Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)

Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained

Late Maastrichtian carbon isotope stratigraphy and cyclostratigraphy of the Newfoundland Margin (Site U1403, IODP Expedition 342)

Earth’s climate during the Maastrichtian (latest Cretaceous) was punctuated by brief warming and cooling episodes, accompanied by perturbations of the global carbon cycle. Superimposed on a long-term cooling trend, the middle Maastrichtian is characterized by deep-sea warming and relatively high values of stable carbon-isotope ratios, followed by strong climatic variability towards the end of the Cretaceous. A lack of knowledge on the timing of climatic change inhibits our understanding of underlying causal mechanisms. We present an integrated stratigraphy from Integrated Ocean Drilling Program (IODP) Site U1403, providing an expanded deep ocean record from the North Atlantic (Expedition 342, Newfoundland Margin). Distinct sedimentary cyclicity suggests that orbital forcing played a major role in depositional processes, which is confirmed by statistical analyses of high resolution elemental data obtained by X-ray fluorescence (XRF) core scanning. Astronomical calibration reveals that the investigated interval encompasses seven 405-kyr cycles (Ma4051 to Ma4057) and spans the 2.8 Myr directly preceding the Cretaceous/Paleocene (K/Pg) boundary. A high-resolution carbon-isotope record from bulk carbonates allows us to identify global trends in the late Maastrichtian carbon cycle. Low-amplitude variations (up to 0.4‰) in carbon isotopes at Site U1403 match similar scale variability in records from Tethyan and Pacific open-ocean sites. Comparison between Site U1403 and the hemipelagic restricted basin of the Zumaia section (northern Spain), with its own well-established independent cyclostratigraphic framework, is more complex. Whereas the pre-K/Pg oscillations and the negative values of the Mid-Maastrichtian Event (MME) can be readily discerned in both the Zumaia and U1403 records, patterns diverge during a ~ 1 Myr period in the late Maastrichtian (67.8–66.8 Ma), with Site U1403 more reliably reflecting global carbon cycling. Our new carbon isotope record and cyclostratigraphy offer promise for Site U1403 to serve as a future reference section for high-resolution studies of late Maastrichtian paleoclimatic change

TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart devices within these networks are employed to handle sensitive information. However, the scale and openness of IoT networks make them highly vulnerable to security breaches and attacks, such as eavesdropping, weak authentication, and malicious payloads. Hence, there is a need for advanced machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS). Existing ML-based IoT-IDSs face several limitations in effectively detecting malicious activities, mainly due to imbalanced training data. To address this, this study introduces a transformer neural network-based intrusion detection system (TNN-IDS) specifically designed for MQTT-enabled IoT networks. The proposed approach aims to enhance the detection of malicious activities within these networks. The TNN-IDS leverages the parallel processing capability of the Transformer Neural Network, which accelerates the learning process and results in improved detection of malicious attacks. To evaluate the performance of the proposed system, it was compared with various IDSs based on ML and DL approaches. The experimental results demonstrate that the proposed TNN-IDS outperforms other systems in terms of detecting malicious activity. The TNN-IDS achieved optimum accuracies reaching 99.9% in detecting malicious activities

FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

BackgroundForests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset.ResultsThe test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data.ConclusionThe integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times

Dissecting the multifunctional role of the N-terminal disordered domain of a plant virus coat protein in RNA packaging, viral movement and interference with antiviral plant defense

