dataesr/scientific-paragraphs-categorization huggingface dataset:
07 million, n = 272 entries). Plant costs were driven primarily by the flowering plants Magnoliopsida ($9. 35 billion, 34% of total costs). When observed costs were considered solely, Magnoliopsida was the costliest class of species, with total reported costs of $1. 59 billion (n = 2,049 entries), followed by insects, with $1. 74 billion (n = 128 entries) (see also Suppl. material 2: Table  S3 ). The database for the Mediterranean contains costs for 218 species and 187 genera (considering only costs attributable to individual species or genera). The pine wood nematode Bursaphelenchus xylophilus, the only species within the class of Secernentea, was by far the costliest invasive species across the Mediterranean basin, with total costs peaking at $14. 08 billion (Suppl. material 2: Table  S3 ).
Discussion Between 1990 and 2017, the total recorded economic costs of biological invasions in Mediterranean countries amounted to $27. 31 billion. However, most costs are the result of predictions or expectations (87% of total costs, $23. 73 billion) rather than realised costs, meaning that costs were projected in time and/or space by the original authors, so these costs have not necessarily been borne in practice. It is important to acknowledge this as a limitation in our understanding of actual economic impacts of invasions in the region. Observed costs of biological invasions were still substantial, at $3. 59 billion over the same time period. Note again though that our database includes reported costs only, implying that costs are likely a substantial underestimate. Additionally, and as suggested by our results, costs may reflect reporting effort as much as real costs.
Biases and gaps in our database likely reflect an absence of published material or a failure of the InvaCost literature searches to find this or unpublished material, rather than a genuine absence of costs. Nevertheless, our analysis of temporal trends identified marked increases in invasion costs over time (during the last three decades), particularly for resource damages, in line with evidence of increasing rates of invasion worldwide  (Seebens et al. 2017 ) and increasing publication rates. Our understanding of the economic impacts of biological invasions in the Mediterranean basin is largely limited to studies from a subset of countries: cost data were found for only 15 out of 26 countries, with the Western European countries (France, Spain and Italy) dominating reported costs. While most of the invasive species causing the highest monetary losses in the Mediterranean are present in many countries, their observed costs are only reported by a few.
For example, our database only contains observed costs for cnidarians in Israel, despite the presence of a number of invasive species of jellyfish all over the Mediterranean  (Brotz and Pauly 2012). Furthermore, previous findings  (Capinha et al. 2014; Essl et al. 2015; Schertler et al. 2020; Zhang et al. 2020)  have shown that large areas of the Mediterranean basin are predicted to be currently climatically suitable for some of the IAS presenting observed damage costs in other regions. Assuming the presence of suitable dispersal vectors, costs are likely already occurring in these regions (but have not been reported or captured in our database) or likely to occur in additional countries as IAS distributions expand. Not surprisingly and in line with earlier literature establishing correlations between economic development and invasions  (Nuñez and Pauchard 2010) , we identified research effort (reflected through expenditure in R&D) to be positively and significantly correlated with both damage and management costs of IAS.
Our results also provide evidence for strong taxonomic gaps and biases, with most costs derived from few invasive species or taxonomic groups. The top 10 costliest species (Suppl. material 2: Table  S4 ) account for 70% of total costs and 91% of observed costs. A key cluster of reported costs was identified for terrestrial forbs in Western Europe and the Balkans. Costs from two publications and three species dominate the database, driving patterns in total costs. First, Issanchou (2012) estimated, by extrapolation, the economic losses to tourism and recreation caused by floating primrose willow Ludwigia peploides and water primrose Ludwigia grandiflora. Although this study focuses on a single French marsh, the annual cost is substantial and is described as extending over 13 years, resulting in a large total cost ($7.
