Low-Cost Optical Sensors for Soil Composition Monitoring.

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification.

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques.

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring.

The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.

Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture.

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.

Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs.

In irrigation ponds, the excess of nutrients can cause eutrophication, a massive growth of microscopic algae. It might cause different problems in the irrigation infrastructure and should be monitored. In this paper, we present a low-cost sensor based on optical absorption in order to determine the concentration of algae in irrigation ponds. The sensor is composed of 5 LEDs with different wavelengths and light-dependent resistances as photoreceptors. Data are gathered for the calibration of the prototype, including two turbidity sources, sediment and algae, including pure samples and mixed samples. Samples were measured at a different concentration from 15 mg/L to 4000 mg/L. Multiple regression models and artificial neural networks, with a training and validation phase, are compared as two alternative methods to classify the tested samples. Our results indicate that using multiple regression models, it is possible to estimate the concentration of alga with an average absolute error of 32.0 mg/L and an average relative error of 11.0%. On the other hand, it is possible to classify up to 100% of the samples in the validation phase with the artificial neural network. Thus, a novel prototype capable of distinguishing turbidity sources and two classification methodologies, which can be adapted to different node features, are proposed for the operation of the developed prototype.

Low-Cost System Based on Optical Sensor to Monitor Discharge of Industrial Oil in Irrigation Ditches.

Uncontrolled dumping linked to agricultural vehicles causes an increase in the incorporation of oils into the irrigation system. In this paper, we propose a system based on an optical sensor to monitor oil concentration in the irrigation ditches. Our prototype is based on the absorption and dispersion of light. As a light source, we use Light Emitting Diodes (LEDs) with different colours (white, yellow, blue, green, and red) and a photodetector as a sensing element. To test the sensor's performance, we incorporate industrial oils used by a diesel or gasoline engine, with a concentration from 0 to 0.20 mLoil/cm2. The experiment was carried out at different water column heights, 0 to 20 cm. According to our results, the sensor can differentiate between the presence or absence of diesel engine oil with any LED. For gasoline engine oil, the sensor quantifies its concentration using the red light source; concentrations greater than 0.1 mLoil/cm2 cannot be distinguished. The data gathered using the red LED has an average absolute error of 0.003 mLoil/cm2 (relative error of 15.8%) for the worst case, 15 cm. Finally, the blue LED generates different signals in the photodetector according to the type of oil. We developed an algorithm that combines (i) the white LED, to monitor the presence of oil; (ii) the blue LED, to identify if the oil comes from a gasoline or diesel engine; and (iii) the red LED, to monitor the concentration of oil used by a gasoline engine.

Evaluating the Effects of Environmental Conditions on Sensed Parameters for Green Areas Monitoring and Smart Irrigation Systems.

The irrigation of green areas in cities should be managed appropriately to ensure its sustainability. In large cities, not all green areas might be monitored simultaneously, and the data acquisition time can skew the gathered value. Our purpose is to evaluate which parameter has a lower hourly variation. We included soil parameters (soil temperature and moisture) and plant parameters (canopy temperature and vegetation indexes). Data were gathered at 5 different hours in 11 different experimental plots with variable irrigation and with different grass composition. The results indicate that soil moisture and Normalized Difference Vegetation Index are the sole parameters not affected by the data acquisition time. For soil moisture, the maximum difference was in experimental plot 4, with values of 21% at 10:45 AM and 27% at 8:45 AM. On the other hand, canopy temperature is the most affected parameter with a mean variation of 15 °C in the morning. The maximum variation was in experimental plot 8 with a 19 °C at 8:45 AM and 39 °C at 12:45 PM. Data acquisition time affected the correlation between soil moisture and canopy temperature. We can affirm that data acquisition time has to be included as a variability source. Finally, our conclusion indicates that it is vital to consider data acquisition time to ensure water distribution for irrigation in cities.

Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas.

Deploying wireless sensor networks (WSN) in rural environments such as agricultural fields may present some challenges that affect the communication between the nodes due to the vegetation. These challenges must be addressed when implementing precision agriculture (PA) systems that monitor the fields and estimate irrigation requirements with the gathered data. In this paper, different WSN deployment configurations for a soil monitoring PA system are studied to identify the effects of the rural environment on the signal and to identify the key aspects to consider when designing a PA wireless network. The PA system is described, providing the architecture, the node design, and the algorithm that determines the irrigation requirements. The testbed includes different types of vegetation and on-ground, near-ground, and above-ground ESP32 Wi-Fi node placements. The results of the testbed show high variability in densely vegetated areas. These results are analyzed to determine the theoretical maximum coverage for acceptable signal quality for each of the studied configurations. The best coverage was obtained for the near-ground deployment. Lastly, the aspects of the rural environment and the deployment that affect the signal such as node height, crop type, foliage density, or the form of irrigation are discussed.

Identification of Major Quantitative Trait Loci Controlling Field Resistance to Downy Mildew in Cultivated Lettuce (Lactuca sativa).

