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Despite being banned by the European Union (EU), Atrazine, a synthetic herbicide, was found in Europe's coastal waters. Given the widespread ban on atrazine in many countries, it is imperative to perform screenings to detect its presence in food and water samples, as it poses significant risks to both human health and aquatic ecosystems. This study presents the development of an innovative, simple, and cost‐effective electrochemical sensing platform that comprises a portable system equipped with a poly‐8‐Amino‐1‐naphthol‐3,6‐disulfonic acid (8‐ANDSA) customized carbon paste electrode (CPE). The poly‐8 ANDSA/CPE was acquired using the process of electrochemical polymerization, utilizing cyclic voltammetry (CV) over the potential domain of −0.8 V to 1.8 V for 25 sweep cycles. The poly‐8‐ANDSA/CPE exhibited a linear relationship between the square wave voltammetric (SWV) peak current amplitude and ATZ concentration within 0.1–100 μM. The limit of detection for ATZ was determined to be 30 nM. Spiked recovery results of 97.71 % to 102.73 % with RSD (%) values less than 5 % validated the method's applicability for atrazine detection from water samples. The favorable outcomes indicated that the portable system performs exceptionally well and validates the practicality of ATZ detection, facilitating on‐site detection in actual water samples. [ABSTRACT FROM AUTHOR]

Dielectric spectroscopy in the sub-THz regime is a promising candidate for microfluidic-based analysis of biological cells and bio-molecules, since multiple vibrational and rotational transition energy levels exist in this frequency range (P. Siegel, "Terahertz technology in biology and medicine," IEEE Trans. Microw. Theor. Tech., vol. 52, pp. 2438–2447, 2004). This article presents our recent efforts in the implementation of microfluidic channel networks with silicon-based technologies to unleash the potential of an integrated sub-THz microfluidic sensor platform. Various aspects of dielectric sensors, readout systems, flowmeter design as well as implemention- and technology-related questions are addressed. Three dielectric sensor systems are presented operating at 240 GHz realizing transmission-based, reflection-based and full two-port architectures. Furthermore different silicon based microchannel integration techniques are discussed as well as a novel copper pillar-based PCB microchannel method is proposed and successfully demonstrated. [ABSTRACT FROM AUTHOR]

• Flexible system to monitor different physical magnitudes in marine environments. • Stand-alone profiler for several weeks deployment. • IoT technologies allowing real-time collection of profiles of reliable data. • Low-cost, small and reproducible mechanical design underwater profiler. • Remote configuration ability for flexible sensor sampling. Marine pollution is a complex issue that has prompted extensive research. The Mar Menor lagoon, a singular Mediterranean ecological feature, is under pressure from various economic activities, leading to episodes of hypoxia and fish mass mortality. The lagoon is covered by an extensive meadow of submerged vegetation that release oxygen during daylight hours but consumes it during the night. Under exceptional conditions, the water column becomes stratified, with higher oxygen consumption in the deeper layers due to organic matter decomposition. Therefore, continuous high-frequency monitoring of critical parameters affecting the metabolic behaviour of the lagoon is essential. This marine environment has traditionally been monitored with traditional techniques from vessels on a weekly basis, incurring in high costs and yielding limited results. This article introduces the s-Nautilus, a submersible profiler that enables near real-time data transmission in shallow marine environments. It showcases an operational autonomy of two months and significantly reduces installation costs by eliminating the need for on-site infrastructure and personnel. Weighing 21 kg, makes it easy to transport, and it can be equipped with different commercial sensors to monitor various parameters as required. Currently, several s-Nautilus profilers are deployed in the Mar Menor lagoon, collecting data during critical hours, and providing valuable information to the managers of this emblematic environment. [ABSTRACT FROM AUTHOR]

Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This work proposes an alternative cadence-measurement system with less of the mentioned restrictions, without the need for distinct cadence-measurement apparatus attached to the pedal and shaft of the road bicycle. The proposed design applies an inertial measurement unit (IMU) to the seating pole of the bike. In an experiment, the motion data were gathered. A total of four different road cyclists participated in this study to collect different datasets for neural network training and evaluation. In total, over 10 h of road cycling data were recorded and used to train the neural network. The network's aim was to detect each revolution of the crank within the data. The evaluation of the data has shown that using pure accelerometer data from all three axes led to the best result in combination with the proposed network architecture. A working proof of concept was achieved with an accuracy of approximately 95% on test data. As the proof of concept can also be seen as a new method for measuring cadence, the method was compared with the ground truth. Comparing the ground truth and the predicted cadence, it can be stated that for the relevant range of 50 rpm and above, the prediction over-predicts the cadence with approximately 0.9 rpm with a standard deviation of 2.05 rpm. The results indicate that the proposed design is fully functioning and can be seen as an alternative method to detect the cadence of a road cyclist. [ABSTRACT FROM AUTHOR]

Flexible electronics is a new paradigm with strong implications from healthcare to energy applications. In this context, electrically conductive polymers are the critical components. Here, we report the design, formation mechanism, and applications of a polymer nanocomposite obtained by single-step laser integration of functionalized graphene into a polymer matrix. Laser processing manipulates the physical-chemical properties of this nanocomposite in a controlled and straightforward way, tuning the electrical resistance from a dielectric (MΩ sq−1) to a highly conductive material (Ω sq−1). We combine experimental and computational approaches to elucidate graphene nanocomposite's nature and formation mechanism, evidencing different processes from photothermal polymer melting to shock wave mixing in a liquid phase within a millisecond time scale. We exploit these fundamental insights on the graphene/polymer nanocomposite in the design and fabrication of electrochemical sensing and antenna devices, showing the potential for healthcare and the Internet of Things. [Display omitted] [ABSTRACT FROM AUTHOR]

Most commercial cadence-measurement systems in road cycling are strictly limited in their function to the measurement of cadence. Other relevant signals, such as roll angle, inclination or a round kick evaluation, cannot be measured with them. This work proposes an alternative cadence-measurement system with less of the mentioned restrictions, without the need for distinct cadence-measurement apparatus attached to the pedal and shaft of the road bicycle. The proposed design applies an inertial measurement unit (IMU) to the seating pole of the bike. In an experiment, the motion data were gathered. A total of four different road cyclists participated in this study to collect different datasets for neural network training and evaluation. In total, over 10 h of road cycling data were recorded and used to train the neural network. The network’s aim was to detect each revolution of the crank within the data. The evaluation of the data has shown that using pure accelerometer data from all three axes led to the best result in combination with the proposed network architecture. A working proof of concept was achieved with an accuracy of approximately 95% on test data. As the proof of concept can also be seen as a new method for measuring cadence, the method was compared with the ground truth. Comparing the ground truth and the predicted cadence, it can be stated that for the relevant range of 50 rpm and above, the prediction over-predicts the cadence with approximately 0.9 rpm with a standard deviation of 2.05 rpm. The results indicate that the proposed design is fully functioning and can be seen as an alternative method to detect the cadence of a road cyclist.

Avocado and tomato production are key agricultural sectors for many economies including South Africa. The quality of avocadoes and tomatoes that reach consumers is directly dependent on the handling at the postharvest stage. The fruit undergoes an extensive journey, and subsequently stress, comprised of several steps, including harvesting, application of postharvest treatments, packaging and transportation by road and sea to reach the export markets. To date, the quantification of these external stresses on a discrete fruit has not been measured in detail, primarily due to a lack of compatible instrumentation. This paper demonstrates a low cost, stand-alone, open source data acquisition system, termed smAvo and smaTo, that can be introduced effortlessly to monitor agricultural processing facilities and transportation networks. The miniaturised Arduino sensor platform is enclosed within a waterproof enclosure and surrounded by a 3D printed shell manufactured from morphologically compatible materials. The software is customisable to the needs of the research project or individual transportation phases under study, providing both extensive environmental data and high-frequency tri-axis acceleration measurements that are crucial to understanding the dynamic processes that directly affect the final quality of the fruit. Four successful field trials demonstrate the fidelity of both the smAvo and smaTo platforms.

