Improving AlphaFold modeling using implicit information from experimental density maps

Machine learning prediction algorithms such as AlphaFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including experimental information, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for crystallographic and electron cryo-microscopy map interpretation.

Iterative Named Entity Recognition with Conditional Random Fields

Named entity recognition (NER) constitutes an important step in the processing of unstructured text content for the extraction of information as well as for the computer-supported analysis of large amounts of digital data via machine learning methods. However, NER often relies on domain-specific knowledge, being conducted manually in a time- and human-resource-intensive process. These can be reduced with statistical models performing NER automatically. The current work investigates whether Conditional Random Fields (CRF) can be efficiently trained for NER in German texts, by means of an iterative procedure combining self-learning with a manual annotation–active learning–component. The training dataset increases continuously with the iterative procedure. Whilst self-learning did not markedly improve the performance of the CRF for NER, the manual annotation of sentences with the lowest probability of correct prediction clearly improved the model F1-score and simultaneously reduced the amount of manual annotation required to train the model. A model with an F1-score of 0.885 was able to be trained in 11.4 h.

Exploiting the S-Iteration Process for Solving Power Flow Problems: Novel Algorithms and Comprehensive Analysis

In recent studies, the competitiveness of the Newton-S-Iteration-Process (Newton-SIP) techniques to efficiently solve the Power Flow (PF) problems in both well and ill-conditioned systems has been highlighted, concluding that these methods may be suitable for industrial applications. This paper aims to tackle some of the open topics brought for this kind of techniques. Different PF techniques are proposed based on the most recently developed Newton-SIP methods. In addition, convergence analysis and a comparative study of four different Newton-SIP methods PF techniques are presented. To check the features of considered PF techniques, several numerical experiments are carried out. Results show that the considered Newton-SIP techniques can achieve up to an eighth order of convergence and typically are more efficient and robust than the Newton–Raphson (NR) technique. Finally, it is shown that the overall performance of the considered PF techniques is strongly influenced by the values of parameters involved in the iterative procedure.

Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling

Anaerobic co-digestion in wastewater treatment plants is looking increasingly like a straightforward solution to many issues arising from the operation of mono-digestion. Process modelling is relevant to predict plant behavior and its sensitivity to operational parameters, and to assess the feasibility of simultaneously feeding a digester with different organic wastes. Still, much work has to be completed to turn anaerobic digestion modelling into a reliable and practical tool. Indeed, the complex biochemical processes described in the ADM1 model require the identification of several parameters and many analytical determinations for substrate characterization. A combined protocol including batch Biochemical Methane Potential tests and analytical determinations is proposed and applied for substrate influent characterization to simulate a pilot-scale anaerobic digester where co-digestion of waste sludge and expired yogurt was operated. An iterative procedure was also developed to improve the fit of batch tests for kinetic parameter identification. The results are encouraging: the iterative procedure significantly reduced the Theil’s Inequality Coefficient (TIC), used to evaluate the goodness of fit of the model for alkalinity, total volatile fatty acids, pH, COD, volatile solids, and ammoniacal nitrogen. Improvements in the TIC values, compared to the first iteration, ranged between 30 and 58%.

Iterative deep learning for improved segmentation of endoscopic images

Iterative segmentation is a unique way to prune the segmentation maps initialized by faster inference techniques or even unsupervised traditional thresholding methods. We used our previous feedback attention-based method for this work and demonstrate that with optimal iterative procedure our method can reach competitive accuracies in endoscopic imaging. For this work, we have applied this segmentation strategy for polyps and instruments.

An iterative feature selection procedure for a classification problem based on the method of logical analysis of data

An iterative procedure for selecting features for classifying observations is proposed. The main principles of the proposed iterative procedure are ranking and selection of features according to the frequency of their use when constructing logical patterns based on the method of logical analysis of data. The empirical confirmation of the expediency of this procedure is given.

Iterative Optimal Design of Special Moment Resisting Devices for Steel Frames

The present paper proposes an iterative procedure devoted to reaching the optimal design of an innovative, recently proposed, moment resisting device. This special device, called Limited Resistance Plastic Device (LRPD), can be utilized, as an example, to equip a steel frame when it is required that the frame must be designed to substitute a masonry panel, i.e., it must be characterized by a structural behaviour as close as possible to the one of the replaced masonry wall. This purpose can be reached by designing the relevant frame imposing appropriate constraints on the elastic stiffness and on the limit resistance. The result can be obtained just by ensuring that the elastic stiffness and the limit resistance be independent of each other. To this aim it is necessary to suitably equip the steel frame by the previously cited LRPD. In particular, these moment resisting connections ensure that in a prefixed portion of the given beam element, the limit bending moment reduces without any variation of the global elastic stiffness. In order to reach this goal, the LRPD is substantially constituted by an inner portion, devoted to exhibit the desired reduced limit bending strength and to receive the plastic deformations, and two outer portions, devoted to guarantee the invariance of the elastic bending stiffness. The proposed iterative procedure allows to design a device respecting all the required features avoiding the presence of any dangerous local instability phenomenon. To this aim, the design will contain appropriate constraints ensuring that the device cross sections appertain to class 1 or class 2 I-cross section, as prescribed in the more recent standard codes. Some examples, validated by 3D solid tetrahedral elements analysis in ABAQUS environment, prove the good reliability of the proposed procedure and show the easy applicability of the computational approach.

STATIC CHARACTERISTICS OF OFFSET 8-LOBE JOURNAL BEARINGS

The 8-lobe journal bearings have found application in the bearing systems of spindles of grinding machines. The design of bearings and the large number of lobes and oil grooves assures good cooling conditions of bearing. These bearings can be manufactured as the bearings with cylindrical, non-continuous operating surfaces separated by six lubricating grooves, bearings with the pericycloidal shape of the bearing bore, and as offset journal bearing. This paper presents the results of the computation of static characteristics of an offset 8-lobe journal bearing operating under the conditions of an aligned axis of journal and bush, adiabatic oil film, and at the static equilibrium position of journal. Different values of bearing length to diameter ratio, relative clearance, and lobe relative clearance were assumed. Reynolds' energy and viscosity equations were solved by means of an iterative procedure. Adiabatic oil film, laminar flow in the bearing gap, and aligned orientation of journal in the bearing were considered.

Dynamical integrity assessment of stable equilibria: a new rapid iterative procedure

A new algorithm for the estimation of the robustness of a dynamical system’s equilibrium is presented. Unlike standard approaches, the algorithm does not aim to identify the entire basin of attraction of the solution. Instead, it iteratively estimates the so-called local integrity measure, that is, the radius of the largest hypersphere entirely included in the basin of attraction of a solution and centred in the solution. The procedure completely overlooks intermingled and fractal regions of the basin of attraction, enabling it to provide a significant engineering quantity in a very short time. The algorithm is tested on four different mechanical systems of increasing dimension, from 2 to 8. For each system, the variation of the integrity measure with respect to a system parameter is evaluated, proving the engineering relevance of the results provided. Despite some limitations, the algorithm proved to be a viable alternative to more complex and computationally demanding methods, making it a potentially appealing tool for industrial applications.