dataesr/scientific-paragraphs-categorization huggingface dataset:
T h es t o c k p i l ea n dt h e i ru n i q u ec o m p onent ID can be invoked and identified at any stage of their requirement in real-time. For this analysis and categorisation, AAM uses R packages, and the dataset is analyzed for feature extraction using automotive software tools  (Manimuthu et al. 2021). Key findings from this stage include: (1) Fault identification (2) Component Classification (3) MappingoftoolsandDeviceswiththeirsuppliers (4) Product policy identification (5) Stocks and Storage range and capacity index (6) Market cost and Energy consumed during Unloading and storage in dedicated facilities From this stage, the raw materials are assigned and model with their specific SC type based on company requirement, market trend, and investors interest. Some primary SC types include Smart Legal contract, Decentralized Autonomous Organization (DAO) contract, Application logic Contract (ALC).
Smart legal contract (SLC) One of the commonly used blockchain elements where all the elements, tools, and software investors and stakeholders are legally merged under one common agreementsaysprofitorlossneedstobesharedlegallyunder accepted terms and conditions. This SC covers software, tools, and hardware, and the industry's data centre for future references. Trust among all involved parties is ensured using SC. Market reliability, stock value predictions, and product commercial value evaluations are accountable and shared by all the investors using SLC. Easily accessible machine-level SC assists in tying the consumer market with industries without any external brokerages. Energy wastages calculation uses a digital IoT environment. Most SMEs use this blockchain in their product procurement, delivery, and market stock analysis.
Application logic contract (ALC) SC assists in using IoT devices for data collection, processing, analysis, and decision-making in automobile assembly and delivery. Most of the tools and methods used in the AAM involve many application-specific IoT devices, support vectors, classifiers, and federated learning models. ALC helps bridge gaps in programming tools, system design software, and industrial standards for assembly and production processes. ALC applies to d e s i g na n dm o d e l l i n g. A l lt h ea c t i v ec o m p o n e n t s ,i r r espective of manufacturers and process, can be brought under a common umbrella of SC without additional policy formation. Many investors agree to get involved in the logical contract without scrutinising their design and development details. Since XYZ company comes under SME in Europe, the implementation and design process is much more flexible while using ALC as one of the SC.
Spare parts inspection and trust validation The pivotal stage is to classify different tools and components from manufacturers. During this stage, market valuation and the component's purchase cost are critically studied. Forecasted market value is quoted as the best market index during their training in realtime. All the datasets are trained and modelled using their unique ID and training vector classifiers. Database that holds the record about multiple components of the exact specifications using SC procedures. Their label and time tags are uniquely modelled, trained, and stored under their unique classifiers and support vectors  (Zheng et al. 2020 ). Once the automotive elements are classified based on different datasheets, market values and usages are modelled and trained using their support vectors and classifiers.
Thishelpsidentifyandeliminatethedataerrorsand accumulated processing errors during data reception and storage by different IoT devices installed at different critical industrial zones. Data play a critical role in TTL limit identification for every industry-grade tool and embedded component. Datasets with errors are processed separately rather than training with other error-free datasets. Further data normalisation shows the datacentric ability of ML in processing the information among different sections of the industry  (Pokhrel and Choi 2020). After classification and modelling, the components are mapped with their respective section based on usage requirements in the assembly process. In this stage, errors accumulated during the dataset training and evaluation are updated, and new entries are stored.
Sections involved in the XYZ company use this mapped dataset and their normalised values for smooth and easy identification of components from multiple manufacturers. This classification is based on their market valuation and stock listings as well. Grouping and logging take place once the trained values are available at respective data loggers. Irrespective of time, cost, and energy usage, all the elements are logged and categorised using their classifiers and support vectors in this stage. Since the next stage of vehicle design requires tools and components based on timing and assembly, these logged data are clustered together  (Manimuthu et al. 2021 ).
