• Rural Digital Europe
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• 2014-2023close
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Smart parking is common in contemporary cities. These smart parking lots are outfitted mostly with wireless sensor networks (WSNs), which are used to detect, monitor, and collect data on the availability status of all existing parking spaces in a given area. Sensors make up WSN, which may gather, process, and transmit informations to the sink. However, the power and communication limitations of the sensors have an effect on the performance and quality of the WSNs. The decrease in the battery and the energy of the nodes causes a decrease in the life of the nodes and also of the entire WSN network. In this article, we present a routing protocol that implements an efficient and robust algorithm allowing the creation of clusters so that the base station can receive data from the entire WSN network. This protocol adopts a reliable and efficient algorithm allowing to minimize the energy dissipation of the sensors and to increase the lifetime of the WSN. In comparison to alternative parking lot management protocols already in use, the simulation results of the proposed protocol are effective and robust in terms of power consumption, data transmission reliability, and WSN network longevity.

One of the major challenges facing photovoltaic (PV) systems is fault detection. Artificial intelligence (AI) is one of the main popular techniques used in error detection due to its ability to extract signal and image features. In this paper, a deep learning approach based on convolutional neural network (CNN) and internet of things (IoT) technology are used to detect and locate shading faults for a PV water pumping system. The current and voltage signals generated by the PV panels as well as temperature and radiation were used to convert them into 3D images and then upload to a deep learning algorithm. The PV system and fault detection algorithms were simulated by MATLAB. The obtained results indicate that the performance of the proposed deep learning approach to detect and locate faults is better than the traditional statistical methods and other machine learning methods.

Global population growth, climate change, and food safety are some of the challenges that the agri-food sector is current facing. Addressing these in a sustainable way requires the adoption of sustainable-oriented innovations capable of developing new knowledge and reshaping production methods and business models. Digitalization and technological evolution in the agri-food sector, culminating within Agriculture 4.0 (A4.0), represent the latest revolution, as testified by the extant body of knowledge. The lack of a systematic model to map the body of knowledge in A4.0 and how it addresses the agri-food sustainability challenges represents the input of our review. We propose a systematic literature review to discover the research routes in A4.0, increasing the understanding of this paradigm as agri-food sustainability enabler. A taxonomy, composed of 566 terms grouped in seven categories representative of the extant research routes, is proposed: the names assumed by agri-food digitalization process, the main aims and issues of A4.0, the features of the agricultural sector, the parameters monitorable and calculable through A4.0, data, and technologies. It emerged that A4.0 absolve the role of enabler in sustainability agri-food providing to the company a set of data and technological sustainable oriented innovations able to solve, according to an interdisciplinary approach, environmental, social, and economic challenges. The findings identify research gaps and theoretical and practical implications for a wide range of stakeholders, such as engineering managers, agri-food companies, researchers, or companies involved in the web content analysis. Limitations and future research route complete the study.

Nanopesticides (Npes) carry the potential of increased efficacy while reducing application rates, hence increasing agricultural productivity in a more sustainable way. However, given its novelty, the environmental risk assessment of these advanced materials is mostly absent. In the present study we investigated the ecotoxicity of a commercial insecticide, with reported nanofeatures, Karate Zeon®, and compared it to its active substance lambda-cyhalothrin. It is hypothesised that the use of the nanopesticide Karate Zeon® poses lower risk to enchytraeids than its active substance. The standard non-target soil invertebrate Enchytraeus crypticus was used, and exposure was done in LUFA 2.2 soil in 4 tests (endpoints: days): avoidance test [avoidance behaviour: 2 days], OECD standard reproduction test [survival, reproduction plus adults' size: 28 days] and its extension [total number organisms: 56 days], and Full Life Cycle (FLC) test [hatching and juveniles' size: 13 days; survival, reproduction and adults' size: 46 days]. Results showed that enchytraeids did not avoid Karate Zeon® nor its active substance lambda-cyhalothrin, which could be due to neurotoxicity. There was no indication of increased toxicity with prolonged exposure (46, 56d) compared to the standard (28d) for neither of the materials, being overall equally toxic in terms of hatching, survival, and reproduction. The FLCt results indicated that the juvenile stage was the most sensitive, resulting in higher toxicity for the adult animals when exposed from the cocoon stage. Although toxicity was similar between Karate Zeon and lambda-cyhalothrin, different patterns of uptake and elimination cannot be excluded. The benefits of using Karate Zeon will rely on reduced application rates. published