These agendas and incentives, which differ across countries depending on e. g. national priorities on certain sectors of the economy, largely shape our understanding of costs at a regional scale, likely creating bias over ecosystems, sectors and countries affected (Kourantidou and Kaiser 2019). Our database contains no information on the economic cost of several IAS known to have large costs in invaded habitats elsewhere in the world, or at the global scale. Such species present as aliens in the Mediterranean, include for example the diamondback moth Plutella xylostella, the carpet sea squirt Didemnum vexillum and kikuyu grass Cenchrus clandestinus or Pennisetum clandestinum  (Musil et al. 2005; Mendieta and Cardenas 2010; Ordóñez et al. 2015; Bradshaw et al. 2016). Similarly, the database is missing information on costs of several IAS or alien species known or expected to have large social and/or ecological impacts in the Mediterranean -which may be linked to high economic costs -such as the common myna Acridotheres tristis, the seaweed Codium parvulum and the Pacific oyster Crassostrea gigas  (Katsanevakis et al.
2016; Peyton et al. 2019 ). An absence of such species from our database should not necessarily be interpreted as an absence of realised economic costs. In addition, several highly costly species in some countries are also invasive in others, but with no recorded costs. As an example, a study of the costs of invasions in France calculated the potential costs of all IAS known to be present but with no cost record, from the cost records in other countries  (Renault et al. 2021). This estimation increased the economic costs of IAS in France by $968 million over the period 1993-2018 (i. e. more than 8%). These examples highlight the need to expand research efforts quantifying the economic impacts of existing, ongoing and expected invasions. These gaps in species reported are also reflected in the ecological literature for the region that describes the presence of many IAS  (Zenetos et al.
Most of the terrestrial species occupy disturbed areas, cultivated lands or forests. No clear pattern has been identified for aquatic invasions which may reflect, among other factors, underreporting of invasions in aquatic systems. With 42% of countries in the Mediterranean basin completely absent from our database, very few recorded costs from the vast majority of the rest and collective action on combating invasions largely missing in the Mediterranean basin, it becomes clear that there is an urgent need for comprehensive, resolute and standardised reporting of how invasions impact human and social wellbeing and economies. This is especially the case in aquatic environments and the Mediterranean Sea in particular, which is known to be among the world's most invaded. Such efforts will allow for specifying high-risk and/or high-impact invasive taxa and identifying with more accuracy the spatial and temporal scale of realized and expected impacts.
Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning.
The quality and reliability of the collected data from a variety of sensor measurements such as voltages and currents are key factors for EVs. In this paper, an FD system with the real values obtained in the experiments has been developed, and then a real simulation model to apply the FD tool has been developed. Section 2 reviews the literature and examines the different machine learning (ML) techniques. Section 3 defines the problem in the FD of EVs and the purpose of the research study. Section 4 introduces the research methodology and simulations. Section 5 discusses the results of the different models. Section 6 presents the conclusion of the article and the potential future work.
Data Analytics and Deep Learning in EVs Big data can be used to assess the capability of data-analyzing algorithms and the corresponding capacity of hardwares to handle large data sets  [20]. In keeping with the fast growth of technology and advances in measurement techniques, the volume of the data generated in EVs (electric motors, multi-energy systems, power electronic converters, and so on) has been increased significantly. Because the amount of the generated data is growing exponentially, the data structure is becoming much more complex. This creates new challenges in analysis methods. Big data requires appropriate data analysis tools to tackle the problem of the FD. The growing interest and recent breakthroughs in AI and ML have actively contributed to the development of new methods for FD and fault prognosis approaches.
Deep learning, which is one of the ML techniques, has recently showed remarkable results in a variety of fields. The main concept in deep learning algorithms is to automate the mining of data representations  [23]. Deep learning methods extract meaningful features by using a huge amount of unsupervised data. Deep learning is a collection of simple trainable mathematical units that learns complex functions  [29]. This architecture improves ANNs with better training methods; it makes learning algorithms much easier to show better performance. Feature representation attempts to learn good features automatically. Deep learning can learn multiple levels of representation. These algorithms are enhanced by AI which aims to imitate the human's brain ability to analyze, learn, and make decisions on very complex problems like EVs and smart grids.
