This paper discuss about existing mathematical models for in-cabin CO₂ concentrations and compare it with experimentally measured CO₂ concentration. The study involves with different size light passenger vehicles under different occupancy and airflow conditions. The sensor board measures temperature, humidity and atmospheric gas concentrations at front and rear of the cabin during journey and these data are used to compare with the theoretical model predicted values. The recirculation mode made rice in CO₂ levels more rapidly compared to open air intake through vehicle air conditioning system. However, the open air intake mode is more vulnerable to toxic gases inside tunnels and confined environments. Therefore measuring in cabin carbon dioxide and oxygen levels and opening ventilation intelligently is essential in the driver-less future vehicles. This study pays the path for intelligent control of cabin air depending on the external environment the vehicle being passing through and the present cabin air quality. New algorithms for predicting in cabin air quality will be derived through experimental observations.

This paper discusses a potential low-cost IoT-enabled COVID-19-compliance system with the ability to monitor and assist in social distancing and standard operating procedure compliance. In the current wake of the COVID-19 pandemic, restricting and limiting community spread has become critical for local governments and administrations. Worldwide lockdowns were implemented despite their concomitant socio-economic impacts on business communities and as a result, businesses were affected. To resume the business/public office activities during the turmoil caused by the pandemic, enforcing COVID SOP compliance is essential. The inability to deploy expensive COVID-19 SOP compliance is challenging for most small businesses. The current practices undertaken by the businesses include contact tracing applications, hiring, or re-appropriate staff members to implement compliance measures such as measuring temperatures of incoming customers and ensuring a safe social distance is kept at all times. The proposed system ensures compliance by counting the number of people in the vicinity, ensure queuing distance, monitors the temperature, and warn attendees/office authorities for any violations. The system also maintains the data on a cloud for logging and monitoring purposes. The system does not record any personal information of the attendees nor supports contact tracing.

The COVID-19 pandemic imposed various restrictions on the accessibility of conventional teaching laboratories. Enabling learning and experimenting at home became necessary to support the practical element of students’ learning. Unfortunately, it is not viable to provide or share a fully featured sensor lab to every student because of the prohibitive costs involved. Therefore, repurposing electronic devices that are common to students can bring about the sought-after practical learning experience without the hefty price tag. In distinction to the conventional lab instruments though, consumer grade devices are not designed for use with external sensors and/or electronic circuitry. They are not professionally maintained, do not undergo periodic safety tests and are not calibrated. Nevertheless, nearly all modern computers, laptops, tablets or smartphones are equipped with high quality audio inputs and outputs that can generate and record signals in the audible frequency range (20 Hz – 20 kHz). Despite cutting off the direct currents completely, this range might be sufficient for working with a variety of sensors. In this presentation we look at the possibilities of making sure that such repurposing by design prevents any potential harm to the learner and to her or his personal equipment. These features seem essential for unsupervised alone experimenting and avoiding damage to expensive devices.

Magnetic Drug Targeting is a promising alternative for cancer treatment that offers the possibility to increase the efficiency of the therapy, while side-effects for patients get reduced. Thereby, the cancer-drug is bounded to magnetic nanoparticles. These particles are injected into a vessel and guided into the tumorous tissue by an external magnetic field outside the body. However, the efficiency of the therapy strongly depends on the performance of this navigation process, which is influenced by several multiphysical parameters including the properties of the nanoparticles, the volume flux, and especially the external applied magnetic field. To investigate these effects, the propagation of particle packets in a vessel with a 45° intersection is modeled in COMSOL Multiphysics®. The particles were distributed according to the density of a parabolic velocity profile. To systematically analyze the influence of the external magnetic field, the magnetic field of a cylindrical rare earth magnet with different ratios of diameter to length and axial plus radial magnetization was evaluated. The magnets were placed shortly before the intersection and for every magnet, the transmission probability for the two paths (direct and deflection) was evaluated. Furthermore, the probability for a particle gets trapped by the magnet and stop at the wall of the vessel, was investigated. The results for the particle steering show that both, the diameter to length ratio and the magnetization, strongly influence the steering of the particles. Overall, the magnets with an axial magnetization have a higher impact on the propagation path than the radial magnetized ones. However, when a single permanent magnet is used, the results depict that it is a narrow ridge between deflecting a particle or trap it at the vessel wall.