Monitoring climate change, and its impacts on ecological, agricultural, and other societal systems, is often based on temperature data derived from official weather stations. Yet, these data do not capture most microclimates, influenced by soil, vegetation and topography, operating at spatial scales relevant to the majority of organisms on Earth. Detecting and attributing climate change impacts with confidence and certainty will only be possible by a better quantification of temperature changes in forests, croplands, mountains, shrublands, and other remote habitats. There is an urgent need for a novel, miniature and simple device filling the gap between low-cost devices with manual data download (no instantaneous data) and high-end, expensive weather stations with real-time data access. Here, we develop an integrative real-time monitoring system for microclimate measurements: MIRRA (Microclimate Instrument for Real-time Remote Applications) to tackle this problem. The goal of this platform is the design of a miniature and simple instrument for near instantaneous, long-term and remote measurements of microclimates. To that end, we optimised power consumption and transfer data using a cellular uplink. MIRRA is modular, enabling the use of different sensors (e.g., air and soil temperature, soil moisture and radiation) depending upon the application, and uses an innovative node system highly suitable for remote locations. Data from separate sensor modules are wirelessly sent to a gateway, thus avoiding the drawbacks of cables. With this sensor technology for the long-term, low-cost, real-time and remote sensing of microclimates, we lay the foundation and open a wide range of possibilities to map microclimates in different ecosystems, feeding a next generation of models. MIRRA is, however, not limited to microclimate monitoring thanks to its modular and wireless design. Within limits, it is suitable or any application requiring real-time data logging of power-efficient sensors over long periods of time. We compare the performance of this system to a reference system in real-world conditions in the field, indicating excellent correlation with data collected by established data loggers. This proof-of-concept forms an important foundation to creating the next version of MIRRA, fit for large scale deployment and possible commercialisation. In conclusion, we developed a novel wireless cost-effective sensor system for microclimates.

Highly efficient training is a must in professional sports. Presently, this means doing exercises in high number and quality with some sort of data logging. In American football many things are logged, but there is no wearable sensor that logs a catch or a drop. Therefore, the goal of this paper was to develop and verify a sensor that is able to do exactly that. In a first step a sensor platform was used to gather nine degrees of freedom motion and audio data of both hands in 759 attempts to catch a pass. After preprocessing, the gathered data was used to train a neural network to classify all attempts, resulting in a classification accuracy of 93%. Additionally, the significance of each sensor signal was analysed. It turned out that the network relies most on acceleration and magnetometer data, neglecting most of the audio and gyroscope data. Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.

Wireless Sensor Networks constitute pervasive and distributed computing systems and are potentially one of the most important technologies of this century. They have been specifically identified as a good candidate to become an integral part of the protection of critical infrastructures. In this paper we focus on railway infrastructure protection and we present the details of a sensor platform designed to be integrated into a slab track system in order to carry out both installation and maintenance monitoring activities. In the installation phase, the platform helps operators to install the slab tracks in the right position. In the maintenance phase, the platform collects information about the structural health and behavior of the infrastructure when a train travels along it and relays the readings to a base station. The base station uses trains as data mules to upload the information to the internet. The use of a train as a data mule is especially suitable for collecting information from remote or inaccessible places which do not have a direct connection to the internet and require less network infrastructure. The overall aim of the system is to deploy a permanent economically viable monitoring system to improve the safety of railway infrastructures.

The main purpose of the work was to analyze sensory platform solutions for use on the Internet of Things. Emphasis was placed on the literature study on Sensor Platforms, Internet of Things, Bluetooth Low Energy Communication Protocol, serial digital and analog interfaces most commonly used in sensory platforms. Analysis of sensory platform solutions was carried out in terms of their functionality and efficiency. The SensorTag CC2650 sensing platform by Texas Instruments, turned out to be the best and has been used to build the hub model. The hub model was based on hardware and software implementation, which resulted in the expansion of the sensor platform with 6 additional analog inputs and a Bluetooth Low Energy data transmission profile. Testing the correctness of the software produced in the laboratory environment has made it possible to determine the correct functioning of the concentrator model.