Abstract: This disclosure provides a compound of formula (III): or a pharmaceutically acceptable salt, solvate or derivative thereof, wherein the substituents are as defined herein. The disclosure also provides pharmaceutical compositions comprising said compounds and the use of said compounds in the treatment of diseases, e.g. cancer.

Abstract: The present disclosure relates to a novel class of bifunctional molecules that are useful in a targeted or selective degradation of a protein.

Abstract: This disclosure provides a compound of formula (III): or a pharmaceutically acceptable salt, solvate or derivative thereof, wherein the substituents are as defined herein. The disclosure also provides pharmaceutical compositions comprising said compounds and the use of said compounds in the treatment of diseases, e.g. cancer.

Abstract: A spent caustic treatment process addresses the shortcomings with traditional Wet Air Oxidation Systems. The process can treat either refinery or sulphidic spent caustic streams with CODs of up to 50,000 mg/L. The process uses >90% oxygen as an oxidising agent. A horizontal, tubular reactor is operated at pressures between 100 and 170 Bar (ideally 145-165 Bar). The reactor has an operating temperature of between 120° C. and 320° C., ideally 280° C. to 300° C. A closed heat transfer medium circulation loop is utilised for heat recovery from the reactor effluent stream to the spent caustic feed stream. The invention allows for a COD reduction of 7 5 to 99.9%.

Abstract: The present disclosure relates to a novel class of bifunctional molecules that are useful in a targeted or selective degradation of a protein.

Abstract: A spent caustic treatment process addresses the shortcomings with traditional Wet Air Oxidation Systems. The process can treat either refinery or sulphidic spent caustic streams with CODs of up to 50,000 mg/L. The process uses >90% oxygen as an oxidising agent. A horizontal, tubular reactor is operated at pressures between 100 and 170 Bar (ideally 145-165 Bar). The reactor has an operating temperature of between 120° C. and 320° C., ideally 280° C. to 300° C. A closed heat transfer medium circulation loop is utilised for heat recovery from the reactor effluent stream to the spent caustic feed stream. The invention allows for a COD reduction of 75 to 99.9%.

Abstract: A process performed by a plant for oxidation of a waste stream with oxidizable material is described. In a start-up phase, supercritical water is fed to a supercritical water oxidation reactor, heating the process up to supercritical conditions. In a treatment phase, the waste stream is fed to the reactor for supercritical water oxidation treatment, in which sufficient mass of water under supercritical conditions is present in the reactor to retain supercritical conditions with the newly introduced waste stream. Oxygen is used as oxidant and a stoichiometric quantum is added to the reactor. The energy released from the oxidation reaction substitutes the energy provided by the addition of supercritical water up to a point where the reactor achieves near autothermal conditions with supercritical water providing trim heat requirement. The reactor outlet is quench cooled, neutralised and energy is recovered from it. A gas liquid separator ensures that the effluent stream is degassed.

Abstract: Segmenting data and forecasting by a combination of models trained on segmented data is provided. A system compares, with a first model, values of timestamps corresponding to data points to determine a time series dependency between the data points. The system generates, with the first model and based on the time series dependency, a first cluster with first data points and a second cluster with second data points. The system allocates, by a controller, a second model to the first cluster, and a third model to the second cluster. The system trains the second model based on the time series dependency and the first data points. The system trains the third model based on the time series dependency and the second data points. The system generates a fourth model based on a combination of the second trained model and the third trained model.