Design of a Tuned Proportional-Integral-Derivative Water Level Controller for First-Order Automated Irrigation System in Rice Field

Some problems of Filipino farmers in Nueva Ecija are irrigation systems and labor shortage. Most of them are unable to work due to old age while others chose to stop because of the COVID-19 pandemic. Meanwhile, irrigation systems have been an issue due to the lack of resources such as continuous water supply and control. Fortunately, there is a progression of smart farming in the country which could assist in optimizing farming processes. This study presents a systematic literature survey on rice farming technologies and challenges. This study also aims to help address these problems by creating a rice irrigation system that introduces a water level control system. The system was comprised of a mobile application, Arduino ESP32 module, and a tank with water level sensors. The mobile application was used to set the desired water level while the proportional- integral-derivative (PID) controller adjusted the water level automatically. When current water level is lower than the setpoint, the valves to the tank will open. Tank specifications were used to come up with a transfer function for the system. The proposed design was simulated in MATLAB Simulink and PID parameters were tuned to enhance system performance. The tuned control system obtained an output response with less overshoot and faster settling time. © 2022 IEEE.

Collaborative Learning Supported by a Brownfield Remediation Case Study

Industrial activity of the past has created several contaminated brownfields, which, particularly in remote areas, are difficult to remedy from an economic point of view. In this project, a novel approach for in-situ removal of mineral hydrocarbons from soil was investigated. The underlying concept was to flush contaminated soil with emulsions of plant oil in water, to suck off the contaminant-laden emulsion from the ground water level and to separate oil and water using oil-binding non-wovens. The process development was carried out in a research project, where students from a university of applied sciences and from a technical college were involved. Based on the specific case of brownfield remediation, a collaborative learning experience for the students was created. Environmental protection and safeguarding is a topic of high interest to students, and there was a high motivation to obtain results. Due to the COVID19 pandemic, most collaboration was handled remotely via virtual teams. The chosen brownfield for this case study was a former petroleum refinery site in Lower Austria, were up to 40 g/kg of mineral hydrocarbons were found in the soil in the non-saturated zone. Mineral hydrocarbons show good solubility in plant oils. Emulsions of 5–10% of rapeseed oil in water were prepared and chosen, to have better wettability of the ground materials and lower viscosity. The goal was to develop a process that can extract 80–90% of mineral hydrocarbons in the soil, and which leaves only a minor fraction of the plant oil in the soil. When the trials, which were carried out in the lab and in the field, showed that the permeability of soil is very low, it was decided to develop a prototype for on-site soil washing. The soil of the chosen brownfield is partly made from gravel and sand, where an in-situ flushing process is possible. However, there is also clay, and that material hardly lets water or emulsion penetrate. For the on-site washing process, a laboratory-scale prototype was developed. It was built by the Linzer Technikum (LITEC) and tested with different soils at the university of applied sciences. The prototype could be built by LITEC, with an extraction vessel made of steel and a mixer. Trials were done to determine the degree of extraction of mineral oil and the fraction of plant oil that is not recaptured. 500 g of soil were mixed intensely with 500 g of solvent (water and emulsions). Table 1 presents the results for sand and clay. The process of washing out mineral hydrocarbon contamination from soil was found to show a good potential. The ground material should be sieved to remove coarse material (>10 mm), and the finer fraction can be subjected to the washing of plant oil in water, where the plant oil fraction can be between 5 and 50%, depending on degree of contamination. To reduce the amount of non-recaptured plant oil, a second and third washing cycle with a lower oil fraction, or with pure water, can be applied. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict”approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch”RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases. Author

Automatic Monitoring and Control System for Different Water Levels in Interconnected Multiple Twin Tanks with Dry Run Protection Using Microprocessor Orange Pi

The proposed system effectively controlled and monitored the water level of the dual tank system with efficient dry-run protection to prevent the motor from burning out and avoid wastage of electricity in case of no water. An Orange Pi was used to test its working and ability to control and monitor a real-Time system as no prior research is done on a dual water tank control system using Orangepi SBC. Due to COVID-19, millions of people's financial condition has worsened, which is why an initiative is taken to use the cheap board for making prototypes and moving towards PLCs after the desired outcomes. This research aims to provide a system at cheaper rates to handle large water tanks. This system is very efficient and valuable in dams, tanks, purifiers, and water containers. The proposed system also has dry run protection to avoid wastage of electricity in case of no water. The research is an advancement in automation in real-Time virtual monitoring and different water level control systems. This research makes life more comfortable because real-Time monitoring with a water control system reduces water wastage, leading to the complete modern solution of the problems. © 2022 IEEE.

