Uninova

A ecocardiografia funcional neonatal (Targeted Neonatal Echocardiography – TnECHO) tem sido utilizada como ferramenta de triagem hemodinâmica precoce em recém-nascidos pré-termo extremos, permitindo a avaliação da fisiologia cardiovascular em tempo real e a individualização das condutas clínicas. O presente estudo tem como objetivo avaliar o papel prognóstico de marcadores ecocardiográficos funcionais e investigar o impacto da triagem hemodinâmica precoce guiada por ecocardiografia funcional neonatal (TnECHO) sobre a morbimortalidade neonatal em recém-nascidos pré-termo extremos. Este estudo observacional multicêntrico avaliou o papel prognóstico de marcadores ecocardiográficos funcionais e o impacto da triagem precoce guiada por TnECHO na morbimortalidade neonatal. Foram incluídos recém-nascidos pré-termo <27–28 semanas e/ou <1.250 g, comparando-se aqueles submetidos à triagem entre 12 e 18 horas de vida com um grupo sob cuidado padrão. O desfecho primário foi morte ou hemorragia intraventricular grave até 36 semanas de idade pós-menstrual. A implementação da triagem precoce associou-se à redução significativa do desfecho composto, de 29% para 16%, com maior chance de sobrevivência livre de hemorragia intraventricular grave após ajuste. A hipertensão pulmonar precoce identificada pela TnECHO destacou-se como o principal marcador prognóstico. Em síntese, a TnECHO realizada precocemente mostrou-se uma ferramenta útil para estratificação de risco hemodinâmico e redução de desfechos adversos em prematuros extremos.

In recent years, healthcare is evolving to meet the needs of a growing and ageing population. To support better and more reliable care, a comprehensive and up-to-date Personal Health Record (PHR) is essential. Ideally, the PHR should contain all health-related information about an individual and be available for sharing with healthcare institutions. However, due to interoperability issues of the medical and fitness devices, most of the times, the PHR only contains the same information as the patient Electronic Health Record (EHR). This results in lack of health-related information (e.g., physical activity, working patterns) essential to address medical conditions, support prescriptions, and treatment follow-up. This paper introduces the B-Health IoT Box, a fog IoT computing framework for eHealth interoperability and data collection that enables seamless, secure integration of health and contextual data into interoperable health records. The system was deployed in real-world settings involving over 4500 users, successfully collecting and transmitting more than 1.5 million datasets. The validation shown that data was collected, harmonized, and properly stored in different eHealth platforms, enriching data from personal EHR with mobile and wearable sensors data. The solution supports real-time and near real-time data collection, fast prototyping, and secure cloud integration, offering a modular, standards-compliant gateway for digital health ecosystems. The health and health-related data is available in FHIR format enabling interoperable eHealth ecosystems, and better equality of access to health and care services.

Improving the energy efficiency of traditional production methods while preserving their cultural and economic value is a challenge aligned with the Sustainable Development Goals of the 2030 agenda. Refrigeration during cheese maturation is particularly energy-intensive, contributing significantly to greenhouse gas emissions and operating costs. An approach to make traditional cheese production more sustainable, through the development of a prototype ripening chamber with a natural refrigerant-based refrigeration system powered by renewable energy was studied. A dedicated system based on an Internet of Things architecture was developed using low-cost sensors, microcontroller units, and single-board computers to enable real-time measurement and monitoring of environmental variables and energy consumption throughout the ripening process. A comparative analysis was conducted using ewe’s milk cheese, produced and ripened with Protected Designation of Origin conditions, in both the prototype and the conventional chambers over four weeks, quantifying energy consumption and evaluating product quality. Results demonstrate the technical feasibility of energy efficient and sustainable refrigeration systems, as well as the possibility of retrofitting installed cheese ripening chambers with affordable IoT monitoring systems, while maintaining traditional cheese quality standards.

This paper presents a model-driven distributed controller development approach, supported by Petri nets modeling that relies on low-code strategies. The proposed approach addresses distributed controller systems having automation and/or cyber-physical systems as targeted application areas. The distributed controller system can be viewed as a globally asynchronous locally synchronous (GALS) system and its development is fully supported by a web-based tool framework offering comprehensive support for integrating hardware-software co-design techniques when mapping components to specific execution platforms. All development phases are supported, starting with the specification by editing the Petri net model and ending up with the deployment to specific implementation platforms, including FPGAs and microcontroller-based ones, adopting a low-code strategy. The framework also supports simulation and behavioral property verification. An example of an automation system application is presented to illustrate the adequacy of the approach.

