dataesr/scientific-paragraphs-categorization huggingface dataset:
The irrigation module This IDSS is designed to provide agricultural experts and end users with a tool to manage irrigation in terms of quantity and timing of water supply. The logic on which this system is based is to provide water at the right time and in sufficient quantity to minimise water stress to crops. In this context, three methods have been developed to determine the water requirement and thus trigger irrigation, namely: i) water supply when the APAW is below a certain SAW (Dsoil, mm); ii) water supply when the crop water consumption exceeds a certain threshold (Dplant, mm) and: iii) water supply based on fuzzy logic applied to Wreli (Irrfuz). With Dsoil, irrigation is triggered as: 𝐼𝑟𝑟 𝐷𝑠𝑜𝑖𝑙𝑖 = 𝑆𝐴𝑊 𝑖 -(𝐴𝑃𝐴𝑊 𝑖 -𝑆𝐴𝑊 𝑊𝑃𝑖 ) 𝑖𝑓 ∑ 𝐴𝑃𝐴𝑊 𝑖 - 𝑛 𝑖=𝑑𝑎𝑦 𝑆𝐴𝑊 𝑊𝑃𝑖 < 𝑆𝐴𝑊 𝑖 * 𝐷𝑠𝑜𝑖𝑙 𝑖𝑛𝑓 (22) where: IrrDsoili is the total amount of water with irrigation (mm) on day i and Dsoilinf is the proportion (0-1) of SAWi under which IDSS provides irrigation.
The field test bed The framework was calibrated and tested on filed experiments carried out over three years  (2002, 2003 and 2004)  at the CREA-AA experimental farm " Pod. 124" located in Foggia (lat. 41° 8' 7'' N; long. 15° 83' 5'' E, alt. 90 m a. s. l. ), Southern Italy. Daily weather data used (maximum and minimum temperatures, rainfall and solar radiation) were collected by the local meteorological station. The soil is a silty-clay, vertisol of alluvial origin, Typic Calcixeret (Soil Taxonomy 10th ed. , USDA 2010), with the following characteristics: organic matter, 2. 1%; total N, 0. 122%; NaHCO3 extractable P, 41 ppm; NH4O Ac-extractable K2O, 1598 ppm; pH (water), 8. 3; field capacity water content, 0. 396 m 3 m -3 ; permanent wilting point water content, 0. 195 m 3 m -3 , available soil water, 202 mm m -1.
1). Despite these disparities, the framework aligned CCadj with the experimental dataset (2004 growing season, Fig. 2a ), due to the adverse effects of water and temperature stress on potential canopy development and crop resilience to water stress conditions (Fig. 2b ). Temperature stress occurred at the beginning of the growing period and on some days during the last part of the growing season period (Fig. 2b ). During the first month of the growing period, the average air temperature fell well below the optimal temperature (25°C), with values around 14°C in the first part of the growth phase, causing Tlim to reach values equal to 0. 4 and more than halving the potential development of the canopy. On some days in August, the average air temperature exceeded 30 °C (Tx threshold) causing the highest temperature stress which then reached the value of 0, i.
This produces a cascade effect on figures that the IDSS formalizes afterward, severely penalizing the accuracy of the system and reducing its reliability. Here, if an adjustment is made to the time and/or amount of water supply, the IDSS reshapes all aspects related to soil water dynamics, water consumption, canopy development, changed water demand, and irrigation scheduling for a longer time frame, the farther the forecast climate dataset is shifted. Adjustments can also be applied to aspects concerning the crop growth dynamic. If the user has actual data on the crop's leaf area index (LAI) and/or total dry matter (TDM), they can be forced into the figures generated by the framework. Based on this, the system will redesign the new scenario, and the irrigation management will be re-modeled accordingly.
2008)  Act as low-level layer between different system components (NLG, Language generation, 3D module, question analyzer)  (Bentivoglio et al. 2010)  Split different domains by domain-expert agents  (Calvaresi et al. 2019)  Provide user profiling capabilities and the possibility to share knowledge between user agents  (Zolitschka 2020)  Orchestration framework should be MAS-based, reusable, scalable, and provide topic-specific agents  (Calbimonte et al. 2019)  System scalability  (Shashaj et al. 2019)  Robust, powerful, and flexible enough to easily adjust to any business context  (Calvaresi et al. 2021a)  Store personal data in a GDPR-compliant and consent-based database where the user has control  (Maher and Gu 2002)  User-centered virtual architecture should change the current state of designing virtual architectures  (Mori et al.