[EN] The coat protein (CP) of Melon necrotic spot virus (MNSV) is structurally composed of three major domains. The middle S-domain builds a robust protein shell around the viral genome, whereas the C-terminal protruding domain, or P-domain, is involved in the attachment of virions to the transmission vector. Here, we have shown that the N-terminal domain, or R-domain, and the arm region, which connects the R-domain and S-domain, are involved in different key steps of the viral cycle, such as cell-to-cell movement and the suppression of RNA silencing and pathogenesis through their RNA-binding capabilities. Deletion mutants revealed that the CP RNA-binding ability was abolished only after complete, but not partial, deletion of the R-domain and the arm region. However, a comparison of the apparent dissociation constants for the CP RNA-binding reaction of several partial deletion mutants showed that the arm region played a more relevant role than the R-domain in in vitro RNA binding. Similar results were obtained in in vivo assays, although, in this case, full-length CPs were required to encapsidate full-length genomes. We also found that the R-domain carboxyl portion and the arm region were essential for efficient cell-to-cell movement, for enhancement of Potato virus X pathogenicity, for suppression of systemic RNA silencing and for binding of small RNAs. Therefore, unlike other carmovirus CPs, the R-domain and the arm region of MNSV CP have acquired, in addition to other essential functions such as genome binding and encapsidation functions, the ability to suppress RNA silencing by preventing systemic small RNA transport.This work was funded by grant BIO2014-54862-R from the Spanish Ministry of Science and Innovation and the Prometeo Program GV2014/010 from the Generalitat Valenciana. J.A.N. and M.S.-S. are the recipients of a postdoctoral contract and a PhD fellowship, respectively, from the Ministerio de Educacion y Ciencia of Spain. We thank L. Corachan for technical assistance. The authors have no conflicts of interest to declare.Serra Soriano, M.; Navarro Bohigues, JA.; Pallás Benet, V. (2017). Dissecting the multifunctional role of the N-terminal disordered domain of a plant virus coat protein in RNA packaging, viral movement and interference with antiviral plant defense. Molecular Plant Pathology. 18(6):837-849. https://doi.org/10.1111/mpp.12448S83784918

Dynamics of Molecular Evolution and Phylogeography of Barley yellow dwarf virus-PAV

Barley yellow dwarf virus (BYDV) species PAV occurs frequently in irrigated wheat fields worldwide and can be efficiently transmitted by aphids. Isolates of BYDV-PAV from different countries show great divergence both in genomic sequences and pathogenicity. Despite its economical importance, the genetic structure of natural BYDV-PAV populations, as well as of the mechanisms maintaining its high diversity, remain poorly explored. In this study, we investigate the dynamics of BYDV-PAV genome evolution utilizing time-structured data sets of complete genomic sequences from 58 isolates from different hosts obtained worldwide. First, we observed that BYDV-PAV exhibits a high frequency of homologous recombination. Second, our analysis revealed that BYDV-PAV genome evolves under purifying selection and at a substitution rate similar to other RNA viruses (3.158×10−4 nucleotide substitutions/site/year). Phylogeography analyses show that the diversification of BYDV-PAV can be explained by local geographic adaptation as well as by host-driven adaptation. These results increase our understanding of the diversity, molecular evolutionary characteristics and epidemiological properties of an economically important plant RNA virus

Detoxification of rats subjected to nickel chloride by a biomaterial-based carbonated orthophosphate

International audienceSummary Recently, the therapeutic approaches of the detoxification against the metals (nickel) in the body are the use of biomaterials such as carbonated hydroxyapatite. The aim of this study is therefore to analyze the physiological and physicochemical parameters of strain white rats "Wistar" receiving nickel chloride and to study the protective associative of apatite against adverse effects of this metal, and this in comparison with control rats. Our results showed that the nickel induced in rats an oxidative stress objectified by elevated levels of thiobarbituric acid-reactive substances and conjugated dienes associated with inhibition of the activity of the antioxidant defense system such as glutathione peroxidase, superoxide dismutase and catalase in the liver, kidney, spleen and erythrocyte. Disorders balances of ferric, phosphocalcic, a renal failure and a liver toxicity were observed in rats exposed to nickel. As well as a significant increase in the rate of nickel in the bones and microcytic anemia was revealed. However, the implantation of carbonated hydroxyapatite in capsule form protects rats intoxicated by the nickel against the toxic effects of this metal by lowering the levels of markers of lipid peroxidation and improving the activities of defense enzymes. Our implantation technique is effective to correct ferric balance and phosphocalcic equilibrium, to protect liver and kidney function, to reduce the rate of bone nickel and to correct anemia. They clearly explain the beneficial and protective of our biomaterial which aims the detoxification of rats receiving nickel by substituting cationic (Ca2+ by Ni2+) and anionic (OH− by Cl−) confirmed by physicochemical characterization like the IR spectroscopy and X-ray diffraction. These techniques have shown on the one hand a duplication of OH− bands (IR) and on the other hand the increase of the volume of the apatite cell after these substitutions (X-ray diffraction)