Green Plants in the Red: A Baseline Global Assessment for the IUCN Sampled Red List Index for Plants

Plants provide fundamental support systems for life on Earth and are the basis for all terrestrial ecosystems; a decline in plant diversity will be detrimental to all other groups of organisms including humans. Decline in plant diversity has been hard to quantify, due to the huge numbers of known and yet to be discovered species and the lack of an adequate baseline assessment of extinction risk against which to track changes. The biodiversity of many remote parts of the world remains poorly known, and the rate of new assessments of extinction risk for individual plant species approximates the rate at which new plant species are described. Thus the question ‘How threatened are plants?’ is still very difficult to answer accurately. While completing assessments for each species of plant remains a distant prospect, by assessing a randomly selected sample of species the Sampled Red List Index for Plants gives, for the first time, an accurate view of how threatened plants are across the world. It represents the first key phase of ongoing efforts to monitor the status of the world’s plants. More than 20% of plant species assessed are threatened with extinction, and the habitat with the most threatened species is overwhelmingly tropical rain forest, where the greatest threat to plants is anthropogenic habitat conversion, for arable and livestock agriculture, and harvesting of natural resources. Gymnosperms (e.g. conifers and cycads) are the most threatened group, while a third of plant species included in this study have yet to receive an assessment or are so poorly known that we cannot yet ascertain whether they are threatened or not. This study provides a baseline assessment from which trends in the status of plant biodiversity can be measured and periodically reassessed

NFKB1 and Cancer: Friend or Foe?

Current evidence strongly suggests that aberrant activation of the NF-κB signalling pathway is associated with carcinogenesis. A number of key cellular processes are governed by the effectors of this pathway, including immune responses and apoptosis, both crucial in the development of cancer. Therefore, it is not surprising that dysregulated and chronic NF-κB signalling can have a profound impact on cellular homeostasis. Here we discuss NFKB1 (p105/p50), one of the five subunits of NF-κB, widely implicated in carcinogenesis, in some cases driving cancer progression and in others acting as a tumour-suppressor. The complexity of the role of this subunit lies in the multiple dimeric combination possibilities as well as the different interacting co-factors, which dictate whether gene transcription is activated or repressed, in a cell and organ-specific manner. This review highlights the multiple roles of NFKB1 in the development and progression of different cancers, and the considerations to make when attempting to manipulate NF-κB as a potential cancer therapy

A complete architecture for Ambient Assisted Living scenarios using a cross protocol proxy

Nowadays, in the most developed countries, modern society is moving towards scenarios in which the self-sufficiency elderly people live alone in their homes. An automatic remote monitoring system using wearable sensors is becoming even more important in Ambient Assisted Living applications. For this type of services, it is important that IoT sensors networks, which are generally composed of devices with limited computing power and storage, implement reliable communication among sensors and the Internet. There are several specialized protocols for the Internet of Things proposed by the scientific community, each characterized by its own levels of Quality of Services. The emergence of new protocols forces the need for developing proxying systems able to intermediate among different types of networks and to translate the relative protocols. In this paper, we propose a complete architecture for monitoring and managing wearable devices, and, in particular, fall detection ones. Our system uses a cross protocol proxy and a device with CoAP and MQTT as application level protocols, while it exploits the NB-IoT at physical and data-link levels. The goal of this work is the performance evaluation of the proposed solution in terms of Throughput, Round Trip Time and Delay. The results highlight the low latency reached by the proposed system architecture thanks to the implemented protocols

Databases Performance Evaluation for IoT Systems: The Scrovegni Chapel Use Case

Internet of things devices are used to collect data from the physical world, and to present the results in a way well usable to the end user. Therefore, an accurate choice of the most appropriate technology for storing data collected from the network is relevant. In this paper we focus the attention on the selection of the best database management system for cultural heritage applications, in particular referring to the use case of light monitoring at the Scrovegni Chapel (Padua, Italy), to emphasize the Giotto's frescoes. For doing so, SQL and NoSQL solutions are compared, and the obtained results are used to find the best solution for this application. Moreover these results can be used as a practical reference for the more appropriate selection of the right database for real use cases

Internet of Things for Earthquake Early Warning Systems: A Performance Comparison Between Communication Protocols

Earthquake Early Warning Systems (EEWSs) characterize seismic events in real time and estimate the expected ground motion amplitude in specific areas to send alerts before the destructive waves arrive. Together with the reliability of the results, the rapidity with which an EEWS can detect an earthquake becomes a focal point for developing efficient seismic node networks. Internet of Things (IoT) architectures can be used in EEWSs to expand a seismic network and acquire data even from low-cost seismic nodes. However, the latency and the total alert time introduced by the adopted communication protocols should be carefully evaluated. This study proposes an IoT solution based on the message queue-telemetry transport protocol for the waveform transmission acquired by seismic nodes and presents a performance comparison between it and the most widely used standard in current EEWSs. The comparison was performed in evaluation tests where different seismic networks were simulated using a dataset of real earthquakes. This study analyzes the phases preceding the earthquake detection, showing how the proposed solution detects the same events of traditional EEWSs with a total alert time of approximately 1.6 seconds lower

An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector

Anomaly detection plays a crucial role in preserving industrial plant health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In complex systems, such as oil and gas, many components need to be kept operational. Predicting which parts will break down in a time interval or identifying which ones are working under abnormal conditions can significantly increase their reliability. Moreover, it underlines how the use of artificial intelligence is also emerging in the process industry and not only in manufacturing. In particular, the state-of-the-art analysis reveals a growing interest in the subject and that most identified algorithms are based on neural network approaches in their various forms. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas: (i) steady-state area, (ii) water anomaly macro-area, (iii) air-water anomaly area, (iv) tank anomaly area, (v) air anomaly macro-area, (vi) and steady-state transition area. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system. The proposed algorithm was tested on an experimental plant at Universita Politecnica delle Marche