Nano and Advanced Materials Institute Limited

Sidechef Group Limited

Four K Knitters Ltd

Kang Zhang

Jifan Li

Shilong Zhang

Christopher Cheung Yec

Kevin Yu

Xiyao Zhang

Jian Zhang

Simone Rebaudengo

Nick Pittom

Matt Au

Kwok Keung Paul Ho

Han Wang

Sze Kui Lam

Liang Zhang

Jianjun Song

Sui Lung Cheung

Cheng Chen

Provided is an adaptive strap for brassieres having at least one adaptive material layer () having a first strength modulus x during static loading and a second strength modulus of at least 2× during dynamic loading of at least approximately 3 Hz oscillation. The adaptive layer () is sandwiched between two or more elastic fabric layers (). The adaptive strap is extendable up to 100% of its initial length during low wearer activity but is extendable not more than 50% during vigorous wearer activity, providing support during exercise and comfort during daily use. In one aspect the adaptive material is a reaction product formed by reacting a silanol terminated polydimethylsiloxane, polymer matrix, boric acid and optional fillers. The adaptive material () is cured in situ within the elastic fabric layers ().

Adaptive straps for brassiere products and the method of making the same

An energy dissipating fiber and fabric for protective textile application, which can absorb energy during shocking, stretching and vibration. The disclosed fiber/fabric can include a polymer matrix, a shear-thickening material and a reinforcing filler.

Energy dissipating fiber/fabric and the method of making the same

A gamified application is provided for users to feed animated virtual characters with images of real-world food items. The images fed to the virtual characters are to be uploaded to a data store in a cloud environment, for use in training a custom machine learning model. A server in the cloud environment receives a photo of a food item fed to a virtual character in an augmented reality environment in the gamified application executing on a user device, invokes the custom machine learning model to generate classification information for the photo, sends the classification information to the user device for verification by a user, and stores the verified information to the data store used for periodically training the machine learning model. Over time, the data store would include a large volume of food images with label data verified by a large number of users.

Crowd-sourced data collection and labelling using gaming mechanics for machine learning model training

Provided herein are energy absorbing composites including a thermoplastic resin, a dilatant, a compatibilizer, a reinforcing filler, and optionally an antioxidant and methods of preparation thereof.

Thermoplastic energy absorbing composites and methods of preparation thereof

The present invention provides new impact protection materials and the method of making the same. More specifically, the impact protection materials are polymer-based shear-thickening composite comprising a non-shear-thickening polymer matrix material and one or more shear-thickening materials. Preferably, the non-shear-thickening polymer matrix material is in an amount from approximately 5 weight percent to approximately 90 weight percent with the balance being one or more shear-thickening materials. The first type of the one or more shear-thickening materials is sol-gel based shear-thickening material in which small inorganic particles are connected in a gel network; the second type is polymer-based shear-thickening material in which polymer chains form network. Compared to the existing shear-thickening materials, the present shear-thickening materials have different molecular structure and formulation and possess properties of good impact protection and good stability.

Impact protection material and method of making the same

Growing community of inventors

Hong Kong, China

Kang Zhang