The use of wireless sensor networks (WSN) to address and improve the environmental quality of the built environment is gaining more and more prominence in modern cities. In this scope, our work aims to assess the spatial variability of local climate in relation to the urban morphology and the distribution of materials and vegetation. Furthermore, on-site measured data have been exploited to run and benchmark numerical models for the simulation and visualization of multiple climate parameters.

To study the long-term micro-scale relation between built environment characteristics and environmental parameters, a LoRaWAN based WSN has been deployed in Bolzano (Italy). 17 autonomous sensors of temperature and relative humidity have been installed in a star topology at pedestrian level in different locations of an urban district.

The analysis of the results highlightings the distribution in terms of air temperature and relative humidity and its correlation with the characteristics of the urban fabric. Collected data (time series) have been used to calibrate the numerical models to extend the investigation to a wider spatial context, including the surroundings of the monitored area, as well as to evaluate local human thermal comfort conditions. Moreover, by means of long-lasting experimental campaign, the reliability and potential hw/sf drawbacks of the low-cost WSN have been assessed.

The work carried out so far opens up to many further developments. The data management flow has been designed according to interoperability principles, which make the collected information possibly available to any decision-support systems for the benefit of planners and policymakers. In addition, the open-source nature of the network allows for a sustainable scaling-up, along with the chance to integrate it with co-creation and citizen science initiatives. Finally, the simulation of the microclimatic conditions can be exploited to address sheat island intensity reduction strategies in extensive urban areas.

Wireless capsule endoscopy is a promising medical application and a potential alternative to conventional endoscopy. A small capsule with an integrated camera for recording a video is swallowed by a patient allowing gastrointestinal diagnosis. It is of particular interest for doctors that a certain video frame is correlated to the precise location of the capsule within the gastrointestinal tract. Static magnetic localization is well-established for that purpose and the localization method is based on the magnetic dipole model. Generally, the dipole model is only valid for sufficiently large distances from the magnet. In this paper, simulations in which the magnetic flux density generated by different-sized permanent magnets and different sized computational domains was compared to the magnetic dipole model by simulations in COMSOL Multiphysics ®. The computational domain dimensions, as well as the ratio of the length and diameter of the magnet, were optimized to fit the magnetic flux density generated by the magnet with the dipole mode. The distance from the magnet, for which the dipole model is sufficiently accurate, was determined. Subsequently, the standard static magnetic localization method was applied to the proposed empirical data-based localization setup with different parameters. The results revealed, that the localization performance was significantly improved by applying the optimized parameters.

To reduce the impact on climate change, many countries developed strategies for the building sector with a goal to reduce the energy demands and carbon emission of buildings. As most buildings that exist today, will very likely exist in foreseeable future, many buildings will need to undergo major renovations. One of the most important parameters in determining the transmission heat losses through the building envelope is the U-value, i.e. thermal transmittance, and it is simply the rate of heat transfer per unit temperature. Since the U-value is one of the most important parameters regarding building energy performance, envelope elements that do not perform well in terms of transmission heat losses must undergo the renovation processes. The in-situ U-value of building elements is usually determined by the Heat Flux Method (HFM). One of the issues of current application of the HFM is the measurement duration. This paper explores the possibilities of reducing the measurement time by predicting the heat flux rate using a multilayer perceptron (MLP), a class of artificial neural network. The MLP uses two input layers that are the interior and exterior air temperatures, and the output layer that is the predicted heat flux rate. The predicted value is trained by comparing the predicted heat flux rates with the measured values, and by rearranging the neural network parameters (weights and biases) in corresponding neurons by minimizing the mean squared error defined for trained values (measured versus predicted heat flux rates). The use of MLP shows promising results for predicting the heat flux rates for the analyzed cases (4 days HFM results) when the training is performed on 2/3, or 1/2, of the overall measurement time. The application of the MLP could be in reducing the in-situ measurement time when determining heat losses through building elements in shorter time periods.

