Abstract: A two-wheeled, variable configuration bicycle having a frame, a handlebar, a saddle, pedals with pedal cranks and a drive gear rotatably mounted on the frame, a front wheel mounted on the front fork, a rear wheel with a sprocket mounted on a rear fork. The bicycle includes the upright configuration and the recumbent configuration, and in recumbent configuration, the bicycle provides support for the back of the cyclist. The frame (2, 2?) are provided two front mounting locations for attaching the front fork (14), wherein the first front mounting location is provided for attaching the front fork (14) in the upright configuration, and the second front mounting location is provided for attaching the front fork (14) in the recumbent configuration. The frame (2, 2?) are provided two rear mounting locations for attaching the rear fork (17).

Abstract: A two-wheeled, variable configuration bicycle having a frame, a handlebar, a saddle, pedals with pedal cranks and a drive gear rotatably mounted on the frame, a front wheel mounted on the front fork, a rear wheel with a sprocket mounted on a rear fork. The bicycle includes the upright configuration and the recumbent configuration, and in recumbent configuration, the bicycle provides support for the back of the cyclist. The frame (2, 2?) are provided two front mounting locations for attaching the front fork (14), wherein the first front mounting location is provided for attaching the front fork (14) in the upright configuration, and the second front mounting location is provided for attaching the front fork (14) in the recumbent configuration. The frame (2, 2?) are provided two rear mounting locations for attaching the rear fork (17).

Abstract: A system and method for dynamic pricing of short-term stay accommodations, using machine learning algorithms to set pricing based on a broad set of non-homogenous dynamic data that may affect pricing at the time of booking, including pricing of competitors, market forecasts of pricing, internal factors including room quality, customer behavior related to booking, environmental factors, events, and economic factors.

Abstract: The method consists in the use of the average network traffic data and a series of measurements taken within a part of a network, along with the recording of the signals, received from all base stations within reach of a given point. The measurement area is divided into polygonal sections, relating to specific P measurements and the total area of such section represents the weight of a given measurement. Subsequently, based on the most powerful signal criterion, individual P measurements are assigned to specific network cells. Further on, the based on the differences between signal power value calculated, measurement weights and the assumed distances between channels, values of certain formulae are calculated, which then provide the basis for determination of the minimum distances between channels.