Proximity Sensor for Measuring Social Interaction in a School Environment.

Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in recent years by advances in applied technology. This paper presents the design and development of a novel device composed of several sensors. Specifically, we propose a proximity sensor integrated by three devices: a Bluetooth sensor, a global positioning system (GPS) unit and an accelerometer. By means of this sensor it is possible to detect the presence of neighboring sensors in various configurations and operating conditions. Profiles based on the Received Signal Strength Indicator (RSSI) exhibit behavior consistent with that reported by empirical relationships. The present sensor is functional in detecting the proximity of other sensors and is thus useful for the identification of interactions between people in relevant contexts such as schools.

Novel Discrete and Imprinted Fluoride-Selective Sensors: Bridging the Gap from DMSO to Aqueous Samples.

Fluoride in drinking water has beneficial or harmful health effects depending on its concentration. This highlights the need for new low-cost and portable sensors capable of in situ monitoring of F- ions. Unfortunately, achieving high levels of water compatibility and fluoride specificity remains a challenge. Here, four new urea-based discrete sensors are prepared and characterized. The sensors containing anthracenyl- (5) and 9H-fluorenyl- (7) signaling units exhibit intense luminescent emissions in dimethyl sulfoxide, the former being particularly sensitive and selective to fluoride. In water, 5 displays a superior sensitivity (871 M-1) and a detection limit (8 µm) below international guidelines, albeit with cross-sensitivity to H2PO4‾. To enhance the performance, 5 and 7 are embedded into a fluoride-imprinted polymeric matrix to give solid-state sensors (5P and 7P, respectively). 5P shows good sensitivity (360 M-1) and specificity in water. Besides, it has a low detection limit (35 µm) and a response linear range (118-6300 µm) encompassing the limit established by the Environmental Protection Agency (211 µm). Furthermore, 5P also displays good reusability and adequate recovery values in real-sample testing (102 ± 2%), constituting the first example of a low-cost anion-imprinted polymeric probe tailored for the selective sensing of fluoride in aqueous samples.

Coastal Dune Vegetation Dynamism and Anthropogenic-Induced Transitions in the Mexican Caribbean during the Last Decade.

In the Mexican Caribbean, environmental changes, hydrometeorological events, and anthropogenic activities promote dynamism in the coastal vegetation cover associated with the dune; however, their pace and magnitude remain uncertain. Using Landsat 7 imagery, spatial and temporal changes in coastal dune vegetation were estimated for the 2011-2020 period in the Sian Ka'an Biosphere Reserve. The SAVI index revealed cover changes at different magnitudes and paces at the biannual, seasonal, and monthly timeframes. Climatic seasons had a significant influence on vegetation cover, with increases in cover during northerlies (SAVI: p = 0.000), while the topographic profile of the dune was relevant for structure. Distance-based multiple regressions and redundancy analysis showed that temperature had a significant effect (p < 0.05) on SAVI patterns, whereas precipitation showed little influence (p > 0.05). The Mann-Kendall tendency test indicated high dynamism in vegetation loss and recovery with no defined patterns, mostly associated with anthropogenic disturbance. High-density vegetation such as mangroves, palm trees, and shrubs was the most drastically affected, although a reduction in bare soil was also recorded. This study demonstrated that hydrometeorological events and climate variability in the long term have little influence on vegetation dynamism. Lastly, it was observed that anthropogenic activities promoted vegetation loss and transitions; however, the latter were also linked to recoveries in areas with pristine environments, relevant for tourism.

Hydroelectric Plant Safety: Real-Time Monitoring Utilizing Fiber-Optic Sensors.

In the context of hydroelectric plants, this article emphasizes the imperative of robust monitoring strategies. The utilization of fiber-optic sensors (FOSs) emerges as a promising approach due to their efficient optical transmission, minimal signal attenuation, and resistance to electromagnetic interference. These optical sensors have demonstrated success in diverse structures, including bridges and nuclear plants, especially in challenging environments. This article culminates with the depiction of the development of an array of sensors featuring Fiber Bragg Gratings (FBGs). This array is designed to measure deformation and temperature in protective grids surrounding the turbines at the Santo Antônio Hydroelectric Plant. Implemented in a real-world scenario, the device identifies deformation peaks, indicative of water flow obstructions, thereby contributing significantly to the safety and operational efficiency of the plant.