For example,  (Jiang et al. 2015; Augello et al. 2011)  deal with the integration of different static sources (i. e. , vector space model-based indices, XML, relational databases, SPARQL queries, and AIML), while  (Pilato et al. 2011; Tarau and Figa 2004 ) are intended to manage knowledge dynamically based on the current dialogue context. While the studies mentioned above are in a user-to-single agent scope, a few studies are in the user to multi-agents (i. e. , chatbots) scope. For example,  (de Bayser et al. 2017 (de Bayser et al. , 2018) )  address the coordination of multiple bots providing financial advice within the same chat. Their final goal is the moderation of the user-bots' interaction. Finally,  (Calvaresi et al. 2021a ) focused, among other aspects, on the facets of data protection and data privacy.
Batista et al. 2009 ), and (Bentivoglio et al. 2010)  rely on JADE and (Hettige and K. 2015) implemented the system based on MaSMT. Although not relying on a pre-existing MAS framework,  (Pilato et al. 2007 ) and  (Tarau and Figa 2004)  implemented their own ad-hoc Java-based systems. Moreover, Python-based back ends are 9. 7% of the total. In particular,  (Jiang et al. 2015)  and  (Calvaresi et al. 2019 ) have developed ad hoc systems, while  (Calvaresi et al. 2021a ) rely on the SPADE framework. 16  Several studies (9. 7%) relied on existing proprietary systems. For example,  (Kalia et al. 2017 ) and  (Angara et al. 2017 )) rely on IBM Watson's Conversation Platform 17 and (Zolitschka 2020) rely on Aimpulse Spectrum. 18  A number of studies (9. 7%) developed their systems' back end as ad-hoc solution using JavaScript (i.
The analysis of the technologies' distribution within the primary studies suggests some trends to be observed. Figure  17a  shows the back-end technologies used over the years. It is possible to notice that Java-based systems have been used extensively. However, since 2015, Python-based systems have emerged, likely due to Python's prevalence in areas such as machine learning and data science libraries. Moreover, since 2017, the employment of proprietary systems (e. g. , IBM Watson) has been increasingly considered. Although initially rather rudimentary, such platforms now offer a wide range of possibilities, such as integrating machine learning modules or extensive analytical capacities. Figure  17b  shows that a shift occurred in the area of front-end technologies too. In addition to the increasing prevalence of web-based solutions, messaging services such as Facebook Messenger or Telegram have become increasingly popular since 2015.
Nevertheless, in recent years, the use of cross-platform frameworks became a consistent practice. Cross-platform frameworks such as Ionic or Flutter make it possible to develop front-end solutions for mobile phones and web browsers using a single code base. Moreover, it can be observed a trend to use more complex multi-agent chatbots (e. g. ,  (Bosse. 2021 )) to blend in IoT and micro-services domain with highly scalable multi-agent chatbot networks. Most studies have used MAS enabling agents to abstract individual components such as language processing or output composition. (Calvaresi et al. 2019 ) and  (Calvaresi et al. 2021a ) have taken a different approach by coupling   (Kalia et al. 2017)  Implement tooling to guide users through the methodology  (Angara et al. 2017)  Connect Foodie to smart appliances and smart services, integrate it into digital assistants (e.
(Vasconcelos et al. 2017)  Add similarity metrics (e. g. perplexity and distance measures) to account for partially correct answers  (Jiang et al. 2015)  Implement new plug-ins to support other types of knowledge sources. Enhance currently implemented plug-ins to fully support the specific knowledge sources  (Memon et al. 2018)  Implement a graphical user interface. Add Natural Language Understanding (NLU)/NLP, so the system can understand spoken language and translate it into text. Develop a multi-party chatbot system supporting emojis, animations etc (Alencar and Netto 2014) Create new intelligent agents that monitor other activities. Improve avatar gestures  (Tarau and Figa 2004)  Find creative uses for the new Google metasearch API  (Huang et al. 2008)  Develop deliberate phase with internal context state instead of current simple AIML script executor.
