the principal component analysis (PCA) to extract the significant and key interactions in native protein structures. We call these interactions as principal interactions and show that the results of the model that considers only these interactions are very close to the full interaction model that considers all interactions in protein fold recognition. In fact, the principal interactions maintain the discriminative power of the full interaction model. This method was evaluated on 3 KBPs with different contact definitions and thresholds of distance and revealed that their corresponding principal interactions are very similar and have a lot in common. Additionally, the principal interactions consisted of 20 % of the full interactions on average, and they are between residues, which are considered important in protein folding.ConclusionsThis work shows that all interaction types are not equally important in discrimination of native structure. The results of the reduced model based on principal interactions that were very close to the full interaction model suggest that a new strategy is needed to capture the role of remaining interactions (non-principal interactions) to improve the power of knowledge-based potential functions.

Building more accurate decision trees with the additive tree

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

Comparative Effectiveness of the Core Components of Cardiac Rehabilitation on Mortality and Morbidity: A Systematic Review and Network Meta-Analysis.

A systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) evaluating the core components of cardiac rehabilitation (CR), nutritional counseling (NC), risk factor modification (RFM), psychosocial management (PM), patient education (PE), and exercise training (ET)) was undertaken. Published RCTs were identified from database inception dates to April 2017, and risk of bias assessed using Cochrane’s tool. Endpoints included mortality (all-cause and cardiovascular (CV)) and morbidity (fatal and non-fatal myocardial infarction (MI), coronary artery bypass surgery (CABG), percutaneous coronary intervention (PCI), and hospitalization (all-cause and CV)). Meta-regression models decomposed treatment effects into the main effects of core components, and two-way or all-way interactions between them. Ultimately, 148 RCTs (50,965 participants) were included. Main effects models were best fitting for mortality (e.g., for all-cause, specifically PM (hazard ratio HR = 0.68, 95% credible interval CrI = 0.54–0.85) and ET (HR = 0.75, 95% CrI = 0.60–0.92) components effective), MI (e.g., for all-cause, specifically PM (hazard ratio HR = 0.76, 95% credible interval CrI = 0.57–0.99), ET (HR = 0.75, 95% CrI = 0.56–0.99) and PE (HR = 0.68, 95% CrI = 0.47–0.99) components effective) and hospitalization (e.g., all-cause, PM (HR = 0.76, 95% CrI = 0.58–0.96) effective). For revascularization (including CABG and PCI individually), the full interaction model was best-fitting. Given that each component, individual or in combination, was associated with mortality and/or morbidity, recommendations for comprehensive CR are warranted.

Targeting Neuroplasticity, Cardiovascular, and Cognitive-Associated Genomic Variants in Familial Alzheimer\u2019s Disease

The identification of novel genetic variants contributing to the widespread in the age of onset (AOO) of Alzheimer’s disease (AD) could aid in the prognosis and/or development of new therapeutic strategies focused on early interventions. We recruited 78 individuals with AD from the Paisa genetic isolate in Antioquia, Colombia. These individuals belong to the world largest multigenerational and extended pedigree segregating AD as a consequence of a dominant fully penetrant mutation in the PSEN1 gene and exhibit an AOO ranging from the early 1930s to the late 1970s. To shed light on the genetic underpinning that could explain the large spread of the age of onset (AOO) of AD, 64 single nucleotide polymorphisms (SNP) associated with neuroanatomical, cardiovascular, and cognitive measures in AD were genotyped. Standard quality control and filtering procedures were applied, and single- and multi-locus linear mixed-effects models were used to identify AOO-associated SNPs. A full two-locus interaction model was fitted to define how identified SNPs interact to modulate AOO. We identified two key epistatic interactions between the APOE*E2 allele and SNPs ASTN2-rs7852878 and SNTG1-rs16914781 that delay AOO by up to ~ 8 years (95% CI 3.2–12.7, P = 1.83 × 10−3) and ~ 7.6 years (95% CI 3.3–11.8, P = 8.69 × 10−4), respectively, and validated our previous finding indicating that APOE*E2 delays AOO of AD in PSEN1 E280 mutation carriers. This new evidence involving APOE*E2 as an AOO delayer could be used for developing precision medicine approaches and predictive genomics models to potentially determine AOO in individuals genetically predisposed to AD.