FD has proven to be a complex problem because of non-linearity. This problem cannot be solved by using a simple analytical formulation. The FD must have advanced data analytics and deep learning tools to decrease the cost of maintenance and instabilities. The FD in EVs with AI techniques have proved to be a useful tool to solve these issues. Nowadays, the most widely used methods are deep learning approaches. They are appropriate for the problem of energy-related fields  [33]. In this paper, an FD approach with the experimental results of an EV prototype is proposed and the use of deep learning to perform the automated FD is discussed. An EV is simulated and the collect data, that may be related to faults such as voltages, currents, phase angles, and so on, is analyzed. It is inserted into the simulation to obtain faulty data to train the model.
( 3 ) The characteristics of the electric motor and the inverter used in modeling and the simulation results have been brought up in the Table  1. The practical tests of the engine of the EV made it possible to obtain the engine parameters which have given in Table  2. The characteristic curves provided by the manufacturer at the output of the electric motor are illustrated in Figure  4. These characteristics are used in the simulation. This allows us to apply the FD method in a real context. With deep learning and data analytics, monitoring can be more effective for maintenance. Data analytics is the computational procedure for determining the relations between variables using methods such as database, clustering, ML, regression, and classification. However, because of the numerous sources, the collected data sets might be of different levels of quality in terms of redundancy and consistency.
The application of data analytics in EVs aims to extract useful knowledge from sensor data that can be used for FD of EVs. In this part, a data-based FD model will be introduced in the form of simulation. The data set used to train the network is acquired from the simulation of the EV. Voltage and current measurements for both correct and faulty conditions are done. At the first step, large amounts of the data collected from sensors are reshaped to train and test our model. A data set consists of 150,000 current or voltage samples for each type of disturbance. The number of the data sets reaches 600,000. Eighty percent of the data sets are used as training sets. The next step is to develop a model using deep learning technique that is suitable for feature extraction from stored data. The created model can be tested by comparing the real-time measurements and the outputs of the model.
The successful model provides crucial information to overcome FD problems of the EV. Figure  5  shows the flow chart of the FD method. An LSTM network is composed of memory blocks called cells. It has two states: the cell states and the hidden states. The states are transferred between the cells. The cell state is the main chain of the data flow. As previously mentioned, a ML architecture like the LSTM network, which is a deep-learning-based model, and an ANN is designed by means of Matlab software. Figure  6  illustrates the basic structure of the LSTM model for the FD design of EVs. The LSTM network can solve long-term and short-term dependency problems. Figure  7  shows the layers of the LSTM. The hidden layer of the LSTM is called a memory cell which is the fundamental part of the LSTM  [36].
The probability of the dropout layer was set at 0. 5 which was a default for training. Different epochs like 100, 250, and 500 were tested. The errors during training were obtained: 3. 86 (12 min), 3. 82 (35 min), and 3. 63 (61 min). When the epochs were set bigger than 100, the training times were extremely increased, but the accuracy of the model did not increase very much. Thus, the epochs of training were adjusted at 100. Model parameters were set as shown in Tables  2  and 3. After training, the faulty and correct data sets obtained from the EV simulation were employed to validate the trained LSTM model. As presented in Figure  8 , an EV based on an electromechanical conversion chain is regarded as the power system model in this paper. Several faults are inserted into the system to validate the system's performance.
Several tests on this EV have been carried out to compare them to the specifications given by the manufacturer in Figure  4. The overall characteristics of the two engines in the measurements have been presented in Figure  10. As depicted in the figures, the results given by the manufacturer match those of the tests. Moreover, the different sensors connected to the EV provide different curves for the different driving cycles. These curves have been recorded in the vehicle microcomputer, and then anomalies in the different driving cycles can be detected. Neural-network-based FD methods, stochastic deep learning techniques, and deep architectures provide a multidirectional connection between neurons and are expected to increase the accuracy. Based on the available data and by designing FD methods based on deep learning, the FD architectures can be enhanced.