Acknowledgements The work is supported by:  ◇   HES-SO RCSO ISNet PERSA  project:  Computational Persuasion in eHealth Support Applications. ◇ Chist-Era  grant  CHIST-ERA19-XAI-005 , and by (i) the  Swiss National Science Foundation  (G. A. 20CH21\_195530 ), (ii) the  Italian Ministry for Universities and Research,  (iii) the  Luxembourg National Research Fund  (G. A. INTER/CHIST/19/14589586 ), (iv) the  Scientific, and Research Council of Turkey (TÜBİTAK , G. A. 120N680 ) ◇ The authors thank  Diego Collarana  from  Universidad Privada Boliviana  for his insights. This collaboration was supported by the  Research Partnership Grant   RPG2106  funded by the  Swiss Leading House for Latin America.
version of ALICE: https:// github. com/ noelb ushxx/ progr amd. 2 https:// www. apple. com/ siri/. 3 https:// devel opers. google. com/ events/ io/ 2012. 4  Now called Google Assistant: https:// assis tant. google. com/. 5 https:// www. micro soft. com/ en-us/ corta na.
Ms. FPOP R package The dynamic programming and functional pruning procedures describe in the algorithm 1 are implemented in C++. The input and output operations are interfaced with the R programming language thanks to the Rcpp R package. The main function MsFPOP() takes as input the sequence of obervations, a vector of weights for these obervations, the parameters β and α of the multiscale penalty. The function returns the set of optimal changepoints in the sense of (4). Analogously, we implemented a version of the PELT algorithm, MsPELT(), that optimizes (4).
iLOVECLIM model We use the iLOVECLIM model, which is a code fork and evolution of the LOVECLIM model  (Goosse et al. , 2010). The iLOVECLIM model is an intermediate complexity model which shares the same atmosphere, ocean, sea ice and terrestrial biosphere components as LOVECLIM. The ocean grid has a 3 • × 3 • resolution with 20 irregular vertical levels, while the atmosphere is on a T21 grid with 3 vertical levels. It can simulate ∼ 1000 yr d -1 , making it very suitable for computing long-term climate changes such as the work undertaken here. To run simulations of the last deglaciation following the PMIP4 protocol, we have modified the code to be able to change the bathymetry and coastline regularly, and we have added the routing of freshwater from ice sheet melt to the ocean. We have also generated the ice sheet topography, ice sheet mask, bathymetry and land-sea mask files on the iLOVECLIM grid using the ICE-6G_C or GLAC-1D reconstructions.
, 2018). In most past studies of deglacial simulations (apart from  Kapsch et al. , 2022 and Ivanovic et al. , 2018) , freshwater fluxes were prescribed and not computed from the ice sheet melting. In addition, in most previous studies, the areas where freshwater flux is added are prescribed. In this study, the freshwater flux is computed from the ice sheet volume change and routed towards the ocean following the topography. In Fig. 14 , we compare the freshwater fluxes used in those simulations. In most model studies, the freshwater flux is much smaller and/or with a different timing compared to the ones computed from the ICE-6G_C and GLAC-1D reconstructions. This leads to a smaller evolution of sea level equivalent than in the reconstructions (Fig. 17 ). These models obtained a similar evolution of AMOC compared to proxy data and accordingly a relatively good evolution of temperature in Greenland, but the prescribed freshwater flux is in disagreement with the currently available ice sheet reconstructions.
Data availability. The source data of the figures presented in the main text of the paper are available on the Zenodo repository with https://doi. org/10. 5281/zenodo. 7857361 (Bouttes et al. , 2023). Author contributions. NB, FL, DR and DP developed the model code for bathymetry changes. NB, AQ and DR developed the model code for freshwater fluxes. NB performed the simulations. NB analysed the results and prepared the figures. NB wrote the manuscript with contributions from all co-authors.
Use of industry-available, manufacturable materials for the thorax The objective was to incorporate material properties of readily available, manufacturable materials into the code of the simplified FE model to accurately replicate the behavior of the human thorax and construct the physical twin of SurHUByx FEM (SurHUByx). To achieve this, a reverse engineering method was used, as detailed in Figure  3. First, a manufacturable material available in the industry was mechanically tested in tension or compression, and its response was analyzed. Due to slippage issues, very soft materials were tested in compression, while harder materials were tested in tension. For compression tests, data were obtained directly from the compression machine. The local displacement of tension samples was measured using Digital Image Correlation (DIC).