The future container throughput for inland shipping on the traditional Rhine: a SARIMAX approach

Inland container shipping is confronted with significant challenges, both on the demand and supply side. In line with the 2019 Green Deal’s ambitious goals and 2020 Sustainable and Smart Mobility Strategy, the European Commission presented an ‘Inland Waterway Transport Action plan 2021-2027’ with the target of shifting more freight across inland waterways. However, the COVID-19 pandemic together with the low water level raise interest in how these could impact the throughput for container transport on the inland waterways. In this research, the scope is on the container throughput for inland container transport on the traditional Rhine. This study first identifies the market drivers for containerized inland navigation in the medium run and then selects the SARIMAX method to analyse Inland Waterway Transport (IWT) volumes. The model application shows that the throughput for inland container transport on the traditional Rhine is impacted on by periods of low water and the weakening of the economy caused by COVID-19. The results of the study suggest that if the IWT container market is impacted by the identified factors, the throughput for containerized IWT is expected to decline by 8.9% in 2023 relative to the volumes in 2020. The research might act as a decision support tool for analysis, management and planning for policymakers and stakeholders. © 2022 Van Meir N., Rashed Y., Storms K., Sys C., Vanelslander T. & van Hassel E.

GARDENPRO-Android Mobile Application for Home Garden Management Using Machine Learning Augmented Reality and IOT

IoT is an important technology for Agriculture. When it comes to machine learning-based decisions related to agriculture the farmers can take precise decisions according to the sensor data collected from IoT. But most people who live in urban areas of Sri Lanka were adopted to agriculture because of COVID 19 pandemic situation. So most of the people have limited space for gardening, and newcomers haven't proper knowledge of what are the crops suitable for their area of garden space and relevant instructions to grow the crops which they like and environment factors needed. And they haven't understood the type of fertilizers that need for the crop. Almost nowadays people haven't much time for watering and care the plants that they like to grow in their garden. Garden Pro Android Application had introduced a feature Machine Learning based crop recommendation system for newcomers to home gardening. By using this feature of the app user can get an idea about crops that are suitable for their garden area, gardening instructions. And soil moisture also fertilizers and the application shows real-time temperature, soil moisture, and humidity, water level required for the crop that collected from IoT sensors and additionally the 3D View of the plants that predicted according to the soil conditions visualize to the user by Augmented reality technology. © 2022 IEEE.

Aqua Monitoring System using AWS

Internet of Things (IoT) is a technology which is rapidly growing, the future of IoT is limitless as the data streams have quadrupled over the years. Future markets are going to shift from traditional data processing techniques to Big Data Analytics and Cloud computing as businesses worldwide are shifting to cloud based work approaches which was largely boosted by the Covid-19 pandemic. Fishes are well known to be highly sensitive to the environment, hence they require proper care and attention from their owners. Many a times they forget to feed the fish, change the water, check the pH levels etc. these problems look simple but when it comes to tracking hundreds of fishes it can be difficult, these issues can be easily resolved by using IoT. An IoT system that can be highly beneficial for small to large scale aquariums is necessary e.g. Dubai Aquarium (where hundreds of fishes are constantly monitored). The System can be used to create the ideal conditions required for high yield. The aquaculture sector is going to play a crucial role in the future economy as fishes are getting scarce all over the world. Steps must be taken to streamline processes and by which increase efficiency while improving fish health. IoT based Aquariums can save Ocean Wildlife by building reliable systems that are capable of real-time data processing. Massive tanks can be built for endangered species in remote locations further increasing biodiversity and building a balanced ecosystem. Larger fishes can be monitored using technologies such as RFID, transmitters, etc. since it can be difficult to monitor them in large tanks. Data received from sensors can be stored in some cloud platform and analyzed for future predictions and redundant storage, all sorts of smart devices are able to communicate with each other regardless of hardware and operating system used. The IoT based Aquarium Monitoring device is capable of capturing the water levels inside the aquarium and notifies the user by email when its low. It can switch on/off the lights of the aquarium, control the automatic feeder using and record the room temperature and humidity readings with the help of AWS technologies. The fish feeder is controlled by the user using a voice application or web/app interface. Parameters used in this project are Room Temperature, Humidity, Water Level, LED status and Feeder status. Sensor acquisition is performed by ESP-32, it is also used as data processing device as well as local server/controller. User can monitor the conditions of the aquarium locally or remotely from any part of the world as long as he/she has an Internet connection since data is processed through AWS Lambda, stored in DynamoDB and hosted in AWS API. Data is further visualized using AWS IoT Analytics and QuickSight for proper decision making. Every feature in this model works effortlessly and is highly accurate. A wide scale Industrial Application of this Project can be included in Aquaponics fish management, fish farming, Zoo keeping, etc. The data received can be used to take necessary actions and are stored for future studies. They are highly beneficial for farmers raising a certain species of fishes and for maintaining a balanced ecology. © 2022 IEEE.