Development of a Device for Monitoring Erosion in the Field.

Monitoring erosion is an important part of understanding the causes of this geotechnical and geological phenomenon. In order to monitor them, it is necessary to develop equipment that is sophisticated enough to resist the sun and water without damage, that is self-mechanized, and that can support the amount of data collected. This article introduces a rain-triggered field erosion monitoring device composed of three main modules: control, capture, and sensing. The control module comprises both hardware and firmware with embedded software. The capture module integrates a camera for recording, while the sensing module includes rain sensors. By filming experimental soil samples under simulated rain events, the device demonstrated satisfactory performance in terms of activation and deactivation programming times, daytime image quality without artificial lighting, and equipment protection. The great differences about this monitoring device are its ease of use, low cost, and the quality it offers. These results suggest its potential effectiveness in capturing the progression of field erosive processes.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification.

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.

Mobile Robot + IoT: Project of Sustainable Technology for Sanitizing Broiler Poultry Litter.

The traditional aviary decontamination process involves farmers applying pesticides to the aviary's ground. These agricultural defenses are easily dispersed in the air, making the farmers susceptible to chronic diseases related to recurrent exposure. Industry 5.0 raises new pillars of research and innovation in transitioning to more sustainable, human-centric, and resilient companies. Based on these concepts, this paper presents a new aviary decontamination process that uses IoT and a robotic platform coupled with ozonizer (O3) and ultraviolet light (UVL). These clean technologies can successfully decontaminate poultry farms against pathogenic microorganisms, insects, and mites. Also, they can degrade toxic compounds used to control living organisms. This new decontamination process uses physicochemical information from the poultry litter through sensors installed in the environment, which allows accurate and safe disinfection. Different experimental tests were conducted to construct the system. First, tests related to measuring soil moisture, temperature, and pH were carried out, establishing the range of use and the confidence interval of the measurements. The robot's navigation uses a back-and-forth motion that parallels the aviary's longest side because it reduces the number of turns, reducing energy consumption. This task becomes more accessible because of the aviaries' standardized geometry. Furthermore, the prototype was tested in a real aviary to confirm the innovation, safety, and effectiveness of the proposal. Tests have shown that the UV + ozone combination is sufficient to disinfect this environment.

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment.

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

Soft Sensory-Motor System Based on Ionic Solution for Robotic Applications.

Soft robots claim the architecture of actuators, sensors, and computation demands with their soft bodies by obtaining fast responses and adapting to the environment. Sensory-motor coordination is one of the main design principles utilized for soft robots because it allows the capability to sense and actuate mutually in the environment, thereby achieving rapid response performance. This work intends to study the response for a system that presents coupled actuation and sensing functions simultaneously and is integrated in an arbitrary elastic structure with ionic conduction elements, called as soft sensory-motor system based on ionic solution (SSMS-IS). This study provides a comparative analysis of the performance of SSMS-IS prototypes with three diverse designs: toroidal, semi-toroidal, and rectangular geometries, based on a series of performance experiments, such as sensitivity, drift, and durability. The design with the best performance was the rectangular SSMS-IS using silicon rubber RPRO20 for both internal and external pressures applied in the system. Moreover, this work explores the performance of a bioinspired soft robot using rectangular SSMS-IS elements integrated in its body. Further, it investigated the feasibility of the robot to adapt its morphology online for environment variability, responding to external stimuli from the environment with different levels of stiffness and damping.

Dependence of n-Type Metal-Oxide Gas Sensor Response on the Pressure of Oxygen and Reducing Gases.

The adsorption of oxygen and its reaction with target gases are the basis of the gas detection mechanism by using metal oxides. Here, we present a theoretical analysis of the sensor response, within the ionosorption model, for an n-type polycrystalline semiconductor. Our goal of our work is to reveal the mechanisms of gas sensing from a fundamental point of view. We revisit the existing models in which the sensor response presents a power-law behavior with a reducing gas partial pressure. Then, we show, based on the Wolkenstein theory of chemisorption, that the sensor response depends not only on the reducing gas partial pressure but also on the oxygen partial pressure. We also find that the obtained sensor response does not explicitly depend on the grain size, and if it does, it is exclusively through the rate constants related to the involved reactions.