Model validation Biomechanical corridors established by  Bir et al. (Bir et al. , 2004)  were used to evaluate the consistency of the model. These corridors were established from tests on 13 Post Mortem Human Subjects (PMHS) impacted over the sternum by different rigid projectiles at different velocities. Three impact cases were conducted: Case A with a 140 g projectile fired at 20 m/s, Case B (140g -40 m/s), and Case C (30g -60 m/s). In order to have quantitative data to compare within these biomechanical corridors, numerical replications of these physical tests were performed: an initial velocity was applied to the FE model of the impactor that impacted the SurHUByx FEM in a manner similar to the experimental tests. Finally, a comparison was made between the experimental and numerical thorax responses in terms of force-time and deflection-time curves and Vc max values over the three impact conditions.
mulatta, rheMacS) genome using the LASTZ program  (27)  and then merged all of them into multiple genome alignments with MULTIZ  (28). These genomes showed high collinearity, with the aligned base pairs spanning more than 2. 7 Gb or 90% of the reference assembly (fig. S1  and table  S5 ). To deduce the correct branching pattern of the sequenced macaque species and to characterize the heterogeneous phylogenetic signals across genomes, we partitioned the genome alignments into nonoverlapping windows of 50 kb and then performed maximum likelihood (ML) analyses for each window sequence. We initially obtained the multispecies coalescent-based species tree (ASTRAL and STAR; fig. S3 ) from windows with high bootstrap values (e. g. , mean value of >80%). Although the topologies obtained from these two methods were mutually consistent and largely in accord with recent studies  (17, 21) , we observed substantial genealogical discordance within and between species groups among all window trees (n = 7392 alternative topologies; Fig.
Genome sequencing, assembly, and annotation The 10 newly assembled macaque genomes were sequenced on different platforms due to the protracted process of collecting tissue samples (see table  S1  for details). We used Supernova (version 2. 0. 0, 10X Genomics Inc. , Pleasanton, CA, USA) with default parameters to assemble the reads generated from the stLFR and 10X Genomics Chromium reads platforms. For the Nanopore long reads, we first performed self-error correction for all Nanopore long reads using NextDenovo software (v2. 4. 0) and then assembled them into contigs using wtdbg-1. 2. 8  (57). The raw assemblies were further polished by Illumina short reads in Pilon v 1. 22  (58)  three times under the default settings. The completeness of the new macaque genomes was assessed by BUSCO (v3. 0.
Whole-genome alignments We first performed pairwise whole-genome alignments against Chinese rhesus macaque (M. mulatta, rheMacS) using the LASTZ program (  27 ) (v 1. 04. 03) under the following parameters: K = 4500, l = 3000, Y = 15,000, E = 150, H = 2000, O = 600, T = 2. The original alignments were then processed by the chainNet package  (64)  to generate the reciprocal best net alignment (default parameters, except for axtChain where we used "-minScore = 5,000 -linearGap = medium"). Subsequently, we used the "maf-swap" (65) command (LAST software) to sort the alignment results and obtain the optimal pairwise synteny blocks between genomes. Last, we used MULTIZ (v 11. 2)  (28)  to merge all pairwise alignments into multiple genome alignments using rheMacS as the reference.
Phylogenetic analysis Sliding window tree and signal of conflict On the basis of the multiple genome alignment, we used scripts provided in  (66)  to partition the alignment (20 autosomes and one X chromosome) into nonoverlapping windows of 50 kb without considering the protein-coding content. Meanwhile, we excluded windows that contained more than 10% gaps or hard-masked repeat sequences. For each window of the alignment, an ML tree was constructed using RAxML v8. 1. 15 (67)  with the GTRGAMMA model and rapid bootstrapping for 100 replicates and specifying P. hamadryas as the outgroup. The number of alternative tree topologies and their relative frequency were categorized by PhyBin v0. 3 program  (68). Conflicts between the gene trees and their frequencies were summarized using the CONSENSE program in PHYLIP v3.
697 package  (69). The coalescent species-tree was estimated from the aforementioned sliding window trees by two summary coalescent-based methods, ASTRAL  (70)  and STAR  (71). Because the major disagreement among all topologies concerned the relative placement of the fascicularis group (Fig. 1, B  and C ), for the sake of simplicity, we pruned the trees so as to include only five ingroup species: M. sylvanus, M. thibetana, M. silenus, M. nigra, and M. mulatta. These five species represented the sylvanus group, sinica group, silenus group (E), silenus group (W), and fascicularis group, respectively, and were selected on the basis of their longest scaffold N50 size. Similarly, we used PhyBin v0. 3  (68)  to estimate the categories of alternative topologies and their relative frequencies. We also evaluated smaller (20 kb) and larger (100 kb) window sizes, but this did not change our results materially.
