Abstract: AI neural networks are integrated with distributed ledger technology (DLT), including the integration of decentralized oracles, to enhance the functionality, security, and efficiency of systems. The integration incorporates several components and methods comprising novel neural network architectures, data preprocessing techniques, DLT integration approaches, privacy and security measures, consensus mechanisms, and others. The invention enables the development of neural smart contracts, neural consensus algorithms, dynamic neural network training, neural network optimization techniques, hybrid DLT architectures, neural network interpretability methods, and reinforcement learning for neural smart contract execution. By integrating decentralized oracles into the system, the invention has the capability to further enhance the reliability, accuracy, and diversity of data used within the DLT, improving transaction validation, consensus mechanisms, and overall system performance.

Abstract: In some examples, a service provider may determine from a shift schedule that a first employee is scheduled to work a particular shift at a business within a threshold period of time. The service provider may determine, based on a location of a first mobile device of the first employee, that the first employee is unlikely to arrive at the business in time for the start of the particular shift. In response, the service provider may find a replacement employee to fill the shift of the first employee. For instance, the service provider may send the communication to a second mobile device associated with a second employee that is determined to be within a threshold distance of the business. Alternatively, if the service provider determines that the business is overstaffed on a particular day, the service provider may offer a selected employee an incentive to depart work early.

Abstract: In some examples, a service provider may determine from a shift schedule that a first employee is scheduled to work a particular shift at a business within a threshold period of time. The service provider may determine, based on a location of a first mobile device of the first employee, that the first employee is unlikely to arrive at the business in time for the start of the particular shift. In response, the service provider may find a replacement employee to fill the shift of the first employee. For instance, the service provider may send the communication to a second mobile device associated with a second employee that is determined to be within a threshold distance of the business. Alternatively, if the service provider determines that the business is overstaffed on a particular day, the service provider may offer a selected employee an incentive to depart work early.

Abstract: In some examples, a service provider may associate, with an employer profile of an employer, a plurality of employee profiles of employees that are available to be assigned to one or more shifts of a plurality of shifts. The service provider may determine, based at least in part on shift information and the employee profiles, a shift schedule that indicates employee assignments for individual shifts. In some cases, in response to determining that a first shift is unfilled, the service provider may add to the shift schedule an incentive for one of the employees to select the first shift. Alternatively, in response to determining, based at least in part on the employee profiles, that multiple employees indicated a preference to work a second shift, the service provider may leave the second shift unassigned to enable the employees to bid on being assigned to work the second shift.

Abstract: In some examples, a service provider may determine from a shift schedule that a first employee is scheduled to work a particular shift at a business within a threshold period of time. The service provider may determine, based on a location of a first mobile device of the first employee, that the first employee is unlikely to arrive at the business in time for the start of the particular shift. In response, the service provider may find a replacement employee to fill the shift of the first employee. For instance, the service provider may send the communication to a second mobile device associated with a second employee that is determined to be within a threshold distance of the business. Alternatively, if the service provider determines that the business is overstaffed on a particular day, the service provider may offer a selected employee an incentive to depart work early.

Abstract: A composite polyester and cotton yarn is formed by blending polyester fibers with cotton fibers which have been regenerated from waste cotton material such as trimmings and cuttings from the apparel manufacturing industry. The fibers are cleaned and blended, then carded to align the fibers into strands. Depending on the size and texture of the desired yarn, the fibers are stretched and drawn into slivers prior to spinning to join the fibers together. The polyester fibers are preferably recycled polyethylene terephthalate fibers which are up to three time longer than the regenerated cotton fibers, so that the polyethylene terephthalate fibers overlap and braid onto the regenerated cotton fibers during spinning. The resultant yarn is stronger and more absorbent than yarns made solely out of regenerated cotton fibers. The yarn is suitable for producing woven or knit fabric for the production of colorful and functional clothing and home textiles without the need for further bleaching or dyeing.