As shown in Figure  12 , the errors for the results are the lowest with an accuracy of 90. 29% in terms of MAPE. The differences between the observed and predicted data can be easily noticed in Figure  12. The ANN is less effective in modeling and has computational problems such as slow convergence and local minimum which cause low accuracy as against the LSTM. The high accuracy achieved by the LSTM model is due to its capability to detect time-series data which are natural in the EVs' data sets. The LSTM network controls the instantaneous information impact on the sensor data by adding memory cells helping the network save and transmit information over a long period of time. Unlike traditional models, the proposed LSTM network can achieve better performance because of the two-dimensional network which is composed of many memory units.
Conclusions and Future Work This paper discussed FD methods in EVs and introduced a particular FD method for the induction motor of an EV to detect and diagnose the short-circuit and open-circuit faults. The deep learning method successfully extracted the features from the sensor data collected from the EV in practice and simulation. The model proposed in this paper applied the LSTM. The improved LSTM network based on deep learning was employed to diagnose the electrical faults and eliminated the local minimum problems in traditional diagnostics methods. The simulation and experimental results validated the viability of the proposed method in diagnosing multiple types of electrical faults in EVs. It was proven that the deep-learning-based FD models such as LSTM networks are much more accurate than traditional FD methods.
The validated libraries (Agilent BioAnalyser 2100, Agilent Technologies, Santa Clara, CA, USA) were pooled (8pM) and sequenced (100 bp paired-end run with 1 lane of an Illumina HiSeq2000 flow cell v3). Raw data BCL files were converted to FASTQ files and quality information assessed. (library preparation and sequencing was performed by Source BioScience, Nottingham, UK).
Assembly, mapping, annotation and differential expression Raw reads were quality trimmed using Trimmomatic  (Bolger et al. , 2014, v. 0. 32)  using the following settings: Illuminaclip 2:30:10, leading = 3, trailing = 3 sliding window = 4 bp, quality = 15, minimum length = 36 bp. The trimmed reads were assembled using Trinity  (Grabherr et al. , 2011, v. trinityrnaseq_r20140717 ), using the default parameters for paired-end reads with 'read normalisation', before assembled contigs were annotated using the Trinotate pipeline  (Bryant et al. , 2017)  and a quantitative measure of transcriptome completeness was performed with BUSCO  (Kriventseva et al. , 2015) (v. 3, odb10  metazoa gene set). Reads and assembled transcriptome were deposited in NCBI: Bioproject No. PRJNA627092. Reads associated with the control and exposed samples were mapped to the transcriptome assembly  [RSEM, v.
1. 2. 13 (Li and Dewey, 2011) ]. The original counts were normalised with weighted trimmed mean of M-values (TMM) to calculate relative expression levels. As the RNASeq has no biological replication, differentially expressed transcripts were calculated using the function exactTest implemented in edgeR  [v. 3. 16. 5 (Robinson et al. , 2010) ] with a common dispersion factor of 0. 1. This associated a p-value with each contig sequence. The cut-off for putatively differentially expressed genes was selected experimentally using a qPCR screen. Bio-logical and molecular function analyses were performed using Panther  (Thomas et al. , 2003) (v. 13. 1). Enriched Gene Ontology (GO) and keyword terms associated with putatively upregulated genes were determined using DAVID  (Huang et al. , 2009)  (v.
6. 7) using the following parameters: a medium classification stringency, similarity term overlap 3, similarity threshold of 0. 50, initial group membership 3, final group membership of 3, and multiple linkage threshold of 0. 50. The GO terms associated with putatively up and downregulated genes was also analysed using ReviGO  (Supek et al. , 2011)  and GOnet  (Pomaznoy et al. , 2018)  software. For the ReviGO analysis, scored GO term lists were input (small similarity [0. 5] with a SimRel semantic similarity measure). Specifically, separate scored GO term lists were created for upregulated and downregulated genes by summing fold-change levels linked to the contigs contributing to each list. GOnet analyses were performed using the log fold change values and their associated Uniprot accessions.