Challenges faced with 3D-printed electrochemical sensors in analytical applications.

Prototyping analytical devices with three-dimensional (3D) printing techniques is becoming common in research laboratories. The attractiveness is associated with printers' price reduction and the possibility of creating customized objects that could form complete analytical systems. Even though 3D printing enables the rapid fabrication of electrochemical sensors, its wider adoption by research laboratories is hindered by the lack of reference material and the high "entry barrier" to the field, manifested by the need to learn how to use 3D design software and operate the printers. This review article provides insights into fused deposition modeling 3D printing, discussing key challenges in producing electrochemical sensors using currently available extrusion tools, which include desktop 3D printers and 3D printing pens. Further, we discuss the electrode processing steps, including designing, printing conditions, and post-treatment steps. Finally, this work shed some light on the current applications of such electrochemical devices that can be a reference material for new research involving 3D printing.

Mesoporous Nitrogen-Doped Holey Reduced Graphene Oxide: Preparation, Purification, and Application for Metal-Free Electrochemical Sensing of Dopamine.

Holey graphenic nanomaterials with porosity within the basal plane attract significant interest. It is observed that the perforation of graphene can enhance the specific surface area of the nanosheet, ensuring effective wetting and penetration of electrolytes to the electrode surface, facilitating rapid charge transfer, and boosting the electrocatalytic efficacy of the transducers. This study reports the first example of nitrogen-doped holey reduced graphene oxide with a mesoporous morphology of the graphene basal plane (N-MHG). It is shown that N-MHG can be synthesized through a one-step hydrothermal treatment of GO using NH3 and H2O2. A straightforward procedure for the purification of N-MHG has also been developed. AFM, TEM, and Raman analyses have revealed that N-MHG possesses a highly mesoporous network structure with a pore size ranging from 10 to 50 nm. X-ray photoelectron spectroscopy data have indicated a partial reduction of the graphene oxide sheets during the etching process but also show a 3-5 times higher content of C═O and O-C═O fragments compared to rGO. This could account for the remarkable stability of the N-MHG aqueous suspension. An electrochemical sensor for dopamine analysis is assembled on a glassy carbon electrode with N-MHG/Nafion membrane and characterized by cyclic voltammetry and electrochemical impedance spectroscopy.

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

A Novel Experimental Apparatus for Characterizing Flow Regime in Mechanically Stirred Tanks through Force Sensors.

Pressure fluctuations in a mixing tank can provide valuable information about the existing flow regime within the tank, which in turn influences the degree of mixing that can be achieved. In the present work, we propose a prototype for identifying the flow regime in mechanically stirred tanks equipped with four vertical baffles through the characterization of pressure fluctuations. Our innovative proposal is based on force sensors strategically placed in the baffles of the mixing tank. The signals coming from the sensors are transmitted to an electronic module based on an Arduino UNO development board. In the electronic module, the pressure signals are conditioned, amplified and sent via Bluetooth to a computer. In the computer, the signals can be plotted or stored in an Excel file. In addition, the proposed system includes a moving average filtering and a hierarchical bottom-up clustering analysis that can determine the real-time flow regime (i.e., the Reynolds number, Re) in which the tank was operated during the mixing process. Finally, to demonstrate the versatility of the proposed prototype, experiments were conducted to identify the Reynolds number for different flow regimes (static, laminar, transition and turbulent), i.e., 0≤Re≤ 42,955. Obtained results were in agreement with the prevailing consensus on the onset and developed from different flow regimes in mechanically stirred tanks.

Planar Printed Structures Based on Matryoshka Geometries: A Review.