Mitochondrial genome tree The mitochondrial genome sequence of each species was obtained from Illumina short reads using NOVOplasty 2. 4 (72). K-mer was set to 33, and the mitogenome of M. sylvanus (AJ309865) downloaded from the National Center for Biotechnology Information (NCBI) database was used as a starting reference. The reliability of mitochondrial contig assemblies was further validated via BLAST searches against the reference mitogenome (AJ309865). The D-loop and all transfer RNA (tRNA) genes were not used owing to their high mutation rate and high rate of loss. Proteincoding genes were translated into amino acid sequences to ensure open reading frames and to avoid NUMTs (nuclear mitochondrial DNA segments). An additional 18 macaque mitochondrial genome sequences from NCBI were added to expand our datasets and to further validate our sample identification.
Y chromosome phylogeny We confined our analyses to five Y-linked genes (TSPY10, SRY, ZFY, USP9Y, and RPS4Y1) owing to the substantial technical challenges presented by the sequence alignment of repetitive sequence regions of the Y chromosome. Seven of our genome assemblies were from males. To expand our dataset, we generated whole-genome shotgun sequences of two male samples from M. tonkeana and M. sylvanus, and further downloaded one male sample from M. nemestrina (SRR5947292). The Illumina short reads of each species were mapped to the Chinese rhesus macaque genome (rheMacS) using BWA-MEM v0. 7. 12 (74) with the default settings. After obtaining the bam files, we used ANGSD  (75)  to obtain the consensus sequence of these genes with the following filtering parameters: -b bamfiles. txt -minQ 20 -minMapQ 20 -remove_bads -uniqueOnly -rf region.
Phylogenetic signal relative to the local recombination rate No recombination map is currently available for the reference genome of the Chinese rhesus macaque (rheMacS). To construct this, we used the liftover tool  (76)  to convert the latest fine-scale linkage map of Indian rhesus macaque (rheMac8)  (77). First, the genome of rheMac8 was aligned to that of rheMacS using the LASTZ program v1. 04. 03  (27)  with the same parameters mentioned for the genome alignments. After obtaining the chain file, which records the links of the reciprocal best orthologous regions of the genome, we transferred the coordinates of the recombination map of rheMac8 to rheMacS coordinates using the program liftover  (76). Only the successfully derived liftover positions (~96. 3%) were used in downstream analyses.
Hybridization and introgression analyses Although ILS certainly underpins some aspects of phylogenetic discordance within and between clades, it is unlikely to be the only explanation for the notable differences we observed across all window trees. Therefore, we used several different methods to test for hybridization and introgression events in the presence of ILS. We first used the InferNetwork_MPL program in PhyloNet  (37)  to reconstruct optimal phylogenetic networks between all macaque species. Owing to the extensive computational requirements, we performed this analysis on 2000 topologies that were randomly selected and had a mean bootstrap support threshold of more than 80%. Three independent analyses were performed assuming reticulation scenarios ranging from 1 to 3, and each analysis executed 100 runs.
We first assessed whether ancient hybridization events had occurred between species groups (sylvanus group, silenus group, sinica group, and fascicularis group) using the "run_hyde. py" script. P. hamadryas was used as an outgroup, and sites with missing/ambiguous bases were ignored (--ignore_amb_sites). Next, an analysis at the individual level was performed using the "individual_hyde. py" script to detect hybridization in individuals within species groups that had significant levels of admixture. Last, bootstrap resampling (500 replicates in "bootstrap_hyde. py" script) was performed on individuals within the hybrid group to obtain a distribution of gamma values to assess heterogeneity in levels of gene flow. The tract size of two parental ancestry was inferred by LOTER  (36). The genomic distributions of each ancestry were calculated using custom R and Perl scripts.
Because HyDe is a powerful method with which to identify hybrids when the parental contribution is symmetrical, we further applied D-statistic analysis (also known as the ABBA-BABA test)  (79)  to explore broader introgression patterns between lineages and species. We performed these analyses in Dsuite package  (80) , which can estimate the D statistics across all possible triplets at one time based on the user-specified topology and can also assign gene flow to specific, possibly internal, branches. Our former steps suggested that the fascicularis group was of hybrid origin; thus, two bifurcating topologies could be used: one supported the sister relationship between the fascicularis and sinica groups (fig. S5A ), whereas the other supported the sister relationship between the fascicularis and silenus groups (fig.