Using ensembles for accurate modelling of manufacturing processes in an IoT data-acquisition solution

The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.Projects TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and projects CCTT1/17/BU/0003 and BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León, all of them co-financed through European-Union FEDER funds

Mapping the scientific structure of organization and management of enterprises using complex networks

Understanding the scientific and social structure of a discipline is a fundamental aspect for scientific evaluation processes, identifying trends and niches, and balancing the trade-off between exploitation and exploration in research. In the present contribution, the production of doctoral theses is used as a proxy to analyze the scientific structure of the knowledge area of business organization in Spain. To that end, a complex networks approach is selected, and two different networks are built: (i) the social network of co-participation in thesis examining committees and thesis supervision, and (ii) a bipartite network of theses and thesis descriptors. The former has a modular structure that is partially explained by thematic specialization in different subdisciplines. The latter serves to assess the interdisciplinary structure of the discipline, as it enables the characterization of affinity levels between fields, research poles and thematic clusters. Our results have implications for the scientific evaluation and formal definition of related fields.Spanish Ministry of Science, Innovation and Universities (RED2018-102518-T), the Spanish State Research Agency (PID2020-118906GB-I00 and PID2020-119894GB-I00 via AEI/10.13039/501100011033), the Junta de Castilla y León – Consejería de Educación (BU055P20), Fundación La Caixa (2020/00062/001) and from NVIDIA Corporation and its donation of the TITAN Xp GPUs that facilitated this research. This work was partially supported by the European Social Fund, as the authors José Miguel Ramírez-Sanz, José Luis Garrido-Labrador and Alicia Olivares-Gil are the recipient of a predoctoral grant from the Department of Education of Junta de Castilla y León (VA) (ORDEN EDU/875/2021). In addition, this work was also partially supported by the Generalitat Valenciana via its Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, as Adrián Arnaiz is recipicient of a predoctoral grant

Adopting a multidisciplinary telemedicine intervention for fall prevention in Parkinson’s disease. Protocol for a longitudinal, randomized clinical trial

Approximately 40–70% of people with Parkinson’s disease (PD) fall each year, causing decreased activity levels and quality of life. Current fall-prevention strategies include the use of pharmacological and non-pharmacological therapies. To increase the accessibility of this vulnerable population, we developed a multidisciplinary telemedicine program using an Information and Communication Technology (ICT) platform. We hypothesized that the risk for falling in PD would decrease among participants receiving a multidisciplinary telemedicine intervention program added to standard office-based neurological care.This work was supported by the project PI19/00670 of the Ministerio de Ciencia, Innovacio´n y Universidades, Instituto de Salud Carlos II, Spain. The authors gratefully acknowledge the support of NVIDIA Corporation and its donation of the TITAN Xp GPUs used in this research

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.This work was supported by project PI19/00670 of the Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, Spain. The authors gratefully acknowledge the support of the NVIDIA Corporation and its donation of the TITAN Xp GPU used in this research. In addition, this work was partially supported by the European Social Fund, as the authors José Miguel Ramírez-Sanz, José Luis Garrido-Labrador, and Alicia Olivares-Gil are the recipients of a pre-doctoral grant (EDU/875/2021) from the Conserjería de Educación de la Junta de Castilla y León

Assistive Devices for Personal Mobility in Parkinson's Disease: A Systematic Review of the Literature

Artículo de revisiónGait abnormalities are a hallmark of Parkinson’s disease and contribute to falling risk. As disease symptoms progress, assistive devices are often prescribed. However, there are no guidelines for choosing appropriate ambulatory devices for gait impairment.This work was supported by the project PI19/00670 of the Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, Spain. The authors declare that there are no additional disclosures to report relevant to this work

Near barrier scattering of 8He on 208Pb

The exotic nucleus 8He is investigated by means of the measurement of the angular distributions of the elastic channel and the 6He and 4He fragment yields produced in the collision with a 208Pb target at two energies around the Coulomb barrier, 16 and 22 MeV. The experiment was performed at the GANIL-SPIRAL facility, with the aim of extracting information about the structure of 8He and the relevant reaction mechanisms. In this contribution, details of the experimental setup and preliminary data on elastic cross sections are reported

Experimental Assessment of Feature Extraction Techniques Applied to the Identification of Properties of Common Objects, Using a Radar System