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. Supplementary data are available at Bioinformatics online.

Quantum filtering equations for atom-single photon interaction by homodyne detection: Considering full interaction Rabi model

Interaction of a two level atom with a single photon packet is investigated by considering full interaction model of Rabi model. A homodyne measurement providence information about the photon-atom system. Quantum filtering equations for this system are found and the results are discussed. Effects of adding an external magnetic field that is explained.

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . hlin1@lsuhsc.edu. Supplementary data are available at Bioinformatics online.

Quantitative study of the f occupation in CeMIn5 and other cerium compounds with hard X-rays

We present bulk-sensitive hard X-ray photoelectron spectroscopy (HAXPES) data of the Ce3d core levels and lifetime-reduced L-edge X-ray absorption spectroscopy (XAS) in the partial fluorescence yield (PFY) mode of the CeMIn5 family with M=Co, Rh, and Ir. The HAXPES data are analyzed quantitatively with a combination of full multiplet and configuration interaction model which allows correcting for the strong plasmons in the CeMIn5 HAXPES data, and reliable weights wn of the different fn contributions in the ground state are determined. The CeMIn5 results are compared to HAXPES data of other heavy fermion compounds and a systematic decrease of the hybridization strength Veff from CePd3 to CeRh3B2 to CeRu2Si2 is observed, while it is smallest for the three CeMIn5 compounds. The f-occupation, however, increases in the same sequence and is close to one for the CeMIn5 family. The PFY-XAS data confirm an identical f-occupation in the three CeMIn5 compounds and a phenomenological fit to these PFY-XAS data combined with a configuration interaction model yields consistent results.

Optimal cross-over designs for full interaction models

We consider repeated measurement designs when a residual or carry-over effect may be present in at most one later period. Since assuming an additive model may be unrealistic for some applications and leads to biased estimation of treatment effects, we consider a model with interactions between carry-over and direct treatment effects. When the aim of the experiment is to study the effects of a treatment used alone, we obtain universally optimal approximate designs. We also propose some efficient designs with a reduced number of subjects.

Molecular Nonlinear Optical Parameters of π-Conjugated Nonalternant Hydrocarbons Obtained in Semiempirical Local Coupled-Cluster Theory

The π-electron dipole polarizabilities and hyperpolarizabilities of organic conjugated hydrocarbons with odd-membered carbon cycles (nonalternant systems) are calculated using local semiempirical coupled-cluster theory. The peculiarity of the approach is the use of local ethylene π-molecular orbitals to represent double bonds in the molecule (cue-CCSD approach). The size-limit values of the (hyper)polarizabilities for different types of nonalternant oligomers obtained in the calculations show a significant dependence on the molecular structure. The effect of the electron correlation in the hyperpolarizability calculations is investigated using different levels of the coupled-cluster method including the simplest CCSD model and other models up to the model where the correlation effects due to all four electron excitations are accounted for, as well as with the full configuration interaction model.

Comparisons of power of statistical methods for gene–environment interaction analyses

Any genome-wide analysis is hampered by reduced statistical power due to multiple comparisons. This is particularly true for interaction analyses, which have lower statistical power than analyses of associations. To assess gene-environment interactions in population settings we have recently proposed a statistical method based on a modified two-step approach, where first genetic loci are selected by their associations with disease and environment, respectively, and subsequently tested for interactions. We have simulated various data sets resembling real world scenarios and compared single-step and two-step approaches with respect to true positive rate (TPR) in 486 scenarios and (study-wide) false positive rate (FPR) in 252 scenarios. Our simulations confirmed that in all two-step methods the two steps are not correlated. In terms of TPR, two-step approaches combining information on gene-disease association and gene-environment association in the first step were superior to all other methods, while preserving a low FPR in over 250 million simulations under the null hypothesis. Our weighted modification yielded the highest power across various degrees of gene-environment association in the controls. An optimal threshold for step 1 depended on the interacting allele frequency and the disease prevalence. In all scenarios, the least powerful method was to proceed directly to an unbiased full interaction model, applying conventional genome-wide significance thresholds. This simulation study confirms the practical advantage of two-step approaches to interaction testing over more conventional one-step designs, at least in the context of dichotomous disease outcomes and other parameters that might apply in real-world settings.