No abstract

The growth of poultry meat production is increasing industrial waste quantities every year. Feathers represent a huge part of the waste, and international directives and restrictions prevent landfilling of such biodegradable materials with high burning values. Furthermore, with their unique properties, poultry waste feathers are already a reliable resource for many byproducts, such as keratin extraction, fibres, hydrogel production, etc., all trying to achieve a high-added value. However, mass reduction of waste feathers into useful applications, such as development of alternative building materials, is also an important aspect. To take advantage of feathers’ thermal insulation capabilities, sound damping, and biodegradability, we worked towards mixing waste feathers with wood residues (wood shavings, dust, and mixed residues) for production of composite fibreboards, comparable to the market’s medium density fibreboards. The emphasis was to evaluate waste poultry feathers as the component of natural insulation composites, along with mixed waste wood residues, to improve their mechanical properties. Various composite fibreboards with different shares of wood and feathers were produced and tested for mechanical, thermal, and acoustic properties, and biodegradability, with comparison to typical particle boards on the market. The addition of waste feather fibres into the fibreboards` structure improved thermal insulation properties, and the biodegradability of fibreboards, but decreased their bending strength. The sound transition acoustic loss results of the presented combination fibreboards with added feathers improved at mid and high frequencies. Finally, production costs are estimated based on small scale laboratory experiments of feather processing (cleaning and drying), with the assumption of cost reduction in cases of large industrial application.

Constructing micro-sized machines always involves the problem of how to bring the energy (electric, magnetic, light, electro wetting, vibrational, etc.) source to the device to produce mechanical movements. The paper presents a rotational micro-sized motor (the diameter of the rotor is 350 µm) driven by low frequency (200–700 Hz) circular vibrations, made by two piezoelectric actuators, through the medium of a water droplet with diameter of 1 mm (volume 3.6 µL). The theoretical model presents how to produce the circular streaming (rotation) of the liquid around an infinitely long pillar with micro-sized diameter. The practical application has been focused to make a time-stable circular stream of the medium around the finite long vibrated pillar with diameter of 80 µm in the presence of disturbances produced by the vibrated plate where the pillar is placed. Only the time-stable circular stream in the water droplet around the pillar produces enough energy to rotate the micro-sized rotor. The rotational speed of the rotor is controlled in both directions from −20 rad/s to +26 rad/s. 3D printed mechanical amplifiers of vibrations, driven by piezoelectric actuators, amplify the amplitude of the piezoelectric actuator up to 20 µm in the frequency region of 200 to 700 Hz.

There have been recent developments in grippers that are based on capillary force and condensed water droplets. These are used for manipulating micro-sized objects. Recently, one-finger grippers have been produced that are able to reliably grip using the capillary force. To release objects, either the van der Waals, gravitational or inertial-forces method is used. This article presents methods for reliably gripping and releasing micro-objects using the capillary force. The moisture from the surrounding air is condensed into a thin layer of water on the contact surfaces of the objects. From the thin layer of water, a water meniscus between the micro-sized object, the gripper and the releasing surface is created. Consequently, the water meniscus between the object and the releasing surface produces a high enough capillary force to release the micro-sized object from the tip of the one-finger gripper. In this case, either polystyrene, glass beads with diameters between 5–60 µm, or irregularly shaped dust particles of similar sizes were used. 3D structures made up of micro-sized objects could be constructed using this method. This method is reliable for releasing during assembly and also for gripping, when the objects are removed from the top of the 3D structure—the so-called “disassembling gripping” process. The accuracy of the release was lower than 0.5 µm.

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject’s quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm’s robustness was also verified with the successful detection of freezing gait episodes in a Parkinson’s disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.