Radar technology has evolved considerably in the last few decades. There are many areas where radar systems are applied, including air traffic control in airports, ocean surveillance, and research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving rise to myriad new applications, from the recognition of the material close objects are made, to the recognition of hand gestures. They have also been recently used to identify how a person interacts with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case, the radar is used to detect the orientation, movement, or distance from the objects to the user’s hands or the digital device. This paper presents a thoughtful comparative analysis of different feature extraction techniques and classification strategies applied on a series of datasets that cover problems such as the identification of materials, element counting, or determining the orientation and distance of objects to the sensor. The results outperform previous works using these datasets, especially when the accuracy was lowest, showing the benefits feature extraction techniques have on classification performance.This work was supported by the Spanish Ministry of Science and Innovation under project PID2020-119894GB-I00, Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE) co-financed through European Union FEDER funds. José Luis Garrido-Labrador was supported by the predoctoral grant (BDNS 510149) awarded by the Universidad de Burgos, Spain

An experiment on animal re-identification from video

In the face of the global concern about climate change and endangered ecosystems, monitoring individual animals is of paramount importance. Computer vision methods for animal recognition and re-identification from video or image collections are a modern alternative to more traditional but intrusive methods such as tagging or branding. While there are many studies reporting results on various animal re-identification databases, there is a notable lack of comparative studies between different classification methods. In this paper we offer a comparison of 25 classification methods including linear, non-linear and ensemble models, as well as deep learning networks. Since the animal databases are vastly different in characteristics and difficulty, we propose an experimental protocol that can be applied to a chosen data collections. We use a publicly available database of five video clips, each containing multiple identities (9 to 27), where the animals are typically present as a group in each video frame. Our experiment involves five data representations: colour, shape, texture, and two feature spaces extracted by deep learning. In our experiments, simpler models (linear classifiers) and just colour feature space gave the best classification accuracy, demonstrating the importance of running a comparative study before resorting to complex, time-consuming, and potentially less robust methods.This work is supported by the UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC), funded by grant EP/S023992/1. This work is also supported by the Junta de Castilla León under project BU055P20 (JCyL/FEDER, UE), and the Ministry of Science and Innovation under project PID2020-119894 GB-I00 co-financed through European Union FEDER funds. J.L. Garrido-Labrador is supported through Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021). I. Ramos-Perez is supported by the predoctoral grant (BDNS 510149) awarded by the Universidad de Burgos, Spain. J.J. Rodríguez was supported by mobility grant PRX21/00638 of the Spanish Ministry of Universities

Benefits of an educational intervention on diet and anthropometric profile of women with one cardiovascular risk factor

Fundamento y objetivo: Evaluar si una intervención educativa en mujeres perimenopáusicas con hiper-tensión, diabetes mellitus y/o dislipidemia sería capaz de mejorar la adherencia a un patrón de dietamediterráneo y conseguir cambios en parámetros antropométricos.Pacientes: Ensayo clínico aleatorizado de grupos paralelos: 320 mujeres (45-60 a˜nos) de 2 servicios deatención primaria urbanos. Variables a estudio: perímetro abdominal y de cadera, índice de masa cor-poral (IMC), grasa corporal total, visceral y de tronco (medidas con bioimpedancia) y adherencia a dietamediterránea (cuestionario MEDAS-14). Grupo intervención: 3 talleres interactivos sobre prevención deenfermedad cardiovascular, y grupo control: información por correo. Resultados: Concluyeron el estudio 230 mujeres (113 en el grupo control y 117 en el grupo interven-ción). Un año después, las diferencias entre grupos fueron significativas en todos los parámetros. En la comparación intragrupos, las mujeres del grupo intervención mantuvieron el IMC y mejoraron la adhe-rencia a la dieta mediterránea. El grupo control aumentó el IMC, el perímetro abdominal y de cadera ylos parámetros de grasa (corporal total, visceral y tronco).Conclusiones: Una sencilla intervención educativa en mujeres perimenopáusicas con riesgo cardiovascularpuede mejorar sus hábitos saludables.Background and objective: To assess whether an educational intervention in perimenopausal women with hypertension, diabetes mellitus and/or dyslipidaemia would improve adherence to a Mediterranean diet pattern and achieve changes in anthropometric parameters. Patients: Randomized clinical trial of parallel groups: 320 women (45-60 years) in 2 urban primary care services. Variables studied: hip and waist circumference, body mass index (BMI), total, visceral and trunk fat (bioimpedance measures) and adherence to Mediterranean diet (MEDAS-14 questionnaire). Intervention group: 3 interactive workshops on prevention of cardiovascular disease, and control group: information by post. Results: Two hundred and thirty women completed the study (113 control group and 117 intervention group). The differences between groups were significantin all parameters one year later. In the intragroup comparison, the intervention group maintained their BMI and improved adherence to the Mediterranean diet. The control group increased their BMI, abdominal and hip circumference and fat parameters (total, visceral and trunk fat)