A review on planar printed structures that are based on Matryoshka-like geometries is presented. These structures use the well-known principle of Matryoshka dolls that are successively nested inside each other. The well-known advantages of the planar printed technology and of the meandered nested Matryoshka geometries are combined to generate miniaturized, multi-resonance, and/or wideband configurations. Both metal and complementary slot structures are considered. Closed and open configurations were analyzed. The working principles were explored in order to obtain physical insight into their behavior. Low-cost and single-layer applications as frequency-selective surfaces, filters, antennas, and sensors, in the microwave frequency region, were reviewed. Potential future research perspectives and new applications are then discussed.

Palladium Phthalocyanine Nanowire-Based Highly Sensitive Sensors for NO2(g) Detection.

Palladium phthalocyanine (PdPc) nanowires (NWs) were developed to achieve the gas sensing of NO2 in the sub-parts-per-million (ppm) range. Non-substituted metal phthalocyanine are well known for their p-type semiconducting behavior, which is responsible for its gas-sensing capabilities. Nanofabrication of the PdPc NWs was performed by physical vapor deposition (PVD) on an interdigitated gold electrode (IDE). The coordination of palladium in the structure was confirmed with UV-Vis spectroscopy. Gas-sensing experiments for NO2 detection were undertaken at different sensed gas concentrations from 4 ppm to 0.5 ppm at room temperature. In this work, the responses at different gas concentrations are reported. In addition, structural studies of the PdPc NWs with scanning electron microscopy (SEM) and electron-dispersive X-ray diffraction (EDS) are shown.

Chemically Functionalized 2D Transition Metal Dichalcogenides for Sensors.

The goal of the sensor industry is to develop innovative, energy-efficient, and reliable devices to detect molecules relevant to economically important sectors such as clinical diagnoses, environmental monitoring, food safety, and wearables. The current demand for portable, fast, sensitive, and high-throughput platforms to detect a plethora of new analytes is continuously increasing. The 2D transition metal dichalcogenides (2D-TMDs) are excellent candidates to fully meet the stringent demands in the sensor industry; 2D-TMDs properties, such as atomic thickness, large surface area, and tailored electrical conductivity, match those descriptions of active sensor materials. However, the detection capability of 2D-TMDs is limited by their intrinsic tendency to aggregate and settle, which reduces the surface area available for detection, in addition to the weak interactions that pristine 2D-TMDs normally exhibit with analytes. Chemical functionalization has been proposed as a consensus solution to these limitations. Tailored surface modification of 2D-TMDs, either by covalent functionalization, non-covalent functionalization, or a mixture of both, allows for improved specificity of the surface-analyte interaction while reducing van der Waals forces between 2D-TMDs avoiding agglomeration and precipitation. From this perspective, we review the recent advances in improving the detection of biomolecules, heavy metals, and gases using chemically functionalized 2D-TMDs. Covalent and non-covalent functionalized 2D-TMDs are commonly used for the detection of biomolecules and metals, while 2D-TMDs functionalized with metal nanoparticles are used for gas and Raman sensors. Finally, we describe the limitations and further strategies that might pave the way for miniaturized, flexible, smart, and low-cost sensing devices.

Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.

This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.

High-resolution X-Ray imaging of small animal samples based on Commercial-Off-The-Shelf CMOS image sensors.

 An automated system for acquiring microscopic-resolution radiographic images of biological samples was developed. Mass-produced, low-cost, and easily automated components were used, such as Commercial-Off-The-Self CMOS image sensors (CIS), stepper motors, and control boards based on Arduino and RaspberryPi. System configuration, imaging protocols, and Image processing (filtering and stitching) were defined to obtain high-resolution images and for successful computational image reconstruction. Radiographic images were obtained for animal samples including the widely used animal models zebrafish (Danio rerio) and the fruit-fly (Drosophila melanogaster), as well as other small animal samples. The use of phosphotungstic acid (PTA) as a contrast agent was also studied. Radiographic images with resolutions of up to (7±0.6)µm were obtained, making this system comparable to commercial ones. This work constitutes a starting point for the development of more complex systems such as X-ray attenuation micro-tomography systems based on low-cost off-the-shelf technology. It will also bring the possibility to expand the studies that can be carried out with small animal models at many institutions (mostly those working on tight budgets), particularly those on the effects of ionizing radiation and absorption of heavy metal contaminants in animal tissues.