Contig sequences representing candidate reference genes (Elong-like, GAPDH-like, beta-actin-like, alpha-tubulin-like and Ubiquitin-like) and differentially expressed genes (Unk1-like, Vtg-like1, Actin-like, Calbind-like, GST-Omega-like, Unk2-like and GST-Mu-like) were used in conjunction with Primer3 software  (Rozen and Skaletsky, 1999) (v. 4. 1. 0) to produce suitable primer sets for qPCR (Table  S2 ). Porewater 2. 2 ± 0. 7 1. 5 ± 0. 5 1. 6 ± 0. 5 3. 6 ± 0. 9 3. 8 ± 1. 5 Tissue 61 ± 7. 7 108 ± 7. 2 98 ± 20 88 ± 5. 9 94 ± 6. 4.
Screening for biomarkers using GO term enrichment Using the qPCR-determined cut-off, we created contig lists representing genes with apparent differential expression (DE) in exposed animals. Of the 696 and 141 contigs representing up and downregulated genes respectively, 371 and 114 could be annotated via BLAST analysis against the Uniprot database (E-value cut-off ≤1e -10 ). The lists of Uniprot accessions were used to perform multiple analyses. Firstly, enriched Gene Ontology (GO) terms (separated into Biological Processes [BPs], Cell Components [CCs] and Molecular Functions [MFs]) associated with the accessions were determined using DAVID  (Huang et al. , 2009) (Figs. S4 and S5). In addition, the GO terms associated with up and downregulated genes were scored by summing fold-change levels linked to all contributing contigs.
This scored GO term list was then analysed using both ReviGO  (Supek et al. , 2011) (Figs. 2 and S6)  to reveal highly expressed GOterms and GOnet software (Pomaznoyetal. , 2018) (Figs. S7 and S8) to reveal their various relationships. The analysis of upregulated genes reveals clear enrichment of GO terms associated with cilia function (Figs. 2, S4, S7 and S8). These are largely represented by a suite of axonemal dynein genes, with a broad range of cilia-associated genes representing the remaining GO term 'cilium'. The modest enrichment of the KOG functional group 'cell motility' in the metal-exposed polychaete Ophelina  (Neave et al. , 2012)  suggests a somewhat similar response may occur across polychaetes. Assuming the modified cytoskeletal gene expression is adaptive, it is possible to speculate on its functional role.
, 2016). Further evidence comes from the polychaete Amphitrite ornata that possesses a dehaloperoxidase, evolved from an oxygen carrying haemoglobin, capable of breaking down volatile metabolites secreted as repellents by organisms cohabiting the same ecosystem  (Lebioda et al. , 1999; Barrios et al. , 2014). Overall, the use of modified Hbs by polychaetes to mitigate environmental stress may be more of a rule than an exception. To determine if the Hb induction is as conserved as suggested by the A. virens and Ophelina data, the upregulation of Hb subunits and linkers needs to be confirmed in other polychaete species. Also, Hb induction needs to be measured following exposure to specific metals and a range of other stressors, as such data should reveal insights into whether the induced subunits are critical to mitigating the specific effects of metal exposure, or play more general roles in stress response.
Flow cytometry Frequencies of human hematopoietic cells in humanized mice blood or splenocytes were determined with a cocktail of antibodies directed against mouse CD45-VioBlue, huCD3-APC, huCD19-PE-Vio770, huCD20-PE-Vio770, huCD45-VioGreen, hCD27-VioGreen, hIgD-VioBlue and hIgM-APC (all from Miltenyi Biotec). Briefly, cells were resuspended in PBS containing 2% FCS and incubated with an optimal dilution of fluorochrome-conjugated antibodies for 30 min after FcR blocking (Miltenyi Biotec) before being washed twice in PBS containing 2% FCS. Data were acquired on the FACSCanto-II (BD Biosciences) and analyzed with the FlowLogic™ software.