Circulating tumor cells (CTCs) are a rare subgroup of cells that detach from the primary solid tumor and circulate in the bloodstream of cancer patients. These cells act as a seed for metastases; moreover, they maintain the primary tumor heterogeneity and mimic tumor properties. All these features make CTCs good candidates to be used as clinical biomarker for patient diagnosis, prognosis and treatment. Indeed, the isolation and characterization of CTCs can smooth the way to apply precision medicine approach in cancer patients. However, the current methods to detect CTCs are mainly based on the recognition of epithelial cell adhesion molecule (EpCAM) expressed on cancer cell membrane, but they typically fail to detect tumoral cells that are losing epithelial phenotype and starting invasion/metastatic process. Here, we used deuterium as vibrational tag to develop a new CTCs detection method based on Raman spectroscopy potentially able to detect any type of CTCs. In particular, we exploited the capacity of cancer cell to internalize and metabolize glucose 5-10 faster than normal cell, the so-called Warburg effect. Normal prostatic cells (PNT2), cancer prostatic (PC3) and hepatic (HepG2) cells were used as in vitro model. Cells were cultured in presence of 25mM deuterated glucose for 48h and then analyzed by Raman spectroscopy. The typical deuterium Raman band at 2100 cm^-1 was present in the spectra of PC3 and HepG2 cells, but not in PNT2 spectra. These results indicate the presence of Warburg effect in our cellular model and that cancer cells can be differentiated from normal cells following glucose metabolism. To simulate the presence of CTCs in blood, PC3 and HepG2 cells were co-cultured with white blood cells isolated from healthy donor blood in presence of deuterated glucose. The deuterium Raman signal was observable only in the spectra of PC3 and HepG2 cells. Our data demonstrate that cancer cells can be distinguished from healthy cells independently from EpCAM expression just exploiting the glycolytic metabolism also when they are in the same media. These results shed a light on the possibility to develop new CTCs detecting methods using label-free approach based on Raman spectroscopy.

In the scenario of massive urbanization and global climate change, the acquisition of microclimatic data in urban areas plays a key role for responsive adaptation and mitigation strategies. The enrichment of kinematic sensor data with precise, high-frequency and robust positioning directly relates to the possibility of creating added-value services devoted to improving life-quality of urban communities. This work presents a low-cost cloud-connected mobile monitoring platform for multiple environmental parameters and their spatial variation in the urban context.

The hardware consists of a U-blox C099-F9P GNSS receiver, coupled with a Raspberry Pi 4 through the serial port. This way, we expanded the connection buses, connectivity protocols and onboard computation capabilities of the prototype. Furthermore, the open-source suite RTKLIB has been installed on NOOBS to enable U-blox proprietary data format to RINEX v3.13 conversion. The GNSS processing chain evaluates three different positional solutions: single point, static point with differential corrections (DCs), and kinematic with DCs. DCs have been achieved by means of a certified base station (downloading the RINEX files from a public service). In addition, GFZRNX toolbox is encompassed within the routine to improve the GNSS data quality.

To compare the configurations under test, the variance of northing, easting, and height parameters has been evaluated for every single positional solution. The kinematics one has proven to be the best method for mobile applications, especially when data quality advancement is applied through GFZRNX (supported by Levene tests). The performance of GFZRNX in reducing the number of floating solutions has been investigated, as well.

The achieved results confirm the approach is suitable for urban monitoring mobile networks, where several nodes can be installed, for instance, on public transport vehicles (commercial average speed below 40 km/h) to expand the spatio-temporal resolution (centimetre-level accuracy in seconds) of environmental data, while keeping low the number of sensors to deploy. The outlook is the assessment of statistic correlations between local microclimatic conditions, urban surface usage and urban morphology to aid the development of sustainable energy and climate action plans at the municipality level.

Data acquisition systems are one of the main components were sensors and remote monitoring strategies are required in a real process. Normally, data acquisition is performed through commercial solutions that are adaptable to a specific solution, and expansion capabilities are associated with the products (HW/SW) of the same company, which results in limited possibilities of expansion. As a contribution to solving this problem, a hardware development project with embedded systems and focused on the Internet of Things was designed, to propose a data acquisition system which is validated through a real application using the prototype built, monitoring variables in a photovoltaic system such as voltage and current to analyze the behavior of the solar panels. Testing and evaluation of the prototype are carried out by several experiments, where the most common failures of a photovoltaic plant were emulated, finding that the recorded data provides the necessary information to identify the moments in which the system being monitored presents problems. In this way, it was found that the developed system can be used as a remote monitoring system since the information that the device takes through the current and voltage sensors can be sent to a server through an Internet connection for data processing, graph generation, or statistical analysis according to the requirements. These features allow a friendly presentation of the data to an end-user.