Product design and schematic verification Asallthedatasetsrequiredforcomponentassemblyand product structuring are readily available, the SC policy is checked before starting this process. During the process, almost all the assembly operations will be completed, and only the delivery of the final product will remain in the industry. Before starting this process, product schematics, as shown in Figure  3 , are critically analyzed and evaluated deeply to come with the process initiation. The schematics include the components assembly with reference to the different viewpoints. The references are shared with all the divisions and the sections for their correct fitting and design verification in detail. Crosssectional views help the design process more compact as the tools are categorised according to their requirements.
Robots take care of the assembly process using the embedded software, and R models help in data visualisation. Once verified and approved, this stage of production and assembly cannot be intervened by any of the embedded devices in the middle(Singh, Rathore, and Park 2020). Only the emergency halt operation can be p e r f o r m e d. S t e p sa n dp r o c e s s e si n v o l v e di nt h i ss t a g e include: (1) Fitting (  2 ) Assembly (3) Polishing (4) Testing (  5 ) Analysis (6) Verification In each section, ML values, trained classifiers elements, and the permitted storage level are recorded; So that there will not be any excess values in any sections. This helps to secure the system from any loss due to excess storage cost, and energy required to process and store the values. The product design schematics with all viewpoints, as shown in Figure  3 , are kept readily available for usage and shared as a key reference to assemble every individual component (O'Leary 2013).
Level 1: Data normalisation and feature extraction R packages and industry-grade smart system software are used in bringing the full features available from every c o m p o n e n ti n v o l v e di nt h ed e s i g n. T r a i n i n ga n de v a luation include stock lists, pricing values, and manufacturer support for smooth testing, loading, unloading, and information extraction. The process involves fact sheets and design information obtained from original vendors andsuppliers. Thisinformationisretrievedandmodelled using SVML, and classifiers are provided to each section for smooth identification  (Nofer et al. 2017 ). Each component is mapped and logged onto a local database for easy pick and place into different sections involved in the process. Investors and stakeholders are advised about the market price.
Thus, TTL and FAI help identify the best pricing agency for delivery and transportation in the commercial market. Table  6  shows the delivery agency list and their enterprise valuations like market cap, enterprise value, and delivery charges before and after implementing FAI and TTL in the workplace. Similarly, the performance index is evaluated for the dealers based on the revenue and gross sales. These data must be submitted as per the company policy and SC blockchain for insurance, maintenance, and market share calculation in real-time. Thus, all the agencies and dealers produce their total retail and commercial revenue data for FAI training and analysis. This helps to study and identify potential issues or threats or quality index and improve cost margin gain regarding distribution and   market limitations.
Quality and performance valuation, design analytics, and productivity enhancement constitute several possible sustainability solutions to withstand in any global industrial market. In addition to the additive manufacturing and process automations, the use of SC and IoT helps the industrial environment and active participants to obtain a detailed market response curated with the existing business practices. Data modelling, computational algorithm design, simulation, and schematic formations were again a big hurdle due to prevailing trust issues between stakeholders and investors. As the SC helps bridge this trust issue by policy formation and legal assistance, all the investors tend to have hassle-free investment plans in real-time. This design includes multiple procedures in terms of purchase, delivery, utility, and distribution.
All the data are modelled using SVML, and Federated Learning ML techniques as the data are effectively captured by the embedded IoT in the industry. Developed AAM shows the use cases of ML and IoT in fixing, evaluating, and processing all forms of data irrespective of sources without any deviation from their programmed functions. The programmed model uses a customised R programming scheme, and simulations are done using company-owned industry-grade licensing software. Thus, unbiased data is fed to the training, testing, and evaluation model at all assembly stages in real-time. Irrespective of industry, these methodologies can be implemented with a few minor customisations. Many industries, including leather, textile, biogas, fuel cells, and two-wheelers, can incubate these methods in their practices to achieve profitability in the short term.