Evaluation of Macrophyte Community Dynamics (2015–2020) in Southern Lake Garda (Italy) from Sentinel-2 Data

Featured ApplicationThe improvement of effective remote sensing-based approaches to map macrophyte features can provide a baseline of adequate spatiotemporal resolution for 21st century monitoring applications equipped to play a prominent role in the context of medium–large-scale management programs of ecological conservation and scientific research.Macrophytes are of fundamental importance to the functioning of lake ecosystems. They provide structure, habitat, and a food source and are a required component in monitoring programs of lake ecological quality. The key aim of this study is to document the variation in spatial extent and density of macrophytes seasonally between 2015 and 2020 of the Sirmione Peninsula (Lake Garda, Italy), using Sentinel-2 imagery. In addition to this, our results were compared to previous data from imaging spectrometry;individual parameters affecting macrophyte communities were tested, and the possible effect of the COVID-19 lockdown on macrophyte colonization was evaluated. Satellite images allowed the mapping of the spatiotemporal dynamics of submerged rooted macrophytes in order to support monitoring of the shallow water ecosystem under study. Substantial changes were found in both spatial extent and density over the period from 2015 to 2020, particularly in 2019 when there was almost a complete absence of dense macrophytes. Variables found to influence the amount of macrophytes included transparency, chlorophyll–a, water level, winter wave height, and grazing by herbivores. A separate analysis focusing on areas associated with boat transit found a recovery in macrophyte coverage during the period of COVID-19 lockdown. The outcome of the study highlights a decline in the density of the macrophytes and a shift towards deeper areas compared to the situation in 1997. The area examined is part of an internationally important site containing the highest abundance and diversity of overwintering water birds in Italy. Exploiting satellite data at high frequency provided an insight to understand the dynamic changes and interactions with herbivorous birds, environmental factors, and anthropogenic pressures, revealing a delicately balanced and threatened ecosystem.

Discussion on Influencing Factors and Control Range of Disinfection Effect in Waterworks during the COVID-19 Outbreak

This paper studied the influencing factors of disinfection effect in water purification process and the influence of external demand on the water purification process to ensure that the effective virus inactivation rate of waterworks can meet the requirements of microbiological safety during the COVID-19 outbreak. The results showed that the effluent turbidity should be no more than 0. 3 NTU to meet the requirements of coagulation sedimentation filtration process for virus 2-lg removal rate under the condition of the fixed source water temperature and pH value. On the basis of the above,with the monitoring of the effluent turbidity,water level of clean water tank,water quantity of waterworks and residual chlorine by real-time online instruments,the CT value of the clean water tank was controlled and adjusted within an appropriate range in real time,so that it not only met the 4-lg virus inactivation rate but also reduced the risk of disinfection by-products. Finally,a virus reduction rate of above 6-lg was achieved with the treatment process of waterworks,which could meet the biological safety requirements of drinking water during the epidemic,and have a sufficient safety margin.

The interactive computer simulation and learning activity for facilitating students' conceptual understanding on the buoyant force through the CoSci learning platform

Learning science, especially in the physics field, there are many varieties of invisible and phenomena that are hard and difficult for students to observe and learn. One of the tools that can help students to understand those phenomena in a better way is computer simulations. The computer simulations are usually used in both on-site classroom and on-line learning platforms. Learning in the COVID-19 pandemic era at present, the computer simulations are very important for helping students to understand the physics concept. Interactive computer simulation can be considered as one of the effective methods of facilitating inquiry learning in science, as it allows students to experience the scientific inquiry process and facilitates students to understand an conception and to understand the relationship between variables of invisible phenomena more clearly in reasonable ways. This study aimed to develop the interactive computer simulation and learning activity for enhancing students' conceptual understanding of the buoyant force on the CoSci learning platform. Totally eighteen participants were studied in the twelfth grade in science classrooms of a university-affiliated school project (SCiUS), Khon Kaen University, Thailand, in 2019. The learning activity was developed based on students' alternative concepts and used to facilitate students' conceptual understanding of the buoyant force. There were six basic concepts related to the buoyant force constructed based on the predict-observe-explain strategy (POE) with the interactive computer simulation (i.e., the CoSci learning platform) in the learning activity. The learning activity on the CoSci learning platform consisted of eight pie charts such as 1) main question pie chart, 2) density pie chart, 3) water level pie chart, 4) volume pie chart, 5) mass pie chart, 6) weight pie chart, 7) submerged depth pie chart, and 8) answer pie chart. There were six interactive computer simulations used in this research including 1) density simulation, 2) water level simulation, 3) volume simulation, 4) mass simulation, 5) submerged depth simulation, and 6) weight simulation. All of these simulations were developed on the CoSci learning platform (https://cosci.tw/). The findings showed that 72% of students performed better in the post-test scores than in the pre-test score in all six basic concepts related to the buoyant force after learning buoyant force on the CoSci platform. Furthermore, the most difficulty in changing misconception in learning of the buoyant force was the concept related to the mass of the object. © 2022 Institute of Physics Publishing. All rights reserved.