Lettuce downy mildew, caused by Bremia lactucae Regel, is the most economically important foliar disease of lettuce (Lactuca sativa L.). The deployment of resistant cultivars carrying dominant resistance genes (Dm genes) plays a crucial role in integrated downy mildew disease management; however, high variability in pathogen populations leads to the defeat of plant resistance conferred by Dm genes. Some lettuce cultivars exhibit field resistance that is only manifested in adult plants. Two populations of recombinant inbred lines (RILs), originating from crosses between the field resistant cultivars Grand Rapids and Iceberg and susceptible cultivars Salinas and PI491224, were evaluated for downy mildew resistance under field conditions. In all, 160 RILs from the Iceberg × PI491224 and 88 RILs from the Grand Rapids × Salinas populations were genotyped using genotyping by sequencing, which generated 906 and 746 high-quality markers, respectively, that were used for quantitative trait locus (QTL) analysis. We found a QTL in chromosome 4 that is present in both Grand Rapids × Salinas and Iceberg × PI491224 populations that has a major effect on field resistance. We also found two additional significant QTLs in chromosomes 2 and 5 in the Iceberg × PI491224 RIL population. Marker-assisted gene pyramiding of multiple Dm genes in combination with QTLs for field resistance provide the opportunity to develop cultivars with more durable resistance to B. lactucae.

Identification and mapping of new genes for resistance to downy mildew in lettuce.

KEY MESSAGE: Eleven new major resistance genes for lettuce downy mildew were introgressed from wild Lactuca species and mapped to small regions in the lettuce genome. Downy mildew, caused by the oomycete pathogen Bremia lactucae Regel, is the most important disease of lettuce (Lactuca sativa L.). The most effective method to control this disease is by using resistant cultivars expressing dominant resistance genes (Dm genes). In order to counter changes in pathogen virulence, multiple resistance genes have been introgressed from wild species by repeated backcrosses to cultivated lettuce, resulting in numerous near-isogenic lines (NILs) only differing for small chromosome regions that are associated with resistance. Low-pass, whole genome sequencing of 11 NILs was used to identify the chromosome segments introgressed from the wild donor species. This located the candidate chromosomal positions for resistance genes as well as additional segments. F2 segregating populations derived from these NILs were used to genetically map the resistance genes to one or two loci in the lettuce reference genome. Precise knowledge of the location of new Dm genes provides the foundation for marker-assisted selection to breed cultivars with multiple genes for resistance to downy mildew.

Entre managerialismo y nostalgia. El debate por el futuro de la Universidad y el Trabajo Académico / Between managerialism and nostalgia: the dispute for the future of the University and AcademicWork

La universidad y el trabajo académico han sido interpeladas a hacerse más eficientes bajo los criterios del Nuevo Management Público, promoviendo privatización y modelos de gestión y fi-nanciamiento por desempeño. Chile, considerado caso emblemático, ha realizado nuevas re-formas respondiendo a las movilizaciones estudiantiles que han cuestionado dicho modelo des-de 2011. Este artículo aborda el debate en torno a estas reformas, desplegado en columnas de opinión, editoriales y cartas al director en los principales medios. Los resultados evidencian la disputa entre dos versiones de universidad y trabajo académico: la managerialista, orientada a competencia, estándares e indicadores, y la humboldtiana basada en constantes apelaciones a un “pasado mejor” que fue truncado. Entre estas, emergen otras híbridas que dan cuenta de la variabilidad en el uso de estos repertorios. Las versiones desplegadas muestran los límites del debate, y de la posibilidad de imaginar un futuro de la universidad más allá del managerialis-mo.(AU)

The university and academic work have been challenged to become more efficient under the criteria of the New Public Management, promoting privatization and performance manage-ment and financing models. Chile, considered an emblematic case, has carried out new re-forms in response to student mobilizations that have questioned this model since 2011. This article addresses the debate around these reforms, displayed in opinion columns, editorials and letters to the editor in the main media. The results show the dispute between two ver-sions of university and academic work: the managerialist, oriented to competence, standards and indicators, and the Humboldtian based on constant appeals to a “better past” that was truncated. These versions show the limits of the debate and of the possibility of imagining a future of the university beyond managerialism.(AU)

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture.

Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

Valencia's Cathedral Church Bell Acoustics Impact on the Hearing Abilities of Bell Ringers.

Studies on the effect of occupational noise have been widely performed for occupations such as construction workers, workers of factories or even musicians and workers of nightclubs. However, studies on the acoustics of church bells are very scarce and usually reported in languages other than English. In Spain, although the tradition of bell ringers is progressively getting lost, some bell ringers that continue transmitting the tradition remain. Church bells create sound with a large sound pressure level that can be heard from a great distance. However, despite the characteristics of the sound of church bells, bell ringers do not present symptoms of occupational hearing loss unlike musicians and construction workers. To determine the effects of the sound of the church bells on bell ringers, in this paper, an acoustic study of the church bells and a physiological study of the hearing abilities of bell ringers. Results show sound pressure levels reaching 120 dB inside the bell tower. The resulting hearing loss in bell ringers is small considering the great intensity of the sound produced by the bells. This is likely due to the short amount of time that bell ringers are exposed to the sound even if it reaches high sound pressure levels.

Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks.

Determining and improving the wellbeing of people is one of the priorities of the OECD countries. Nowadays many sensors allow monitoring different parameters in regard to the wellbeing of people. These sensors can be deployed in smartphones, clothes or accessories like watches. Many studies have been performed on wearable devices that monitor certain aspects of the health of people, especially for specific diseases. In this paper, we propose a non-invasive low-cost and low-energy physical wellbeing monitoring system that provides a wellness score based on the obtained data. We present the architecture of the system and the disposition of the sensors on the sock. The algorithm of the system is presented as well. The wellness threshold evaluation module allows determining if the monitored parameter is within healthy ranges. The message forwarding module allows decreasing the energy consumption of the system by detecting the presence of alerts or changes in the data. Finally, a simulation was performed in order to determine the energy consumption of the system. Results show that our algorithm allows saving 44.9% of the initial energy in 10,000 min for healthy people.

Design and Deployment of Low-Cost Sensors for Monitoring the Water Quality and Fish Behavior in Aquaculture Tanks during the Feeding Process.

The monitoring of farming processes can optimize the use of resources and improve its sustainability and profitability. In fish farms, the water quality, tank environment, and fish behavior must be monitored. Wireless sensor networks (WSNs) are a promising option to perform this monitoring. Nevertheless, its high cost is slowing the expansion of its use. In this paper, we propose a set of sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process. The WSN is based on physical sensors, composed of simple electronic components. The system proposed can monitor water quality parameters, tank status, the feed falling and fish swimming depth and velocity. In addition, the system includes a smart algorithm to reduce the energy waste when sending the information from the node to the database. The system is composed of three nodes in each tank that send the information though the local area network to a database on the Internet and a smart algorithm that detects abnormal values and sends alarms when they happen. All the sensors are designed, calibrated, and deployed to ensure its suitability. The greatest efforts have been accomplished with the fish presence sensor. The total cost of the sensors and nodes for the proposed system is less than 90 €.

Assisted Protection Headphone Proposal to Prevent Chronic Exposure to Percussion Instruments on Musicians.

The effects of chronic exposure to high sound pressure levels (SPLs) are widely studied in the industry environment. However, the way that SPLs affect music students has not been thoroughly examined. In this paper, we study the SPL exposure of batucada students and we propose an assisted protection headphone as a part of e-health system. We measured the SPL reached during a regular percussion class. Pure-tone audiometries were performed to a set of percussion students. The gathered data were statistically analyzed. The assisted protection headphones and their operation are detailed and tested during a regular class. Our results show that 35% of the musicians present with a noise-induced hearing loss, considered as two frequencies with hearing loss of 25 dB or more or one frequency with a hearing loss of 30 dB or more. Our data also shows that those students that do not use any protection have a greater hearing loss. However, the students that use protection headphones are also suffering hearing loss. There might be a problem in the way that musicians are using the protection headphones. Our assisted protection headphones as a part of e-health measures can diminish the negative effects of percussion instruments for musicians.

Development of a Conductivity Sensor for Monitoring Groundwater Resources to Optimize Water Management in Smart City Environments.

The main aim of smart cities is to achieve the sustainable use of resources. In order to make the correct use of resources, an accurate monitoring and management is needed. In some places, like underground aquifers, access for monitoring can be difficult, therefore the use of sensors can be a good solution. Groundwater is very important as a water resource. Just in the USA, aquifers represent the water source for 50% of the population. However, aquifers are endangered due to the contamination. One of the most important parameters to monitor in groundwater is the salinity, as high salinity levels indicate groundwater salinization. In this paper, we present a specific sensor for monitoring groundwater salinization. The sensor is able to measure the electric conductivity of water, which is directly related to the water salinization. The sensor, which is composed of two copper coils, measures the magnetic field alterations due to the presence of electric charges in the water. Different salinities of the water generate different alterations. Our sensor has undergone several tests in order to obtain a conductivity sensor with enough accuracy. First, several prototypes are tested and are compared with the purpose of choosing the best combination of coils. After the best prototype was selected, it was calibrated using up to 30 different samples. Our conductivity sensor presents an operational range from 0.585 mS/cm to 73.8 mS/cm, which is wide enough to cover the typical range of water salinities. With this work, we have demonstrated that it is feasible to measure water conductivity using solenoid coils and that this is a low cost application for groundwater monitoring.

Embarazo múltiple: diagnóstico y manejo ultrasonográfico / Multiple pregnancy: diagnosis and ultrasonographic management

La incidencia de embarazo gemelar es de 1 por ciento de los nacimientos, sin embargo esta condición está asociada a un alto riesgo de mortalidad y morbilidad perinatal debido a la prematuridad y a complicaciones específicas de los embarazos múltiples, como el síndrome de transfusión fetofetal, síndrome de perfusión arterial reversa gemelar y los pagos. En este artículo revisamos la importancia del ultrasonido en el diagnóstico, el manejo y el tratamiento de estas complicaciones con especial énfasis en la determinación de la corionicidad