Qualitative evaluation of every deliverable plays a significant role in design, development, and product assembly. Many techniques other than ML includes fuzzy logic; the neural network can explore SMEs based on their production cost and infrastructure capabilities. Prediction analytics and modelling with these techniques aim to offer better performance without considering any market investments. Thus, ultimately, improved production cost, commercial value gain, and market valuation become void in these industries. In some worst cases, the decision model lags in providing the required results as the dataset may not be sufficient to provide resultoriented decisions in real-time. Thus, flexible and robust infrastructure capable of emerging smart technologies in industrial practice is required in the automated industrial setup.
Trust Threshold Level for each element involved in the production process, 4. SVML and FAI for performance comparison, modelling, and analysis at various stages of design. In addition to these critical form factors, other methods used in training and processing data and stages include classification, assembly, fitting, polishing, testing, analysis, assembly, grouping, logging, and schematic verification. Data shared between different stages with the ML algorithms will extract the features and help in decision making. SC comes into the picture when the devices or equipment are observed of any faults or damages or malfunction during any stage of the assembly until the product is delivered into the commercial market. As this process is technically considered stable, the marketfluctuationalwaysprevailswhilefixingtheproducts' cost and commercial value.
Thus, in this AAM, a novel estimation technique called TTL is implemented in thedesignitself. Through this estimation scheme, all the products are modelled based on their cost, energy usage, market value, and profit margin of utilising them in the design. TTL has the following features that have helped the XYZ company to have a commercial profit margin of 13 percent within 60 days of their commercial vehicle sales. Feature includes: (1) Data modelling and component analysis (Vendors, suppliers, stocks) (2) Loading, Unloading, distribution, and usage valuation cost (3) Consumer satisfactory index (4) Market valuation and SC legal policies (5) Sale valuation and investors profit margin (6) Storages and stock listing data (7) Stock purchase valuation comparison (8) Market pricing vs. Purchase pricing analytics These data help fine-tune each component's threshold value after their potential utility during the assembly and delivery process.
Further, the TTL value helps identify and process every component's functional attribute with detailed identification, analysis, and market utility. Thus, it indirectly facilitates the firm to get more visibility andpositionitselfinthecompetitivecommercialvehicle market. Besides, FAI and TTL designed and implemented are unique and novel where both relatively bring sustainable industrial practices and solutions that any industry can quickly adapt in their workplace. The model developed, solutions provided, and dataset training functionalities are entirely customisable based on the industry requirement and infrastructure support. It will significantly contribute to all forms of supply chain practices and operations management with assured safety. Finally, the secured smart contract usages act as a backup to meet any legal grounds in real-time.
+Xn (2) Total number of samples recorded at the time 't' = Y (3) Number of clients involved in the same process = Z (4) Training dataset value for X at any time 't' = W(R, B) where W is the weight of a particular dataset. (5) Error accumulated during training = E Thus, during the training and while applying FAI, Teste d s ampling rate for comp onent X = n t=1 Y(Z) W(Rn, Bn).
g. , CIBERSORT)  (13, 14) , to quote a few. Since two decades, thousands of transcriptomics studies have been produced and were made freely available on public repositories (NCBI Gene Expression Omnibus (GEO), https://www. ncbi. nlm. nih. gov/gds, European Bioinformatics Institute's Array Express https://www. ebi. ac. uk/ arrayexpress/). Despite such significant improvements however, transcriptomics remained bulk and, by lacking the ultimate resolution of its single cells taken individually, was the mere arithmetic mean of its cell constituents. Bulk RNA studies in lymphoma patients proved that TME signatures strongly impacted prognosis in  (15, 16) , besides genetic lesions. Deconvolution of publicly available gene expression profiles (GEP) datasets from 480 DLBCL patients treated with R-CHOP (standard therapy frontline) has allowed to draw maps of TME in 2015 (CIBERSORT), through characterization of cell composition from their GEP (this method has been applied to 18,000 human tumors and correlated leukocytes subsets to survival across various cancers)  (14).