This paper analyzes the channel estimation of rate splitting multiple access (RSMA) wireless network through the full-duplex amplify-and-forward (AF) relay. Basically, full-duplex transmission can improve temporal efficiency, however the loop interference is an unavoidable problem that occurs in the strong user of this proposed network. The orthogonal frequency division multiplexing (OFDM) system is used to provide high data rate communication, assuming the presence of phase noise (PN) in local oscillators. Using the least square (LS) estimate, the channel coefficients of the proposed RSMA relay network are estimated. In addition, convex optimization techniques are applied to estimate the phase noise components of this network. The problem is formulated by optimizing phase noise under transmit power constraints. We analyze the Bit Error Rate (BER) performance of the proposed network under binary phase shift keying (BPSK) modulation and 16-quadrature amplitude modulation (QAM). Simulation results demonstrate that channel estimation achieves better performance after the PN compensation.

Stations for maintaining strip tension are widely used at metallurgical plants. The station device usually has several rollers, therefore, these systems are multi-drive. Asynchronous motors controlled by frequency converters, which cope with their tasks in normal operation, are widely used in these mechanisms. However, in the event of an emergency, such as loss of torque or its excess, the vector control system does not assume an emergency stop of the facility, but at the same time further operation of the equipment is not allowed. To solve the problem under consideration, it is proposed to introduce a block operating on the basis of fuzzy logic into the control system - a fuzzy controller that responds to a critical change in torque and affects the speed of the motors, causing them to stop.

This paper presents a gamified Virtual Reality (VR) application integrated with a second-screen Web interface to support exposure therapy for children with sensory food aversion. The system allows therapists to configure therapeutic sessions, monitor user actions in real time, and adapt the virtual environment according to the patient's responses. The Web interface records session data, including behavioral performance and, when available, biosignals such as EEG and ECG, building a historical profile for each patient. The solution was developed through a co-design process with therapists to ensure usability and alignment with therapeutic practice. Preliminary results show the system's potential to improve engagement, support therapist intervention, and enable future developments, such as the integration of real-time neurofeedback to adapt the experience based on physiological data.

The increase in the number of wildfires in recent years in different parts of the world has caused growing concern among the population, since the consequences of these fires go beyond the destruction of the ecosystem. With the growing relevance of the Internet of Things (IoT) industry, developing solutions for the early detection of fires is of critical importance. This paper proposes a low-cost network based on Long-Range (LoRa) technology to autonomously assess the level of fire risk and the presence of a fire in rural areas. The system consists of several LoRa nodes with sensors to measure environmental variables such as temperature, humidity, carbon monoxide, air quality, and wind speed. The data collected is sent to a central gateway, where it is stored, processed, and later sent to a website for graphical visualization of the results. In this paper, a survey of the requirements of the devices and sensors that compose the system was made. After this survey, a market study of the available sensors was carried out, ending with a comparison between the sensors to determine which ones met the objectives. Using the chosen sensors, a study was made of possible power solutions for this prototype, considering the expected conditions of use. The system was tested in a real environment, and the results demonstrate that it is possible to cover a circular area with a radius of 2 km using a single gateway. Our system is prepared to trigger fire hazard alarms when, for example, the signals for relative humidity, ambient temperature, and wind speed are below or equal to 30%, above or equal to 30 °C, and above or equal to 30 m/s, respectively (commonly known as the 30-30-30 rule).

The paper deals with the evaluation of the main parameters of reliability of contacts of devices with voltage up to 1000 V, operated in industrial electrical networks. The purpose of the work is to study the reliability parameters of contacts using the method of processing data on failures of devices with the analysis of physical processes occurring in the contact connections. To increase the accuracy of determining the intensity of failures introduced coefficients that take into account the influence of the main factors affecting the apparatuses. The study of the contact life of apparatuses was performed by analytical and graphical-analytical methods. To increase the reliability of the apparatuses parameters estimation it is necessary to use the graphical-analytical method taking into account correction coefficients. The value of the number of switching cycles of the device, after which the occurrence of failure is most likely, depends on its loading factor. For the studied devices after the expiration of 1/4 of switching resource the probability of failure-free operation becomes lower than the permissible value. The obtained results are recommended to be taken into account when scheduling preventive maintenance in the electrical networks of the shop-floor power supply.