Alzheimer’s disease (AD) is the most common cause of dementia. Several haemodynamic risk factors for AD have been identified, including high systolic blood pressure (BP), brain hypoperfusion and arterial stiffness, as well as ageing. We propose a novel approach for assessing haemodynamic risk factors by analysing arterial pulse waves (PWs). The aim of this feasibility study was to determine whether features extracted from PWs might have utility for stratifying patients at risk of AD.

A numerical model of PW propagation was used to simulate arterial PWs at a range of potential measurement sites, for virtual subjects of each age decade from 25 to 75 years, with subjects at each age exhibiting normal variation in BP and arterial stiffness. Several PW features were extracted, and their relationships with AD risk factors were investigated.

PWs closer to the brain (at carotid and temporal arteries) were found to be suitable for detecting haemodynamic changes associated with AD. Several candidate PW features were identified for future clinical testing. These included features extracted from both BP and photoplethysmogram (PPG) PWs.

This study demonstrates the potential feasibility of using non-invasive PWs to assess haemodynamic risk factors for AD. Not only could these factors be assessed from the BP PW, which is usually measured by a skilled operator, but also from the PPG, which can be acquired by smart watches and phones. If the findings are replicated in clinical studies, then this may provide opportunity for patients to assess their own risk and make lifestyle changes accordingly.

The purpose of this project was to design and implement an autonomous system based on Arduino to monitor environmental parameters that intervene in the perception of human comforts such as temperature, humidity, and solar radiation, and use them to analyze factors related to climate control and energy efficiency in buildings.

The system was tested in laboratory conditions as well as by in situ measurements of building elements and living spaces. Some of the experiments carried out were contrasted with numerical simulations that allowed us to understand the implemented system.

The thermal and optical sensors were calibrated in the laboratory by comparison with standard probes for temperature and illuminance. The result was used to adjust the measurements and to control any possible errors due to the sensors.

Preliminary measurements were made in systems that simulate enclosures, with walls of different thermal conductivities, for comparison with computer simulations, and later measurements were performed outside the laboratory, which was contrasted with a thermodynamic simulation tool.

Additionally, it was assessed the use of the system to measure the surface evolution of thermal parameters in a space.

With the use of the system to measure real conditions and its calibration, it was possible to demonstrate its practicality and good operation. The result of this work can be the basis for an interesting alternative to systems for recording and monitoring thermodynamic variables in the field of architecture and energy efficiency for its versatility and economy.

Cardiopulmonary Resuscitation – CPR is a recurring practice in medical urgency and emergency. CPR is characterizes by a set of maneuvers performed in an attempt to reanimate the victim of cardiac and / or respiratory arrest and is intended to cause the heart and lung to return to their normal functions while maintaining oxygenation of the brain. This scenario represents an extreme medical emergency, which can lead to irreversible brain injury and even death if appropriate measures to restore blood flow and breathing are not performed properly. Thus, students and professionals in the area must acquire skills that enable them to act quickly and efficiently during patient care. This work proposes to adapt an existing sensor and embed on mannequins used in CPR training to accurately measure the amount of air supplied to the lungs during ventilation. The proposed sensor consists of measuring the airflow using propellers. The method directly measures the variable of interest and makes reference to expirometric techniques in the elaboration of its model, improving the realism of the dummies. The projected sensor presented an agreement with its theoretical model and with the expirometric model, besides advantages over the sensors that are used for this purpose. It was suitable for applications with an accuracy of ±17 mL, and resolution of 50 mL and 26 mL for initial and final measurements, respectively, ranging from 30 to 1800 mL.

Today administration and management of public services and infrastructure relies more and more on user data collected by many domestic and private devices including smartphones and Internet services. User data and user decision making has a large impact on public decision making processes, for example, plan-based traffic flow control. Furthermore, intelligent behaviour, i.e., cognitive, knowledge-based, adaptive, and self-organizing behaviour based on learning, emerges rapidly in today's machines and environments.

It is difficult to study such large-scale data collection, data mining, and their effect on public services like smart traffic control in field studies. Commonly, simulation in virtual reality worlds is used instead to derive an evaluation. In this work, a new concept and framework for augmented virtual reality simulation is introduced, suitable, but not limited to, to investigate large-scale socio-technical systems. Mobile agents are used to combine in-field ubiquitous Crowdsensing, e.g., performed by mobile devices, with simulation. The agents can operate both in real and virtual worlds including games and fusion both worlds be seamless migration. Agents are loosely coupled to their environment and platform and interact with each other via Tuple Spaces (generative communication) and via unicast or broadcast signals. Mobility is provided by agent process snapshot migration between platforms.

The agents are programmed in JavaScript and executed by the JavaScript Agent Machine (JAM) that can be deployed on a wide range of host platforms (mobile devices, servers, IoT devices, WEB browser). Simulation is performed by the Simulation Environment for JAM (SEJAM), extending JAM with a simulation layer providing visualization of 2D- and 3D worlds and an advanced simulation control with a wide range of integrated analysis tools. Each JAM node is capable to process thousands of agents concurrently. JAM and SEJAM nodes can be connected in clusters and in the Internet (of Things) enabling large-scale "real-world"-in-the-loop simulations with Millions of agents.

Such large-scale simulations producing Big Data and can offer an enhanced statistical strength beyond Big data analysis by understanding correlation between data and cause. Due to the loosely coupling of agents the emergence of strong heterogeneous Multiagentsystems and their cooperation can be investigated with one unified framework without disruption. Agents can represent different behaviour, goals, and individuals like chat bots, machines, robots, or artificial humans and their interaction with virtual and real individuals. We expect to simulate large-scale agent societies with agent population beyond one Million individual agents to get statistical strength. Agents are mobile with respect to networks of processing platform in the real world and with respect to the location and interaction domain in virtual worlds. The agent behaviour, planning, and decision making is based on an individual and global (domain specific) knowledge base.

Related video presentation at: http://www.sblab.de/assets/ecsa2018/out/ecsa2018.html

Grinding is one of the most commonly used finishing processes in the manufacture of precision parts. The monitoring of the workpiece surface quality is considered highly complex due to particularities of the cutting tool and material removal mechanism. In this context, the monitoring of the grinding process is very important for the manufacturing industry and of great interest to researchers in the field. A large number of grinding process monitoring studies have been developed aiming at the automation of this process. The objective of this work is to monitor the material removal in the grinding of SAE 4340 steel workpieces by piezoelectric transducers in the emitter and receiver modes along with digital signal processing techniques. The tests were performed in a surface grinding machine equipped with an aluminum oxide grinding wheel. The transducers signals were sampled at a sampling frequency of 2 MHz. The digital signal processing was performed through the spectrum analysis and the application of techniques such as root mean square. The workpiece weight was measured by means of a digital scale before and after the grinding tests. The results show that the monitoring technique proposed in this work is sensitive to the material removal in the grinding process. The appropriate selection of frequency bands allows for the best diagnosis in relation to the events that occur during the grinding process.