Today, current scRNA-Seq technologies measure about <2,000 genes per cell. Further standard pre-processing of the data includes a normalization of all read counts and a quality control (QC) in which cells with too few genes, genes in too few cells, dead cells, and cell doublets are discarded from the dataset. A first step of data processing consists in clustering cells according to their gene expression profile, providing the most coherent and data-driven analysis of a mixed sample. To this aim, a principal component analysis is first performed to reduce the large dimensionality of all transcriptomes to their first principle components (PC). Once these fewer dimensions are selected upon user's decision based on the wanted precision, clusters of cells with similar profiles are delineated under the same user's criteria: low granularity makes less clusters of very different cells while more granularity means more clusters of more closely related cell types.
Finally, the entire dataset is represented on bi-dimensional maps of cells, in which the above n first principle components are dimensionally reduced to two dimensions by sophisticated unsupervised algorithms such as t-distributed stochastic neighbor embedding (t-SNE). More recently, a superior method for both PCA and dimension reduction called uniform manifold approximation and projection (UMAP) has gained a large popularity for processing and visualization of scRNA-seq datasets  (17). Within t-SNE or UMAP representations of those datasets, clusters are typically shown with colors and define cell subsets with high fidelity to the data: all cells alike are plotted next to each other while different cells form separate clusters with or without continuity to the former (Figure  1 ). Those steps are now performed by skilled bioinformatics users applying either R scripts from algorithms such as Seurat  (18) , or the proprietary Chromium ™ (https://www.
Authors performed scRNA-Seq characterization of immune cells and assessed their spatial sub-localization out of 22 HD patients and five reactive lymph nodes. Transcriptome data from >100,000 cells and 1,200 genes in median identified 22 clusters, all being assignable to a cell type based on the published transcriptomes of sorted immune cells. No tumor specific cluster was found. But three regulatory T cells (Treg) clusters dominated the TME of HD. The cluster in HD cases with the highest proportion of immune cells was also enriched in LAG3 and CTLA transcripts, as opposed to controls where B cells and CD8+ T cells were enriched. Non-Treg CD4+ clusters also enriched in HD included Th2 and Th17 subsets. Treg CD4+ subsets in HD cases expressed GITR, CD25, not FOXP3 (and thus endowed with a type 1 Tr1 phenotype).
UPCOMING IMPROVEMENT TO UNRAVEL TME FROM scRNA-SEQ APPROACHES A major improvement has recently been brought by implementing scRNA-seq with use of DNA oligonucleotide-tagged antibodies (ADT), such as to integrate cell surface proteins together with transcriptome measurements. The resulting technique, called cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), analyses ADT-labeled cells by scRNA-seq to provide simultaneously both immuno-profiling and transcriptomics of the same single cells  (67). ADT-labeling enhances the identification of each cellular population present in the samples, and avoid further misinterpretation of results. Data can be processed with dedicated software, such as Single-Cell_Signature_Explorer and single cell virtual cytometer, the first published method for qualitative and scoring of single gene or gene-set based signatures  (68).
More recently multi-omics single cell strategies are focused to study genetic, epigenetic, phenotypic, and transcriptomic profiles within the same cell. As examples, SCI-seq (single cell combination marker) provides cell copy number variation  (71) , scATAC-seq could identifies specific chromatin motifs  (72) , RAGE-Seq (repertoire and gene expression by sequencing) identifies B or T cell repertoire  (73) , scNGS could provide information about somatic mutations  (1). All these approaches combined to new computational software should give huge knowledge of cellular heterogeneity, evolution, dynamics of lymphoma cells within their TME. Multi-omics (three techniques) evaluation of seven patients with chronic lymphocytic leukemia (CLL) exposed to ibrutinib has unraveled a consistent regulatory program of treatment-induced changes, but at a pace that varied among patients.
