In summary, the most important issues here are:

• The Aggregate’s main task is to protect invariants (business rules, the boundary of immediate consistency)
• In a multi-threaded environment, when multiple threads are running simultaneously on the same Aggregate, a business rule may be broken
• A way to solve concurrency conflicts is to use Pessimistic or Optimistic concurrency techniques
• Pessimistic Concurrency involves the use of a database transaction and a locking mechanism. In this way, requests are processed one after the other, so basically concurrency is lost and it can lead to deadlocks.
• Optimistic Concurrency technique is based on versioning database records and checking whether the previously loaded version has not been changed by another thread.
• Entity Framework Core supports Optimistic Concurrency. Pessimistic Concurrency is not supported
• The Aggregate must always be treated and versioned as a single unit
• Domain events are an indicator, that state was changed so Aggregate version should be changed as well 

public class AggregateRootBase : Entity, IAggregateRoot
{
    private int _versionId;

    public void IncreaseVersion()
    {
        _versionId++;
    }
}

internal sealed class OrderEntityTypeConfiguration : IEntityTypeConfiguration<Order>
{
    public void Configure(EntityTypeBuilder<Order> builder)
    {
        builder.Property("_versionId").HasColumnName("VersionId").IsConcurrencyToken();
 
        //...
    }
}

var order = await _ordersContext.Orders.FindAsync(orderId);
order.AddOrderLine(request.ProductCode); 
var domainEvents = DomainEventsHelper.GetAllDomainEvents(order);
if (domainEvents.Any())
{
    order.IncreaseVersion();
}
await _ordersContext.SaveChangesAsync();

Support Vector Machines (SVMs) are some of the most performant off-the-shelf, supervised machine-learning algorithms. In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problem-solving algorithms. He also includes numerous code examples and a lengthy bibliography for further study. By the end of the book, SVMs should be an important tool in the reader’s machine-learning toolbox.