Evaluation of the Viability of 3D Printing in Recycling Polymers

The increased use of plastics in industrial and agricultural applications has led to high levels of pollution worldwide and is a significant challenge. To address this plastic pollution, conventional methods such as landfills and incineration are used, leading to further challenges such as the generation of greenhouse gas emissions. Therefore, increasing interest has been directed to identifying alternative methods to dispose of plastic waste from agriculture. The novelty of the current research arose from the lack of critical reviews on how 3-Dimensional (3D) printing was adopted for recycling plastics, its application in the production of agricultural plastics, and its specific benefits, disadvantages, and limitations in recycling plastics. The review paper offers novel insights regarding the application of 3D printing methods including Fused Particle Fabrication (FPF), Hot Melt Extrusion (HME), and Fused Deposition Modelling (FDM) to make filaments from plastics. However, the methods were adopted in local recycling setups where only small quantities of the raw materials were considered. Data was collected using a systematic review involving 39 studies. Findings showed that the application of the 3D printing methods led to the generation of agricultural plastics such as Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate (PET), and High-Density Polyethylene (HDPE), which were found to have properties comparable to those of virgin plastic, suggesting the viability of 3D printing in managing plastic pollution. However, limitations were also associated with the 3D printing methods; 3D-printed plastics deteriorated rapidly under Ultraviolet (UV) light and are non-biodegradable, posing further risks of plastic pollution. However, UV stabilization helps reduce plastic deterioration, thus increasing longevity and reducing disposal. Future directions emphasize identifying methods to reduce the deterioration of 3D-printed agricultural plastics and increasing their longevity in addition to UV stability.

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (ECw) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (εa) readings were lower than that of Topp's permittivity-water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θm-εa0.5 decreases with the increase in bulk EC (σb) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εa-σb, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) and the soil type.

The Aging of Polymers under Electromagnetic Radiation

Polymeric materials degrade as they react with environmental conditions such as temperature, light, and humidity. Electromagnetic radiation from the Sun's ultraviolet rays weakens the mechanical properties of polymers, causing them to degrade. This study examined the phenomenon of polymer aging due to exposure to ultraviolet radiation. The study examined three specific objectives, including the key theories explaining ultraviolet (UV) radiation's impact on polymer decomposition, the underlying testing procedures for determining the aging properties of polymeric materials, and appraising the current technical methods for enhancing the UV resistance of polymers. The study utilized a literature review methodology to understand the aging effect of electromagnetic radiation on polymers. Thus, the study concluded that using additives and UV absorbers on polymers and polymer composites can elongate the lifespan of polymers by shielding them from the aging effects of UV radiation. The findings from the study suggest that thermal conditions contribute to polymer degradation by breaking down their physical and chemical bonds. Thermal oxidative environments accelerate aging due to the presence of UV radiation and temperatures that foster a quicker degradation of plastics.

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation.

Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs of the population on Earth and the degradation of natural resources. Focusing on the “hot” area of natural resource preservation, the recent appearance of more efficient and cheaper microcontrollers, the advances in low-power and long-range radios, and the availability of accompanying software tools are exploited in order to monitor water consumption and to detect and report misuse events, with reduced power and network bandwidth requirements. Quite often, large quantities of water are wasted for a variety of reasons; from broken irrigation pipes to people’s negligence. To tackle this problem, the necessary design and implementation details are highlighted for an experimental water usage reporting system that exhibits Edge Artificial Intelligence (Edge AI) functionality. By combining modern technologies, such as Internet of Things (IoT), Edge Computing (EC) and Machine Learning (ML), the deployment of a compact automated detection mechanism can be easier than before, while the information that has to travel from the edges of the network to the cloud and thus the corresponding energy footprint are drastically reduced. In parallel, characteristic implementation challenges are discussed, and a first set of corresponding evaluation results is presented.

Early Warning Systems for COVID-19 Infections Based on Low-Cost Indoor Air-Quality Sensors and LPWANs

During the last two years, the COVID-19 pandemic continues to wreak havoc in many areas of the world, as the infection spreads through person-to-person contact. Transmission and prognosis, once infected, are potentially influenced by many factors, including indoor air pollution. Particulate Matter (PM) is a complex mixture of solid and/or liquid particles suspended in the air that can vary in size, shape, and composition and recent scientific work correlate this index with a considerable risk of COVID-19 infections. Early Warning Systems (EWS) and the Internet of Things (IoT) have given rise to the development of Low Power Wide Area Networks (LPWAN) based on sensors, which measure PM levels and monitor In-door Air pollution Quality (IAQ) in real-time. This article proposes an open-source platform architecture and presents the development of a Long Range (LoRa) based sensor network for IAQ and PM measurement. A few air quality sensors were tested, a network platform was implemented after simulating setup topologies, emphasizing feasible low-cost open platform architecture.