CONCLUSION To advance cancer research and resolve heterogeneity, we need integrated single cell multi-omics platforms to study cell-by-cell (tumor, immune, stroma) transcriptomes, proteomes, methylomes, cell surface proteins, localization within the tumor, and even future evolution through (pseudo-time analysis), across various patients' datasets. The information of the precise localization of normal immune versus tumor cells will be resolved in part through spatial transcriptomics. Thanks to newer bioinformatics tools, we need to understand the functional significance of the different clusters, better visualize them, and aggregate our data, from labs to labs all together, in a global effort of a multi-modal integration across generated datasets of tumors worldwide. These efforts will be paid back by enhanced risk stratification, disease monitoring, and personalized therapy in lymphomas, like a few studies in multiple myeloma and acute myeloid leukemia have already demonstrated.
of GC differentiation process between DZ/LZ), as well as the precursors of both memory B cells and antibody secreting cells (based on expression of CCR6 and PRDM1). Moreover, a gene classifier (selecting the 50 up-and down-regulated genes in the sc signatures associated with these GC subpopulations) was built and applied on bulk RNA-Seq expression profiles from two published DLBCL panels (481 fro NCI and 230 from BCA). Non-GC cases were scattered across the sc-RNA Seq classes (but mostly in late GC stages) while the majority of GC cases were related with normal GC B cells in the LZ (like early of intermediate stages). Interestingly, 12% of DLBCL cases in both datasets displayed DZ gene signatures despite distributing across GC/non-GC/unclassified tumors.
of GC differentiation process between DZ/LZ), as well as the precursors of both memory B cells and antibody secreting cells (based on expression of CCR6 and PRDM1). Moreover, a gene classifier (selecting the 50 up-and down-regulated genes in the sc signatures associated with these GC subpopulations) was built and applied on bulk RNA-Seq expression profiles from two published DLBCL panels (481 fro NCI and 230 from BCA). Non-GC cases were scattered across the sc-RNA Seq classes (but mostly in late GC stages) while the majority of GC cases were related with normal GC B cells in the LZ (like early of intermediate stages). Interestingly, 12% of DLBCL cases in both datasets displayed DZ gene signatures despite distributing across GC/non-GC/unclassified tumors. hi CD83 lo ), this algorithm indicated that 26% of the cells were misclassified, with an intermediate CXCR4/CD83 phenotype, proving that our vision of GC maturation was incorrect.
The theoretical uncertainty brought by the scale dependence leads us to introduce a new criterion on the perturbativity of the IDM. This criterion further delimits the allowed parameter space which we investigate carefully by including a host of constraints, both theoretical and experimental, including in particular, new data from the LHC. We come up with a set of benchmark points that cover three different mechanisms for a viable relic density of DM: i) a dominance of co-annihilation into a fermion pair, ii) annihilation into 2 vector bosons of which one is off-shell that requires the calculation of a 2 → 3 process, iii) annihilation that proceeds through the very narrow standard model Higgs resonance. Since the 2 → 3 vector boson channel features in all three channels and is essentially a build up on the simpler annihilation to OS vector bosons, we study the latter in detail in the present paper.
Automation of the calculation A new model file for the IDM allowing different schemes for the renormalisation of the model, to which we turn shortly, has been added to SloopS  [5, 54, [58] [59] [60] [61] [62] [63] [64]. SloopS, our automated code for the calculation of tree-level and one-loop observables relies of the bundle of packages based on FeynArts  [65] , FormCalc  [66]  and LoopTools  [67] , that we refer to as FFL for short. A few improvements to LoopTools  [58]  have been made over the years. A key component is the generation of the model file (with counterterms and renormalisation conditions) made possible with LANHEP  [68, 69]  judiciously interfaced with the bundle FFL. We have exploited this approach successfully for the SM, the full renormalisation of all sectors of the MSSM  [54, 59, 62]  and the NMSSM  [64].