The role of the social network in the study of adherence to diabetic retinopathy screening programs.

Diabetic retinopathy screenings are a vital strategy to avoid the severe consequences of this disease. However, their success depends on the adherence of the target population. The present work aims to review the adherence to diabetic retinopathy screening, more specifically the influence of the persons with diabetes' social network (contacts between persons with diabetes) on their screening behaviour.

The use of social simulation modelling to understand adherence to diabetic retinopathy screening programs.

The success of screening programs depends to a large extent on the adherence of the target population, so it is therefore of fundamental importance to develop computer simulation models that make it possible to understand the factors that correlate with this adherence, as well as to identify population groups with low adherence to define public health strategies that promote behavioral change. Our aim is to demonstrate that it is possible to simulate screening adherence behavior using computer simulations. Three versions of an agent-based model are presented using different methods to determine the agent's individual decision to adhere to screening: (a) logistic regression; (b) fuzzy logic components and (c) a combination of the previous.

An Interpretable Digital Twin for Self-Aware Industrial Machines.

This paper presents a proposed three-step methodology designed to enhance the performance and efficiency of industrial systems by integrating Digital Twins with particle swarm optimization (PSO) algorithms while prioritizing interpretability. Digital Twins are becoming increasingly prevalent due to their capability to offer a comprehensive virtual representation of physical systems, thus facilitating detailed simulations and optimizations. Concurrently, PSO has demonstrated its effectiveness for real-time parameter estimation, especially in identifying both standard and unknown components that influence the dynamics of a system.

Five regions, five retinopathy screening programmes: a systematic review of how Portugal addresses the challenge.

Background: The implementation of a population-based screening programme for diabetic retinopathy involves several challenges, often leading to postponements and setbacks at high human and material costs. Thus, it is of the utmost importance to promote the sharing of experiences, successes, and difficulties. However, factors such as the existence of regional programmes, specificities of each country's health systems, organisational and even linguistic barriers, make it difficult to create a solid framework that can be used as a basis for future projects.

PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data.

We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse.

Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System.

There is a direct relationship between the prevalence of musculoskeletal disorders of the temporomandibular joint and orofacial disorders. A well-elaborated analysis of the jaw movements provides relevant information for healthcare professionals to conclude their diagnosis. Different approaches have been explored to track jaw movements such that the mastication analysis is getting less subjective; however, all methods are still highly subjective, and the quality of the assessments depends much on the experience of the health professional.

Growing self-reconstruction maps.

In this paper, we propose a new method for surface reconstruction based on growing self-organizing maps (SOMs), called growing self-reconstruction maps (GSRMs). GSRM is an extension of growing neural gas (GNG) that includes the concept of triangular faces in the learning algorithm and additional conditions in order to include and remove connections, so that it can produce a triangular two-manifold mesh representation of a target object given an unstructured point cloud of its surface. The main modifications concern competitive Hebbian learning (CHL), the vertex insertion operation, and the edge removal mechanism.